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Vertica OverviewUNIXBusinessApplication

Vertica is the #3 ranked solution in our list of top Data Warehouse tools. It is most often compared to Snowflake: Vertica vs Snowflake

What is Vertica?

Micro Focus Vertica is the most advanced SQL database analytics portfolio built from the very first line of code to address the most demanding Big Data analytics initiatives. Micro Focus Vertica delivers speed without compromise, scale without limits, and the broadest range of consumption models. Choose Vertica on premise, on demand, in the cloud, or on Hadoop. With support for all leading BI and visualization tools, open source technologies like Hadoop and R, and built-in analytical functions, Vertica helps you derive more value from your Enterprise Data Warehouse and data lakes and get to market faster with your analytics initiatives.

To learn more about Micro Focus Vertica Advanced Analytics, visit our website.

Vertica is also known as Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand.

Vertica Buyer's Guide

Download the Vertica Buyer's Guide including reviews and more. Updated: September 2021

Vertica Customers

Cerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics.

Vertica Video

Archived Vertica Reviews (more than two years old)

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BS
Sr. Business Intelligence Analyst / Developer at DXC
Real User
Has improved the majority of our ETL operations, but performance degrades seriously for large datasets

Pros and Cons

  • "Eighty percent of the ETL operations have improved since implementing this solution."
  • "Fact-to-fact joins on multi-billion record tables perform poorly."

What is our primary use case?

We use this solution as our data warehouse. It handles our analytics and we have power users connected.

How has it helped my organization?

Eighty percent of the ETL operations have improved since implementing this solution. Complex queries are challenging to improve.

What is most valuable?

This most valuable feature is the database designer, which helps significantly improve our storage footprint.

What needs improvement?

There is serious performance degradation for large datasets. Fact-to-fact joins on multi-billion record tables perform poorly. Star schema joins also perform poorly if the fact tables reach more than one billion records and the dimension tables reach more than one million records.

For how long have I used the solution?

Two years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
ITCS user
Hewlett Packard Enterprise Solution Architect at a tech services company with 11-50 employees
Consultant
It leverages machine learning and predictive analytic features to help preprocess data

Pros and Cons

  • "It maximize cloud economics for mission-critical big data analytical initiatives."
  • "It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands."
  • "It needs integration with multiple clouds."

What is our primary use case?

The primary use case is as an analytics database on EC2 instances.

How has it helped my organization?

  • We gain insights into data in real-time with blazing, fast SQL analytics across exabytes of data.
  • It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands.
  • It leverages machine learning and predictive analytic features to help preprocess data.

What is most valuable?

It maximize cloud economics for mission-critical big data analytical initiatives.

What needs improvement?

It needs integration with multiple clouds.

For how long have I used the solution?

One to three years.

What do I think about the stability of the solution?

I have implemented it on Amazon EC2 instances with medium IT workloads.

What do I think about the scalability of the solution?

It has an elastic scalability solution.

How was the initial setup?

It is easy to integrate with EC2 instances.

What's my experience with pricing, setup cost, and licensing?

It is fast to purchase through the AWS Marketplace.

The pricing and licensing depend on the size of your environment and the zone where you want to implement.

What other advice do I have?

It is a complete solution and a also good solution for EC2 instances.

I have not tried to integrate it with other products.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Learn what your peers think about Vertica. Get advice and tips from experienced pros sharing their opinions. Updated: September 2021.
541,708 professionals have used our research since 2012.
it_user806967
Architect at OpenSCG
User
Any novice user can tune vertical queries with minimal training

What is our primary use case?

We use the product for compressed data store, fast reporting, and self healing analytical data workloads. It also helps with big data ingestions, processing, and reporting.

How has it helped my organization?

Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics.

What is most valuable?

The tool for performance tuning and recommendations Any novice user can tune vertical queries with minimal training (or no training at all).

What needs improvement?

It should provide a GUI interface for data management and tuning. Monitoring tools need to be lightweight. They should not take up heavy resources of the main server.

For how long have I used the solution?

What is our primary use case?

We use the product for compressed data store, fast reporting, and self healing analytical data workloads. It also helps with big data ingestions, processing, and reporting.

How has it helped my organization?

Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics.

What is most valuable?

  • The tool for performance tuning and recommendations
  • Any novice user can tune vertical queries with minimal training (or no training at all).

What needs improvement?

  • It should provide a GUI interface for data management and tuning.
  • Monitoring tools need to be lightweight. They should not take up heavy resources of the main server.

For how long have I used the solution?

One to three years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
DW
Bi Group Manager at Intuit Inc.
Vendor
Its projections and encoding are excellent tools for tuning large volumes

Pros and Cons

  • "Vertica gives knowledgeable users and DBAs excellent tools for tuning."
  • "Its projections and encoding are excellent tools for tuning large volumes."
  • "If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer."
  • "It would be great if this were a managed service in AWS."

What is our primary use case?

We push both raw and modeled data into a Vertica cluster. It is used mainly for internal analysis and Tableau reports by data scientists and analysts.

How has it helped my organization?

It is tremendously scalability, with excellent performance. Vertica gives knowledgeable users and DBAs excellent tools for tuning.

What is most valuable?

  • Its projections and encoding are excellent tools for tuning large volumes.
  • The product is simple and elegant.
  • It has excellent written documentation. I am able to answer any question by querying on Google.

What needs improvement?

You need to know what you are doing to get the most out of Vertica. If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer.

It would be great if this were a managed service in AWS.

For how long have I used the solution?

Three to five years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
ITCS user
Cloud Architect, Oracle ACE, Oracle DBA at Pythian
MSP
Its scalability has enabled Pythian's clients to manage data with agility and scale accordingly.

Pros and Cons

  • "Its analytics has enabled Pythian's clients to get the business insights as quick as they wanted. Its lower maintenance has also improved the ROI."
  • "One feature, which has really benefited us, is the scalability offered by Vertica as it has enabled Pythian's clients to manage data with agility."

What is our primary use case?

It has performed well for the analytical and data warehousing performance. It has enabled scalability and has added value to the business.

How has it helped my organization?

Its analytics has enabled Pythian's clients to get the business insights as quick as they wanted. Its lower maintenance has also improved the ROI.

What is most valuable?

HPE Vertica is a unique solution as it handles a huge magnitude of data with matchless speed and simplicity. One feature, which has really benefited us, is the scalability offered by Vertica as it has enabled Pythian's clients to manage data with agility.

What needs improvement?

The documentation could be improved with more examples of commands and step-by-step scenarios.

For how long have I used the solution?

Three to five years.

What do I think about the stability of the solution?

There were no stability issues.

What do I think about the scalability of the solution?

There were no scalability issues.

How are customer service and technical support?

The technical support is good, although it could be improved in terms of the response time and skill-set.

Which solution did I use previously and why did I switch?

NA

How was the initial setup?

The setup was pretty straightforward as it doesn't take much; if you plan your infrastructure right, then it is a breeze.

What about the implementation team?

NO

What's my experience with pricing, setup cost, and licensing?

Read the fine print carefully.

What other advice do I have?

First, analyze your business requirements and if the analytics, scalability, and lower maintenance are your requirements then go for HPE Vertica.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
ITCS user
Co-Founder at a tech services company
Consultant
Its speed differentiates it from other columnars, and works on commodity hardware

Pros and Cons

  • "The feature of the product that is most important is the speed. I needed a columnar database, and its speed is what it's built to do, and so that's what really does differentiate Vertica from its competitors."
  • "I don't need any special hardware. I can use commodity hardware, which is nice to have in a commercial solution."

    What is our primary use case?

    When I have a business need for a few pieces of information, and I need to process it quickly, that's when I use Vertica.

    How has it helped my organization?

    We got something like a six-times improvement using Vertica.

    What is most valuable?

    The feature of the product that is most important is the speed. I needed a columnar database, and its speed is what it's built to do, and so that's what really does differentiate Vertica from its competitors.

     I think what also draws me to it is that I don't need any special hardware. So I can use commodity hardware, which is nice to have in a commercial solution.

    What needs improvement?

    I think it's starting to get a little expensive. Open source products are starting to get more robust, so I think that's something that they need to start looking at in terms of licensing.

    For how long have I used the solution?

    More than five years.

    What do I think about the stability of the solution?

    Absolutely stable. It's supported. The stability is one thing, the support is the other thing.

    What do I think about the scalability of the solution?

    No scalability issues. Like I said, in its competitive set it is just faster, better, depending on how you use it, because it is columnar.

    How are customer service and technical support?

    We don't need them that much, but when we do need them, we use the virtual tech support, and that's fine. It works, and it's responsive. Within 24 hours, we get resolution.

    We didn't pay for a higher tier of service, but we generally just have questions for support.

    Which solution did I use previously and why did I switch?

    We've used Greenplum, Teradata, and then Vertica. We used the big data open source solutions as well that are getting better. So those are the four that I can think of off the top of my head. Greenplum and Teradata are just getting too expensive. 

    Particularly compared against its open source set, I think that's really the one key piece where Vertica might have a little bit more ladder room. It was always the leader in terms of pricing against Greenplum and Teradata, so that's why Vertica turned up again for us, but now that the open source solutions are trying to compete a little bit better in terms of stability, that's where we sometimes consider change.

    Which other solutions did I evaluate?

    I evaluated Teradata, and another, but I didn't like either of them, not for what we needed.

    What other advice do I have?

    The pros are, if you have columnar processing, then this is in your top three solutions. I think the con is the software pricing, and licensing needs to start getting more competitive with the open source solutions, or they need to market their stability a lot more.

    Test out the solution. Most people who test it buy it. So that's the biggest draw that it has, you can test in a day.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user703536
    Technical Leader / Business Intelligence Consultant with 11-50 employees
    Consultant
    Columnar database supports our advanced analytics and ETL process

    Pros and Cons

    • "Vertica is a columnar database, this support our developments in analytics, advanced analytics, and ETL process with large sets of data."
    • "I believe the installation process could be streamlined."

    How has it helped my organization?

    Before we used Vertica we used another columnar database which turned out to be very unstable and its performance was inconsistent. Vertica turned that around, to the point that it is now our go-to database. We became Vertica partners.

    What is most valuable?

    Vertica is a columnar database, this support our developments in analytics, advanced analytics, and our ETL process with large sets of data.

    What needs improvement?

    I believe the installation process could be streamlined.

    For how long have I used the solution?

    One to three years.

    What do I think about the stability of the solution?

    No stability issues.

    What do I think about the scalability of the solution?

    No scalability issues.

    How are customer service and technical support?

    They are very professional and responsive.

    Which solution did I use previously and why did I switch?

    See "Improvements to organization," above.

    How was the initial setup?

    There are some considerations to be evaluated before you start the installation, but the installer does the respective checks so things will function properly. And there are a lot of options.

    What's my experience with pricing, setup cost, and licensing?

    The first TB is free and you can use all the Vertica features. After 1TB you have to pay for licensing. The product is worth it, but be aware of this condition, and plan. The compression ratio is explained in the documentation.

    Which other solutions did I evaluate?

    Infobright and MonetDB.

    What other advice do I have?

    The technical requirements for the product are really important. The design tool for vertica is the core of the database for performance. Never forget to use it to create projections to optimize the storage compression and response times. Better compression means the 1TB mark takes longer to be reached.

    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner.
    HN
    Infrastructre Manager - Senior Maintenance Manager with 10,001+ employees
    Real User
    Lack of Stored Procedures, packages, triggers make things difficult for developers

    Pros and Cons

    • "Partition and join back to node are easy and simple for DBAs."
    • "DBAs don’t need to add a partition every month/quarter like with other DBs."
    • "There are a lot of limitations within this product and it makes things extremely hard for developers. It lacks Stored Procedure, packages, and triggers like other RDBMs."
    • "Very bad support, I would rate it two out of 10."

    What is most valuable?

    Partition and join back to node are easy and simple for DBAs.

    DBAs don’t need to add a partition every month/quarter like with other DBs.

    What needs improvement?

    There are a lot of limitations within this product and it makes things extremely hard for developers. It lacks Stored Procedure, packages, and triggers like other RDBMs.

    For how long have I used the solution?

    One to three years.

    What do I think about the stability of the solution?

    Yes, we have encountered issues with Projections and performance.

    How are customer service and technical support?

    Very bad support, I would rate it two out of 10.

    Which solution did I use previously and why did I switch?

    We use DB2, Oracle , MySQL, MSSQL. We switched to Vertica to explore it for  future projects.

    How was the initial setup?

    Easy setup. Much easier than setting up Oracle RAC.

    What's my experience with pricing, setup cost, and licensing?

    Licensing is based on size of the database.

    Which other solutions did I evaluate?

    They did a good PoC and we were impressed with Vertica. However, when we implemented, it was nightmare with bad support.

    What other advice do I have?

    My advice regarding this product is a definite "no", due to bad support.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    JS
    Sr. SW Engineer - Databases with 201-500 employees
    Real User
    Easy to implement, by tuning the model (projection design) you get great performance

    Pros and Cons

    • "Vertica enabled us to close large deals. Customers with large data sets had to be migrated from PostgreSQL to Vertica due to performance."
    • "Performance of management of metadata layer (database catalog) needs improvement. We still have to have smaller customers on PostgreSQL; Vertica cannot manage thousands of schemata."
    • "Suboptimal projection design causes queries to not scale linearly."
    • "Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes."

    How has it helped my organization?

    It enabled delivery of a new Agile Data Warehousing Service.

    It enabled us to close large deals. Customers with large data sets had to be migrated from PostgreSQL to Vertica due to performance.

    What is most valuable?

    • Clustered database
    • Horizontal scaling
    • Disaster recovery
    • Columnar Storage
    • Compression (you read only columns you need)
    • Immutable storage
    • Fast ingesting

    What needs improvement?

    Performance of management of metadata layer (database catalog) needs improvement. We still have to have smaller customers on PostgreSQL; Vertica cannot manage thousands of schemata.

    Query performance: Improve either Database Designer (automation of projection design) or performance of queries using suboptimal projection design.

    Scaling of execution independently on storage: Upcoming Eon Mode (now Beta in Amazon) will hopefully solves this.

    For how long have I used the solution?

    One to three years.

    What do I think about the stability of the solution?

    Encountered stability issues three times during last three years.

    What do I think about the scalability of the solution?

    Suboptimal projection design causes queries to not scale linearly.

    The metadata layer does not scale linearly.

    Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes.

    How are customer service and technical support?

    I have experience with legacy vendors of enterprise RDBMS solutions, and I rate Vertica support to be much better.

    Which solution did I use previously and why did I switch?

    In my current company I was not responsible for the switch. As far as I know, they switched from PostgreSQL, especially because of performance of analytical queries processing large data.

    How was the initial setup?

    Just getting Vertica running is straightforward. However, with an increasing number of customers, we had to develop our own tooling. For example:

    • Automated deployment
    • Monitoring, alerting
    • Backup/restore.

    What's my experience with pricing, setup cost, and licensing?

    Start with license per 1TB. Starting from hundreds of TB there is unlimited licensing to be considered.

    Move historical data to HDFS/S3 which are significantly cheaper or even free.

    Vertica is delivering more and more features to support load/unload for external storages.

    Which other solutions did I evaluate?

    2012 - Detailed evaluation including benchmarks of: Greenplum, Vectorwise.

    2017 - Evaluation of features and initial communication with vendors, if needed, for: Greenplum, EXASOL, Amazon Redshift, Spark, SAP HANA, IBM dashDB, Snowflake, Azure SQL.

    What other advice do I have?

    It is easy to implement this solution for one customer. By tuning the model (projection design) you get incredible performance. You won’t face issues with metadata (catalog) layer up to tens of thousands of tables.

    It can be a challenge to operate clusters for many customers with varied data pipelines. Consider using Database Designer.

    Don't hesitate to push Vertica (through support/product management) to improve it.

    Consider implementing your own tools to automate performance tuning tasks.

    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner.
    it_user431877
    Consultant at a tech services company with 10,001+ employees
    Consultant
    All joint operations were enhanced by creating identically segmented projections

    What is most valuable?

    I found the columnar storage, which increases performance of sequential record access, to be the most valuable feature.  I also like the projection feature, which increases query performance.

    How has it helped my organization?

    The workload on our ETL tools were reduced.  All joint operations were enhanced by creating identically segmented projections.

    What needs improvement?

    Limitations in group by projections is where I would like to see an improvement. 

    What was my experience with deployment of the solution?

    We have not had any issues with deployment.

    What do I think about the stability of the solution?

    We have not had any issues with stability.

    What do I think about the scalability of the solution?

    We have been able to scale it for our…

    What is most valuable?

    • I found the columnar storage, which increases performance of sequential record access, to be the most valuable feature. 
    • I also like the projection feature, which increases query performance.

    How has it helped my organization?

    • The workload on our ETL tools were reduced. 
    • All joint operations were enhanced by creating identically segmented projections.

    What needs improvement?

    Limitations in group by projections is where I would like to see an improvement. 

    What was my experience with deployment of the solution?

    We have not had any issues with deployment.

    What do I think about the stability of the solution?

    We have not had any issues with stability.

    What do I think about the scalability of the solution?

    We have been able to scale it for our needs.

    What other advice do I have?

    It is a good database that can be used for ad hoc queries as well as analytical queries.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user428343
    Managing Partner at Thorium Data Science
    Vendor
    The architecture means it can process/ingest data in parallel to reporting and analyzing because of in-memory Write-Optimized Storage alongside the analytics optimized Read-Optimized Storage.

    Pros and Cons

    • "The Vertica architecture means it can process/ingest data in parallel to reporting and analyzing because of its in-memory Write-Optimized Storage sitting alongside the analytics optimized Read-Optimized Storage."
    • "I would personally like to see extended developer tooling suited to Vertica – think published PowerDesigner SQL dialect support."

    What is most valuable?

    Vertica’s analytic capabilities are its key strength. It can aggregate and analyze data at massive scale and neatly bring the calculation logic to the data with external procedures in C, Java and R.

    The Vertica architecture means it can process/ingest data in parallel to reporting and analyzing because of its in-memory Write-Optimized Storage sitting alongside the analytics optimized Read-Optimized Storage.

    Which brings us to projections and the DB designer which intelligently structures how data is actually stored on disk to improve the queries you actually run against it. So tables are a logical construct which are operated on as per other DBMS systems, but there’s a whole next level of intelligence in optimization for querying that puts Vertica in another league.

    How has it helped my organization?

    Our consultancy has introduced Vertica to a number of clients, from small scale ones who benefit from the free tier and per TB pricing model to have a powerful analytics cluster fairly cheaply to large investment banks who have been able to handle data at a scale that wouldn’t otherwise be possible.

    What needs improvement?

    We’ve built a data ingestion tool to sit alongside Vertica for easy data loading, and I would personally like to see extended developer tooling suited to Vertica – think published PowerDesigner SQL dialect support, IDE with IntelliSense, and stored procedures which we’ve also had to build a work-around module for.

    For how long have I used the solution?

    Personally, I've used it for three to four years (since v6), but a few others in Thorium Data Science have used it for longer.

    What was my experience with deployment of the solution?

    We've had no issues. You do need to invest a little time to understand how to set things up and optimize for your workload, but it’s all well documented and there are consultancy firms who will happily help with that.

    What do I think about the stability of the solution?

    We've had no issues with the stability.

    What do I think about the scalability of the solution?

    We've had no issues scaling it.

    How are customer service and technical support?

    It's very good. HP have some technically smart guys and are willing to give access to them when you start using Vertica. We’ve had some great support from their engineering team with things like telling us about upcoming features (snapshotting, in this case), which were spot on for a need a client of ours had. We were looking into engineering a solution ourselves and HP happened to have just what we needed coming down the pipeline in the next version.

    Which solution did I use previously and why did I switch?

    We previously used Exadata, which is typically very expensive by comparison. This is because Oracle throw top end hardware at the problem as opposed to
    HP Vertica’s commodity hardware and smart software approach.

    How was the initial setup?

    It takes some time to come to grips with the various considerations. I’d suggest bringing in a consultant if you don’t have the time or inclination to do it yourself as it takes going through and install and configuration one or two times to really understand the implications of the different options.

    What other advice do I have?

    The implementation itself is excellent with fantastic features, speed and scalability. They lose a point only for the development experience which relies on third party tooling like squirrel, and not having SQL based stored procedures.

    Go for it! Try the pre-installed VM which HP offers to have a play with it and get a feel for it. It can certainly scale better than any other RDBMS and pushes the envelope of SQL analysis so you can query/analyze/report “BIG-DATA” without having to resort to the complications associated with Hadoop & unstructured data analysis. If your data is structured and large Vertica is what you need.

    Disclosure: My company has a business relationship with this vendor other than being a customer: We are an HP Partner offering consultancy on Vertica (as well as Oracle, SQL Server and other DBs).
    it_user692295
    Staff Dev Lead - Analytics Data Storage at a tech services company with 1,001-5,000 employees
    Consultant
    Our typical run time for a query is now measured in seconds not hours.

    Pros and Cons

    • "The extensibility and efficiency provided by their C++ SDK."
    • "Whatever's out, the core is not always as great as the engine, especially their first version."

    What is most valuable?

    Two of them:

    • The core feature, meaning their highly efficient columnar file format and execution engine along with a great coverage of ANSI SQL, provides our analysts with a highly expressive and performing platform.
    • The extensibility and efficiency provided by their C++ SDK.

    How has it helped my organization?

    Before Vertica, we used a combination of sharded RDBMSs and Hive: the typical runtime for a query was in the hours. It's now in the seconds, with way
    more data than then (we're talking hundreds of terabytes).

    What needs improvement?

    Whatever's out, the core is not always as great as the engine, especially their first version. That's true, for example, for the Kafka or Hadoop integration.
    But they're getting better release after release.

    For how long have I used the solution?

    Four years.

    What do I think about the stability of the solution?

    Vertica's code, being designed to use the hardware at its maximum, is very sensitive to low level changes such as kernel bumps or GLibC upgrades. It's also important to do tests on the storage layer (RAID controller + disks).

    What do I think about the scalability of the solution?

    The default layout (all nodes running spread) introduces latencies in query planning when you reach about 60 nodes, in our experience. Switching to a large cluster (one control node per rack) would be advised, way before reaching the 128 nodes hard limit.

    How are customer service and technical support?

    It's really great. One of the best I had to deal with. They also assisted us during the development phase of the custom components we've designed.

    Which solution did I use previously and why did I switch?

    Not really in the same area (MPP databases). However, we ran benchmarks back then against a bunch of competitors and Vertica was one of the fastest, while
    being relatively cheap and able to accommodate our hardware.

    How was the initial setup?

    The setup per se was pretty straightforward. However, it took us some time to design the most efficient loading pattern from Hadoop.

    What's my experience with pricing, setup cost, and licensing?

    Nothing to advise really; try it out first, it's free up to three nodes and 1TB, and then get in contact with their sales guys.

    Which other solutions did I evaluate?

    We did evaluate mostly SAP HANA and SQL Server PDW back in 2013, along with a bunch of OSS solutions.

    What other advice do I have?

    If you plan to use Vertica for different workloads (in term of IO patterns, query frequency, dataset structure) plan to split your clusters: the mother of all cluster patterns becomes quite difficult to manage at some point. We today have around 20 clusters for different usages.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user624996
    Architect at a comms service provider with 1,001-5,000 employees
    Vendor
    The engine analyses offline usage and sends customers alerts when they exceed certain limits.

    What is most valuable?

    Quick retrieval of data Fast upload of data

    How has it helped my organization?

    Vertica was a key component in a billing systems analytic engine. Among other functionalities, the engine is constantly analysing offline usage and sending customers alerts when they exceed certain limits.

    What needs improvement?

    It would be hugely beneficial if HP Vertica offered stored procedures.

    For how long have I used the solution?

    I have used it for five years.

    What was my experience with deployment of the solution?

    As a green field solution, the features of the application were not clear and the system integrator was not up to the mark.

    What do I think about the stability of the solution?

    We did not encounter hardly any stability issues.

    What do I think

    What is most valuable?

    • Quick retrieval of data
    • Fast upload of data

    How has it helped my organization?

    Vertica was a key component in a billing systems analytic engine. Among other functionalities, the engine is constantly analysing offline usage and sending customers alerts when they exceed certain limits.

    What needs improvement?

    It would be hugely beneficial if HP Vertica offered stored procedures.

    For how long have I used the solution?

    I have used it for five years.

    What was my experience with deployment of the solution?

    As a green field solution, the features of the application were not clear and the system integrator was not up to the mark.

    What do I think about the stability of the solution?

    We did not encounter hardly any stability issues.

    What do I think about the scalability of the solution?

    We did not encounter hardly any scalability issues.

    How are customer service and technical support?

    Customer Service:

    It was a green field solution, and getting quick customer service was a challenge.

    Technical Support:

    Technical support is scarce in Australia.

    Which solution did I use previously and why did I switch?

    We did not previously use a different solution.

    How was the initial setup?

    Initial setup is straightforward.

    What about the implementation team?

    We implemented it through a vendor. The team was good, but they were not experts.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user550089
    Vertica Support Engineer at a media company with 10,001+ employees
    Vendor
    Its column-oriented architecture makes it a database specialized for data warehouses.

    What is most valuable?

    Vertica is an excellent data warehouse platform. Its column-oriented architecture makes it a powerful database specialized for data warehouses. Data should be designed around a star schema.

    Data is accessed via SQL, which most developers are already familiar with.

    Vertica is "catching on" in the software market, so its user knowledge base is gradually increasing.

    The price seems reasonable, the product is reliable, and it uses SQL, so developers don't need to learn a new language.

    How has it helped my organization?

    It provides very fast results for analysts running reports. These reports are crucial to help our clients strategize their targeted marketing.

    What needs improvement?

    Vertica is relatively new and needs some polish and refinement, but its core functionality is excellent.

    Documentation overall is fair to good; but lacks continuity or cohesiveness in places.

    Although its knowledge base is increasing, it is still relatively small, making some issues difficult to diagnose without consulting Vertica Tech Support.

    Vertica does not have native stored procedures or a native scripting language. Instead, external functions (which can be called from within Vertica) using Java, C++, Linux shell scripting, etc., are supported. This is an unpleasant surprise for many developers, but I feel this has not been a big hindrance in my experience. Complex business logic probably does not belong in a high-performance data warehouse platform. Rather, this should be taken care of during ETL.

    For how long have I used the solution?

    I have 3+ years of experience with Vertica.

    What was my experience with deployment of the solution?

    Deployment had only a few minor issues that one finds with most software.

    What do I think about the stability of the solution?

    It has been very stable.

    How are customer service and technical support?

    I would give technical support 8 out of 10. They have been responsive, professional and knowledgeable.

    Which solution did I use previously and why did I switch?

    • I have used traditional, row-oriented relational databases like SQL Server, Oracle and PostgreSQL for data warehousing. They are optimized for handling transactions, not data warehousing. Vertica is optimized for data warehousing and that was very clearly demonstrated in its ability to scan large amounts of data at high speed. It is also very fast at loading data.
    • Vertica uses a distributed, shared-nothing architecture which allows for nodes to be added (or removed) according to need. This is a very scalable architecture which is very difficult to achieve with traditional row-oriented databases.
    • Compared to Hadoop, Hive, and Spark, Vertica is much more adept at handling concurrent users.

    How was the initial setup?

    Installation is recommended for someone familiar with Linux (the only OS available for Vertica). For developers with a Linux background, the issues are very manageable. Documentation is good for the installation, so follow it carefully, step-by-step.

    What about the implementation team?

    Implementation was in-house. No significant issues were encountered.

    What was our ROI?

    ROI is good because Vertica, while not cheap, is a better performer than traditional databases.

    What other advice do I have?

    • Understand that its strengths depend on a good data warehouse design using a star schema. It was never intended for high volumes of small, randomly distributed inserts, updates and deletes that are typically found in transactional databases.
    • It uses column-oriented architecture. It is important to study aspects of this architecture and to implement them and modify them as the database grows in size and more users access the system. This is especially true for projections, run-length encoding, sorting and column ordering. It is important to understand these aspects in order to truly maximize Vertica's performance.
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user158742
    Director of Software Development at a tech company with 501-1,000 employees
    Vendor
    It is scalable and worth the expense if you need the production capability that it can support.

    What is most valuable?

    It has a very good design with high query performance. It provides the scale out capability by adding additional servers instead of scaling up the servers.

    How has it helped my organization?

    It has provided much better performance than SQL Server for big data analytics.

    What needs improvement?

    I would like to see integration with the latest Hadoop ecosystem.

    For how long have I used the solution?

    We have used this solution for three years.

    What do I think about the stability of the solution?

    It is usually very stable, but we occasionally see some nodes going down.

    What do I think about the scalability of the solution?

    There have not been any scalability issues. We are able to support trillions of data elements by adding more servers.

    How are customer service and technical support?

    The technical support is pretty good. I would give it a rating of 9/10.

    Which solution did I use previously and why did I switch?

    We used to use MS SQL Server. It is good for data transactions, but it is not good for big data analytics.

    What's my experience with pricing, setup cost, and licensing?

    It is pretty expensive, but it is worth it if you need the production capability that it can support.

    Which other solutions did I evaluate?

    We evaluated SQL Server and Teradata.

    What other advice do I have?

    It is worth a try if you are looking to provide a high-performance, big data analytics database.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user567630
    Senior Vice President Data at Adform
    Consultant
    Ad-hoc data analysis improved the SLAs for our end clients.

    What is most valuable?

    The most valuable feature in the solution is ad hoc data analysis. It improved the SLAs for our end clients.

    What needs improvement?

    There are some predictive analytics features that we might be using which we thought were part of IDOL, but it seems some are also already part of Vertica.

    What do I think about the stability of the solution?

    The stability is super good, especially when you scale out.

    What do I think about the scalability of the solution?

    Before using Vertica, we used to have problems scaling out because we increase our customer base significantly each year. We have more than 20.000 clients now. Since we implemented the Vertica solution, it is much less effort to maintain scalability.

    How are customer service and technical support?

    I haven’t used technical support, but the IT colleagues definitely have. I think they are rather happy with it. I haven't heard any complaints. It could be quicker sometimes, but that's always the case with big processes.

    Which solution did I use previously and why did I switch?

    Previously, we were basically using an old school setup based on a relational database. I’m not sure which database management system it was.

    The performance of the previous solution was no longer adequate to support the growth we were seeing in our business. Response times were up to 10-15 seconds on different queries. We needed to get that down to under a second.

    Now we’ve moved to a real big data analytics solution.

    How was the initial setup?

    I wasn’t involved with that, but I think that those who did it were happy with the support.

    What other advice do I have?

    When we chose a solution, we were looking at scalability, maintenance, and ease of use. With Vertica, we can access big data using regular SQL queries.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user540294
    Member of Technical Staff at a tech company with 1,001-5,000 employees
    Vendor
    In a PoC, query performance outperformed other solutions.

    What is most valuable?

    We are evaluating storage and database solutions for an OLAP application with following requirements:

    • Extract, transform and load high velocity and volume of a numerical data stream on a distributed system.
    • Interactive (less than 20 sec latency) query performance for critical group-bys.

    Vertica is superior to other solutions in query performance.

    How has it helped my organization?

    We have not yet integrated the solution.

    What needs improvement?

    Vertica’s resource demands for RAM and I/O during load and storage were challenging for our platform. They recommend reserving 40% of storage for Vertica’s internal usage. Lower I/O usage during load is also highly desirable.

    For how long have I used the solution?

    The solution is not integrated into our product. We engaged in a PoC for 2-3 months in 2015 and put the evaluation on hold due to other project priorities.

    What do I think about the stability of the solution?

    We did not encounter any issues with stability.

    What do I think about the scalability of the solution?

    We did not encounter any issues with scalability.

    How is customer service and technical support?

    The level of technical support by the sales engineers during our PoC was excellent.

    How was the initial setup?

    Well-organized, online documentation made the initial setup fairly straightforward.

    What about the implementation team?

    Our in-house team worked on the PoC.

    Which other solutions did I evaluate?

    We evaluated a number of open-source and proprietary databases, as well as an in-house solution. Our PoC has been put on hold and we have not made final decision on a solution.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user539511
    Database Administrator (DBA) at a computer software company with 501-1,000 employees
    Vendor
    I liked the auto-distribution to all nodes for fault tolerance and query performance.

    What is most valuable?

    The auto-distribution to all nodes for fault tolerance and query performance was pretty amazing.

    How has it helped my organization?

    Our data warehouse at the time was a multi-terabyte PostgreSQL cluster. It worked really well, but we wanted to increase the size to many TB's and our due to our query and loading patterns we found greater performance from Vertica's multi-node warehouse.

    What needs improvement?

    In the versions I worked with, if a majority of the nodes were being loaded under heavy, sustained rates the nodes would see some dramatic decreases in performance due to the data shuffling that needed to occur between all the nodes. To work around that we ended up doing most of the loading in one or two nodes and that helped significantly.

    The synchronizations problems occurred when loading about 10 billion events, at a rate of about 100k tuples/second/node across 5 nodes. One of the suggestions from Vertica engineering was to increase the number of nodes to offset how much data was being sync'd per node.

    For how long have I used the solution?

    Extensive use of Vertica 5 as a production datawarehouse, and a POC for a client.

    What was my experience with deployment of the solution?

    In earlier versions Vertica, it could sometimes be a pain to install on multiple nodes. In the most recent versions most of that pain has been fixed. Stability in earlier versions was compromised at times when the majority of the nodes were under heavy write loads.

    How are customer service and technical support?

    The service and support from Vertica was excellent. Every tech and sales rep I dealt with was very responsive, pleasant, and helped me solve any engineering issues we ran into in very short order.

    Which solution did I use previously and why did I switch?

    I have used Greenplum and Postgres extensively. The latter is an excellent general-purpose database and is entirely suitable for most data needs, however Vertica works really well in cases where you are storing and querying a lot of data that can be compressed and stored in columnar format, and you need your data auto-balanced across many nodes.

    How was the initial setup?

    The installation procedure was reasonably straightforward, but earlier versions of Vertica were a bit more tricky due to libraries and dependencies. The docs were unclear in a few places during the installation, particularly with OS' that were not fully compatible with the required libraries. I expect those issues have been resolved in the newest version (8 at this time).

    What about the implementation team?

    Implementation was done in-house, with excellent support from the Vertica engineers.

    What other advice do I have?

    My advice is to clearly define your expectations, and benchmark performance in real-world-like environments. If you expect to be executing 100 queries per second and loading 10 million tuples per minute, then test that, and test several times that so you collect measurements about where the system is liable to break down.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user531828
    Software and Data Architect at a computer software company with 1,001-5,000 employees
    Vendor
    The concurrency got better in this version and we are able to run more queries and load concurrently.

    Valuable Features

    The compute and processing engine returns the queries fast and let us use our analysis resources in a better utilization.

    The concurrency got better in this version and we are able to run more queries and load concurrently.

    Improvements to My Organization

    We built an internal dashboard using the MicroStrategyto increase visibility to our management and our employees. Also, we built tool to expose the data to our selected partners and users to create better engagement with our platform.

    Room for Improvement

    • Loading times for “real time” sources - for example, loading from Spark creates a load on the DB at high scale
    • Connectors to other sources such as Kafka or AWS Kinesis
    • Better monitoring tools
    • Better integration with cloud providers - we were missing some documentation regarding running Vertica on AWS

    Use of Solution

    We've been using Vertica for a year.

    Stability Issues

    In case of one HD failure in the cluster, the entire cluster got slower. We feel that it should be able to handle such issues.

    Scalability Issues

    No.

    Customer Service and Technical Support

    The support was slow and didn’t provide a solution in most cases. The community proved to be the better source for knowledge and problem solving.

    Initial Setup

    Pretty straightforward, the installation was simple and we added more nodes easily as we grew.

    Pricing, Setup Cost and Licensing

    Vertica is pretty expensive, take into account the servers and network costs before committing.

    Other Solutions Considered

    We evaluated both AWS Redshift and Google BigQuery.

    Redshift didn’t fulfill our expectations regarding query latency at high scale (over 60 TB). Regarding BigQuery, we found the pricing structure pretty complex (payment per query and data processed) and harder to control.

    Other Advice

    Don't plan a production usage on high-scale straight on Vertica, use caching or other buffers between the users and the DB. Get yourself familiar with the DB architecture before planing your model (specifically, make sure you know ROS/WOS and projections). Try to avoid LAP before your schema gets stabilized.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user533094
    Lead Data Scientist Machine Learning at a financial services firm with 51-200 employees
    Vendor
    Columnar storage makes 'hot data' available much faster than a traditional RDBMS solution.

    What is most valuable?

    Columnar storage makes 'hot data' available much faster than a traditional RDBMS solution. Also, Vertica scales up quickly and maintains good performance.

    How has it helped my organization?

    Performance management of high-traffic sites - Vertica's ease of scaling has been invaluable for one of our main customers.

    What needs improvement?

    I'm concerned that HP Enterprise has sold their software business, and worry about future investment to enhance predictive/machine-learning capabilities.

    For how long have I used the solution?

    3 years.

    What do I think about the stability of the solution?

    Not really.... Vertica shines on stability.

    What do I think about the scalability of the solution?

    No, scalability is also a strength of the solution.

    How are customer service and technical support?

    9 out of 10. HPE has some excellent engineers who are eager to help us make Vertica work well.

    Which solution did I use previously and why did I switch?

    I've been a 'full stack' data expert for years, started on Oracle and SQL Server, moved to Hadoop, Mongo, etc, but Vertica was the right fit for large enterprises with high performance demands and ease of scalability.

    How was the initial setup?

    Initial setup is a bit clunky, like most complex, tunable products can be.

    What's my experience with pricing, setup cost, and licensing?

    Negotiate when their fiscal year is about to close :)

    What other advice do I have?

    It's a solid product that keeps its promises. I do worry about HP Enterprise's sale of Vertica to Micro-Focus

    Rating: 8/10 - it works very well, but some customers worry about 'Vendor lock-in'.

    Disclosure: My company has a business relationship with this vendor other than being a customer: We are a Certified Vertica/IDOL (HAVEN) Big Data partner with HP Enterprise.
    ITCS user
    Senior business Intelligence consultant at Asociación SevillaUP
    Consultant
    ​Data Warehouse response times have decreased​. It doesn't support stored procedures in the way we are used to thinking of them.

    What is most valuable?

    Speed in query in general and specifically in aggregate functions on multi-million rows tables.

    How has it helped my organization?

    Data Warehouse response times have decreased of one order of magnitude with respect to the previous solution (SQL Server + Oracle).

    What needs improvement?

    Sadly, it does not support stored procedures in the way we are used to thinking of them. There is the possibility to code plug-in in C++, but that's out of our reach. Correlated sub-queries are another point where we'd love to see enhancements, plus the overall choice of functions available. ETL with SSIS was not as easy as one we had expected (must remember to COMMIT and we had some issues with datetime + timezone, but that's was probably our fault).

    OleDB and .NET providers need some touches; and another great improvement would be support for Entity Framework, which so far I haven't seen.

    There is no serious graphical IDE for HPE Vertica, that's frustrating. One free option available is DbVisualizer for Vertica, but it's a bit basic.

    For how long have I used the solution?

    One year.

    What do I think about the stability of the solution?

    We have a one node cluster on Red Hat and last week the DB went down. The setting to restart the database is not very intuitive and by default the DB does not restart alone.

    After a reboot, which may be good in some environments, but leaves you with an insecurity feeling.

    What do I think about the scalability of the solution?

    Our DB isin in the tens of Gigs, we did not need to scale yet.

    How are customer service and technical support?

    N/A, not used.

    Which solution did I use previously and why did I switch?

    We had SQL Server, switched for money reasons and space. But we're not sure yet, SQL Server is way more stable and predictable.

    How was the initial setup?

    No, the documentation is scarce on non standard setups. We had to create a virtual machine locally, set it up and then upload it to AWS.

    What's my experience with pricing, setup cost, and licensing?

    We use the free community license, plenty of space for our environment. If I had unlimited budget I'd buy a preinstalled instance on EC2, much faster, but costly.

    Which other solutions did I evaluate?

    Netezza, but I didn't like it. For no particular reason, but the feeling was not right. Redshift - I was not impressed by the performance. Google Big Query - we tried it.

    What other advice do I have?

    Do COMMIT, and enable/enforce constraints because by default they ARE NOT!!!!

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user539496
    Development Operations/SRE at a computer software company with 501-1,000 employees
    Vendor
    We built a custom analytical tools on top of Vertica.

    What is most valuable?

    HA Clustering Speed / Performance

    How has it helped my organization?

    We're able to retrieve queries nearly instantaneous for our custom analytical tools we built on top of Vertica.

    What needs improvement?

    More frequent updates.

    For how long have I used the solution?

    1 year

    What do I think about the stability of the solution?

    None.

    What do I think about the scalability of the solution?

    None.

    How are customer service and technical support?

    Very knowledgable team which has provided excellent documentation for every issue we've had to troubleshoot.

    Which solution did I use previously and why did I switch?

    MonetDB -- unstable, frequent crashes.

    How was the initial setup?

    Straightforward, was able to get the database up fairly quickly…

    What is most valuable?

    • HA Clustering
    • Speed / Performance

    How has it helped my organization?

    We're able to retrieve queries nearly instantaneous for our custom analytical tools we built on top of Vertica.

    What needs improvement?

    More frequent updates.

    For how long have I used the solution?

    1 year

    What do I think about the stability of the solution?

    None.

    What do I think about the scalability of the solution?

    None.

    How are customer service and technical support?

    Very knowledgable team which has provided excellent documentation for every issue we've had to troubleshoot.

    Which solution did I use previously and why did I switch?

    MonetDB -- unstable, frequent crashes.

    How was the initial setup?

    Straightforward, was able to get the database up fairly quickly with minimal effort.

    What's my experience with pricing, setup cost, and licensing?

    We're still using the Community Edition (CE).

    Which other solutions did I evaluate?

    MonetDB, Cassandra, Amazon RedShift.

    What other advice do I have?

    Great product, very mature and robust. Vertica is able to scale to meet our demands as we scale our business 10x.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user528873
    Data Scientist at a media company with 501-1,000 employees
    Vendor
    The fact that it is a columnar database is valuable. Columnar storage has its own benefit with a large amount of data.

    What is most valuable?

    The fact that it is a columnar database is valuable. Columnar storage has its own benefit with a large amount of data. It's superior to most traditional relational DB when dealing with a large amount of data. We believe that Vertica is one of the best players in this realm.

    How has it helped my organization?

    Large-volume queries are executed in a relatively short amount of time, so that we could develop reports that consume data in Vertica.

    What needs improvement?

    Speed: It's already doing what it is supposed to do in terms of speed but still, as a user, I hope it gets even faster.

    Specific to our company, we do store the data both in AWS S3 and Vertica. For some batch jobs, we decided to create a Spark job rather than Vertica operations for speed and/or scalability concerns. Maybe this is just due to the computation efficiency between SQL operations vs. a programmatic approach. Even with some optimization (adding projections for merge joins and grouped by pipelined), it's still taking a longer time than a Spark job in some cases.

    For how long have I used the solution?

    I have personally used it for about 2.5 years.

    What do I think about the stability of the solution?

    I have not recently encountered any stability issues; we have good health checks/monitoring around Vertica now.

    What do I think about the scalability of the solution?

    I have not encountered any scalability issues; I think it's scalable.

    How are customer service and technical support?

    N/A; don't have much experience on this.

    Which solution did I use previously and why did I switch?

    We do have some pipelines accessing raw data directly and process it as a batch Spark job. Why? I guess it's because the type of operations we do can be done easily in code vs. SQL.

    What other advice do I have?

    I would recommend using Vertica for those people/teams having large denormalized fact tables that need to be processed efficiently. I worked around optimizing the query performance dealing with projections, merge joins and groupby pipelines. It paid off at the end.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user515835
    Solution Engineering and Arcitect - Big Data, Data Science and Cloud Computing at a tech services company with 1,001-5,000 employees
    Consultant
    It delivers speed and performance in query response time. Complicated multi-table queries perform well.

    What is most valuable?

    Speed and performance: Vertica stands top among its peers in the MPP world, delivering unparalleled speed and performance in query response time. Its distributed architecture and use of projection (materialized version of data) beats most of its competitors.

    How has it helped my organization?

    This product is used for in-database analytics for reports and queries that require very fast response times. Complicated multi-table queries perform very well, and the company has improved on business operations looking at hot data from various dimensions.

    What needs improvement?

    Projections take up a lot of space and hence, compression can be improved. Installation can be more intuitive via a simple, lightweight web client instead of the command line.

    For how long have I used the solution?

    I have used it for two years.

    What do I think about the stability of the solution?

    While Vertica is otherwise stable, sometimes there are issues with restores to the last checkpoint.

    What do I think about the scalability of the solution?

    I have not encountered any scalability issues.

    How are customer service and technical support?

    Technical support is very good and knowledgeable.

    Which solution did I use previously and why did I switch?

    I previously used Postgres; switched as performance suffered due to data growth.

    How was the initial setup?

    Initial setup was straightforward through the command line.

    What's my experience with pricing, setup cost, and licensing?

    Negotiate; with HDFS, storage is cheap. Vertica charges per terabyte of compressed data. But the underlying architecture materializes data in a different order and hence space utilization is always heavy, even for a single table; add to that the replication factor.

    Which other solutions did I evaluate?

    Before choosing this product, we evaluated Netezza and ParAccel.

    What other advice do I have?

    Make sure the data and table structures are compact. Vertica will create many versions of the same data as a projection and isolated tables will increase size, increasing licensing cost.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user514728
    Senior DBA at a local government with 1,001-5,000 employees
    Vendor
    We use it for marketing analytics. Documentation could be improved.

    Valuable Features

    • Compression / speed with highly complex queries

    Improvements to My Organization

    We use it for analytics (marketing).

    Room for Improvement

    • Performance tuning
    • Not much by way of any documentation: The explain plans are very difficult to read / understand. I tried to diagnose some specific queries using the DBD Vertica utility, etc. For one example of using the explain plan, the query was complex with lots of joins and so on (the query took up about three A4 pages), but the explain plan I printed out took up in excess of 32 A4 pages. How on earth would you read that? No visual tools were available that I could find.
    • Very little if any training available in the UK: Our company wasn't able to find any on the topic. We found very little if any documentation (from the vendor) that was of much use.
    • Cloning / export was not well documented; poor examples.

    Use of Solution

    I have used it for three years. I worked with versions 4-7.x.

    Stability Issues

    I occasionally encountered stability issues (more so in earlier versions).

    Scalability Issues

    I have not encountered any scalability issues.

    Customer Service and Technical Support

    Technical support is excellent.

    Initial Setup

    Initially when I first started, the documentation, etc. available was scarce. However, this has improved substantially.

    I was used to OLTP and DWH solutions based on technology such as Oracle, so some of the concepts are quite different.

    Other Solutions Considered

    Before choosing this product, other options were considered, e.g., Kognitio.

    Other Advice

    It’s still not mainstream (especially in the UK) and I would say to some extent still ‘improving’ at each release, but it is enterprise ready and a hugely cheaper option than some.

    We did some like-for-like comparisons between HP Vertica and Oracle Exadata (work load / timings) and the two compared favourably, with Vertica being faster than Oracle in all but the biggest and most complex of queries.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user418314
    Associate at a tech services company with 501-1,000 employees
    Consultant
    I like the clustering aspect with the share-nothing mentality. I also value the ease of maintenance.

    What is most valuable?

    The biggest, most valuable feature for us is the clustering aspect with a share-nothing mentality. Most clusters usually require their own shared storage, shared subnet, etc. and this becomes a pain and a nightmare to maintain.

    The second most valuable feature is that it's very easy to maintain. It's a breeze once you know how to handle it with your scenario in mind.

    How has it helped my organization?

    Loading raw data and leveraging column store to quickly aggregate the values as well as run a general analysis were the biggest improvements we found. Before, we had to scrub the data or reformat, load it, possibly scrub it some more, and then run the first set of analysis, and so on.

    With Vertica, we were able to combine some of these steps, such as loading gzip data directly into the table and leveraging R in Vertica to run all of the analysis.

    What needs improvement?

    Developer Tools - Vertica really needs some kind of IDE plugin for a system such as Eclipse or IntelliJ. Developing external functions in Vertica can kind of be like shooting in the dark sometimes. Also, an improved monitor or monitoring with alerting built-in that actually works would be a welcome addition.

    They truly need a Python or some script that can handle all of the low-level system changes for you and find out how the customer has heavily modified their nodes before the install. Some automation here would help a lot.

    The product overall is a great product, however management tools as well as monitoring tools are lacking. The product does, however, offer a lot of information in the form of system views and tables, but most of the data is hard to translate with out the help of their support team.

    For how long have I used the solution?

    I have used HP Vertica in multiple companies over the last four years. We currently have it running on a three-node Centos cluster and a six-node Centos cluster.

    What was my experience with deployment of the solution?

    There have been no issues with the deployment.

    What do I think about the stability of the solution?

    There have been no issues with the stability.

    What do I think about the scalability of the solution?

    We have had no issues scaling it for our needs.

    How are customer service and technical support?

    Like everything else HP has support for, the support is very poor. You normally have to threaten to leave, not buy support renewals, or call your sales rep to talk
    to anyone who knows anything about the product. The community normally knows more than support and most of my questions or issues were resolved by searching the old community boards while I wait for overseas support to ask me to send them the logs again for the 50th time.

    Which solution did I use previously and why did I switch?

    I have previously tried SQL PDW, Mongo, Cassandra for alternatives. Even though all of those products are in different landscapes, the Vertica column store ended up being the best thing that was needed.

    How was the initial setup?

    It is straightforward if you read the documents and have mid to senior-level knowledge of Linux. Non-Linux admins will find the setup complex and cumbersome since most are Windows admin and they want point-and-click.

    What about the implementation team?

    We implemented through our in-house team. You need to read the docs, then read them again, and then make yourself a cheat sheet. Once you have done the setup for a two-node cluster, do some Research and Development before taking the time to do a large production cluster or buy the license.

    What was our ROI?

    ROI is great compared to the previous solution, SQL Server.

    What's my experience with pricing, setup cost, and licensing?

    TCO is much lower given the Linux OS and the fact that Vertica is licensed by data size and not node count. The best advice for licensing is to make sure you have a proper data retention policy in place and well-documented as well as some growth expectations before buying. Following this, it will make sure you don't over or under buy.

    What other advice do I have?

    If you are not Linux savvy, find a person that is. Make a cheat sheet with the commands and/or steps for your environment. If you are in the cloud, make sure to understand the networking aspect is completely different in AWS from it will be in your local data center. Failure to plan is planning to fail with Vertica implementation, and try not to mess up the spread as it's a pain to fix. If you read the documents, you will see what I am talking about.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Vertica DBA at a tech services company with 51-200 employees
    Consultant
    It has helped us escalate, we need information almost real-time.

    What is most valuable?

    Analytical features are amazing, the integration is wonderful.

    How has it helped my organization?

    It has helped us escalate, we need information almost real-time.

    What needs improvement?

    Documentation, there are functions that are not documented. UDF SDK, I'd like to see a step by step simulator example in a manual. The read-me code is good, however, an example would be great for starters.

    For how long have I used the solution?

    3 years

    What was my experience with deployment of the solution?

    Yes, cluster migration takes time.

    What do I think about the stability of the solution?

    Yes, in data streaming ROS containers is a pain to work with.

    What do I think about the scalability of the solution?

    Not yet.

    How are customer service and technical

    What is most valuable?

    Analytical features are amazing, the integration is wonderful.

    How has it helped my organization?

    It has helped us escalate, we need information almost real-time.

    What needs improvement?

    Documentation, there are functions that are not documented. UDF SDK, I'd like to see a step by step simulator example in a manual. The read-me code is good, however, an example would be great for starters.

    For how long have I used the solution?

    3 years

    What was my experience with deployment of the solution?

    Yes, cluster migration takes time.

    What do I think about the stability of the solution?

    Yes, in data streaming ROS containers is a pain to work with.

    What do I think about the scalability of the solution?

    Not yet.

    How are customer service and technical support?

    Customer Service:

    It is good, they answer in good time. There are times that they really don't come with a proper answer.

    Technical Support:

    Decent.

    Which solution did I use previously and why did I switch?

    Yes, regular relational databases. We switched for scalability reasons.

    What about the implementation team?

    In-house.

    Which other solutions did I evaluate?

    Yes, Netezza.

    What other advice do I have?

    I like the new things they are introducing. I want to see more with Python.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    High FrequencyTrading Systems and Strategy Architect/Quant Trader at a financial services firm with 51-200 employees
    Vendor
    Fast inserts, queries way faster than in SQL Server.

    What is most valuable?

    Fast inserts, queries way faster than in SQL Server.

    How has it helped my organization?

    Certain research which was unattainable beforehand, now is in reach.

    What needs improvement?

    Some GUI Tools out of the box, better python integration. I would love to see some nice query engine, tooled specifically to Vertica extensions of SQL (with IntelliSense). We currently use TOAD, but it has a lot of bugs and does not provide full support of all Vertica features. For example with the "copy to" command it is buggy - extremely hard to debug.

    Other issues would be better query plan display and better management tools like command line admin tools. (MC would probably solve a bit here, I saw a demo on the conference). We have several avid Python users in the company, but this how maybe lower priority after I reviewed my conference materials and found that Vertica now has native Python driver.

    For how long have I used the solution?

    2 years.

    What was my experience with deployment of the solution?

    I was acting as my own DBA for a while, so a lot of hurdles before. But things are getting easier as more people in the company bought into the solution. I also got HP Training in house. (Thanks Herb!)

    What do I think about the scalability of the solution?

    We can scale a lot, wish it was a bit more affordable.

    Which solution did I use previously and why did I switch?

    It complements our SQL Server solution.

    What about the implementation team?

    Own efforts.

    What's my experience with pricing, setup cost, and licensing?

    Switch to per node from per TB.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Management Consultant at a computer software company with 51-200 employees
    Vendor
    A SQL-based compute platform like Vertica enables far less human overhead in operations and analytics.

    What is most valuable?

    Scale-out, analytical functions, ML.

    How has it helped my organization?

    We are an HP partner. A SQL-based compute platform like Vertica enables far less human overhead in operations and analytics.

    What needs improvement?

    More ML, both data prep, models, evaluation and workflow.  Improved support for deep analytics/ predictive modelling with machine learning algorithms. This area of analytics need a stack of functionality in order to support the scenario. The needed functionality includes:

    • Data preparation. Scaling, centering, removing skewness, gap filling, pivoting, feature selection and feature generation
    • Algorithms/models. Non-linear models in general. More specifically, penalized models, tree/rule-based models (incl. ensambles), SVM, MARS, Neural networks, K-nearest neighbours, Naïve bayes, etc.
    • Support the concept of a “data processing pipeline” with data prep. + model. One would typically use “a pipeline” as the overall logical unit used to produce predictions/scoring.
    • Automatic model evaluation/tuning. With algorithms requiring tuning, support for automated testing of different settings/tuning parameters is very useful. Should include (k fold) cross validation and bootstrap for model evaluation
    • Some sort of hooks to use external models in a pipeline i.e. data prep in Vertica + model from Spark/R. 
    • Parity functionality for the Java SDK compared to C++. Today the C++ SDK is the most feature rich. The request is to bring (and keep) the Java SDK up to feature parity with C++.
    • Streaming data and notifications/alerts. Streaming data is starting to get well supported with the Kafka integration. Now we just need a hook to issue notifications on streaming data. That is, running some sort of evaluation on incoming records (as they arrive to the Vertica tables) and possibly raising a notification.

    For how long have I used the solution?

    Two years.

    What was my experience with deployment of the solution?

    No, not really.

    What do I think about the stability of the solution?

    No.

    What do I think about the scalability of the solution?

    No.

    Which solution did I use previously and why did I switch?

    Postgresql, MySQL, SQL Server. Switched because of scalability and reliability, analytics functionality. V being a better engineered product.

    How was the initial setup?

    Straightforward. Good docs helped a lot.

    What's my experience with pricing, setup cost, and licensing?

    Its reasonably priced for non-trivial data problems.

    Which other solutions did I evaluate?

    Yes, Hadoop / Spark, SQL Server.

    What other advice do I have?

    See additional functionality above.

    Disclosure: My company has a business relationship with this vendor other than being a customer: We are a vendor partner.
    ITCS user
    Sr. DevOps Engineer, Adometry at a tech company with 10,001+ employees
    Vendor
    We can process vast amounts of data, fast.

    What is most valuable?

    Super-fast aggregated results from massive data.

    How has it helped my organization?

    We can process vast amounts of data, fast and with a high degree of reliability.

    What needs improvement?

    Better feedback from installation. I would like to see more meaningful errors returned and more graceful handling of those. Thankfully, we don't often hit error conditions.

    For how long have I used the solution?

    4 years.

    What was my experience with deployment of the solution?

    No

    What do I think about the stability of the solution?

    No

    What do I think about the scalability of the solution?

    Depends on the environment. Generally pretty good. If you have a large catalog, you can get timeouts adding nodes. Large catalog issues have been dealt with it recent…

    What is most valuable?

    Super-fast aggregated results from massive data.

    How has it helped my organization?

    We can process vast amounts of data, fast and with a high degree of reliability.

    What needs improvement?

    Better feedback from installation. I would like to see more meaningful errors returned and more graceful handling of those. Thankfully, we don't often hit error conditions.

    For how long have I used the solution?

    4 years.

    What was my experience with deployment of the solution?

    No

    What do I think about the stability of the solution?

    No

    What do I think about the scalability of the solution?

    Depends on the environment. Generally pretty good. If you have a large catalog, you can get timeouts adding nodes. Large catalog issues have been dealt with it recent releases so this should make scaling up even more robust.

    How are customer service and technical support?

    Excellent. It can take some time to get to the right people but generally our issues are all addressed in an acceptable timeframe.

    Which solution did I use previously and why did I switch?

    Greenplum. It was less stable.

    Vertica is very robust and recovers predictably from unexpected infrastructure failures.

    What other advice do I have?

    Great overall solution.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Sr. Software Engineer (Database) at a tech services company with 1,001-5,000 employees
    Consultant
    I like the Query Performance.

    Valuable Features:

    Query Performance.

    Room for Improvement:

    More analytical functions. Optimization around DML operations such that we be able to use it more. I understand that Vertica is not meant for that, but I would really love it if it has capability to support procedural processing.

    Optimization around DML refers to fast DELETE and UPDATE statement such that we can leverage Vertica around those operations. I do understand that Vertica is not meant as an OLTP system, neither I'm asking to have it similar but if DML operations can be optimized, that would be admirable.

    Regarding, my comment on the capability to support procedural processing - Vertica as of now is mainly used via SQL only. If we have to use any procedure based operation, we do it via User Defined Functions. If within Vertica itself, we have the capability to create & execute procedure similar to that for functions, it would be a plus. Again, I do understand that this may be against the architecture of Vertica but if there is anything that can be revised to get these supported, that would be preferred.

    Use of Solution:

    3+ years

    Deployment Issues:

    Initially yes, but now we're used to it. Challenges do come up but it's all well understood.

    Stability Issues:

    We had confronted couple of issues during Vertica upgrades for which we do align with your support group.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Database Admin at a tech services company with 1,001-5,000 employees
    Consultant
    Replication is the main feature for my use.

    Valuable Features

    Replication

    Improvements to My Organization

    Replication and Node recovery in 8.0.

    Room for Improvement

    vbr.py needs to be improve to support diff no of nodes source to target.

    Use of Solution

    5 years

    Deployment Issues

    No

    Stability Issues

    Yes

    Scalability Issues

    No

    Customer Service and Technical Support

    Customer Service: 8 Technical Support: 8/10

    Valuable Features

    Replication

    Improvements to My Organization

    Replication and Node recovery in 8.0.

    Room for Improvement

    vbr.py needs to be improve to support diff no of nodes source to target.

    Use of Solution

    5 years

    Deployment Issues

    No

    Stability Issues

    Yes

    Scalability Issues

    No

    Customer Service and Technical Support

    Customer Service:

    8

    Technical Support:

    8/10

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Senior Database Administrator; HP Vertica, MySQL, MSSQL, Cloud Ops at a tech vendor with 501-1,000 employees
    Real User
    For me the most valuable aspect is the speed of columnar data.

    What is most valuable?

    Speed of columnar data.

    What needs improvement?

    Performance tuning; user community is needed.

    For how long have I used the solution?

    1 year

    What do I think about the stability of the solution?

    Node recovery is very inconsistent and impacts performance.

    What do I think about the scalability of the solution?

    Concurrency

    How are customer service and technical support?

    Customer Service: Terrible Technical Support: Terrible

    Which solution did I use previously and why did I switch?

    SQL Server and Oracle.

    How was the initial setup?

    Simple

    What is most valuable?

    Speed of columnar data.

    What needs improvement?

    Performance tuning; user community is needed.

    For how long have I used the solution?

    1 year

    What do I think about the stability of the solution?

    Node recovery is very inconsistent and impacts performance.

    What do I think about the scalability of the solution?

    Concurrency

    How are customer service and technical support?

    Customer Service:

    Terrible

    Technical Support:

    Terrible

    Which solution did I use previously and why did I switch?

    SQL Server and Oracle.

    How was the initial setup?

    Simple

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    DW Admin at a hospitality company with 1,001-5,000 employees
    Vendor
    It is the foundation of our new Data Warehouse platform because of the scalability and query speed.

    What is most valuable?

    Scalability, query speed.

    How has it helped my organization?

    It is the foundation of our new Data Warehouse platform.

    What needs improvement?

    Data velocity and manageability.

    For how long have I used the solution?

    The utilization/launching project has lasted for a bit more than a year now.

    What was my experience with deployment of the solution?

    There have been some issues with deleting and updating data from tables.

    What do I think about the stability of the solution?

    No

    What do I think about the scalability of the solution?

    No

    How are customer service and technical support?

    Customer Service: I have not been in contact with their customer service. Technical Support: I have not been in contact with their tech support.

    Which solution

    What is most valuable?

    Scalability, query speed.

    How has it helped my organization?

    It is the foundation of our new Data Warehouse platform.

    What needs improvement?

    Data velocity and manageability.

    For how long have I used the solution?

    The utilization/launching project has lasted for a bit more than a year now.

    What was my experience with deployment of the solution?

    There have been some issues with deleting and updating data from tables.

    What do I think about the stability of the solution?

    No

    What do I think about the scalability of the solution?

    No

    How are customer service and technical support?

    Customer Service:

    I have not been in contact with their customer service.

    Technical Support:

    I have not been in contact with their tech support.

    Which solution did I use previously and why did I switch?

    We used Oracle but it did not scale well enough

    How was the initial setup?

    It was straightforward.

    What about the implementation team?

    I was not involved with the implementation.

    What was our ROI?

    Don't know.

    What's my experience with pricing, setup cost, and licensing?

    The pricing is very flexible

    Which other solutions did I evaluate?

    There was a proof of concept for a number of technologies but I wasn't involved in those.

    What other advice do I have?

    It looks promising.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Principal Data Architect (BI) at a media company with 1,001-5,000 employees
    Vendor
    I like the fast analytical functions and ability to extend functionality.

    What is most valuable?

    Bulk loading data using copy Fast analytical functions Ability to extend functionality

    How has it helped my organization?

    Great reporting.

    What needs improvement?

    Faster deletes!

    For how long have I used the solution?

    4 years

    What was my experience with deployment of the solution?

    None

    What do I think about the stability of the solution?

    Sometimes users write bad queries that has brought down the cluster. Need a way to better manage resources.

    What do I think about the scalability of the solution?

    None

    How are customer service and technical support?

    Customer Service: Great Technical Support: Great

    Which solution did I use previously and why did I switch?

    No

    What about the implementation team?

    In house implementation.

    What is most valuable?

    Bulk loading data using copy

    Fast analytical functions

    Ability to extend functionality

    How has it helped my organization?

    Great reporting.

    What needs improvement?

    Faster deletes!

    For how long have I used the solution?

    4 years

    What was my experience with deployment of the solution?

    None

    What do I think about the stability of the solution?

    Sometimes users write bad queries that has brought down the cluster. Need a way to better manage resources.

    What do I think about the scalability of the solution?

    None

    How are customer service and technical support?

    Customer Service:

    Great

    Technical Support:

    Great

    Which solution did I use previously and why did I switch?

    No

    What about the implementation team?

    In house implementation.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Senior Data Architect at a media company with 1,001-5,000 employees
    Vendor
    Having the ability invoke analytic functions without having write self join SQL statements is beneficial.

    Valuable Features:

    Analytic functions.

    Improvements to My Organization:

    We are trying to data mine customer event data. Having the ability invoke analytic functions without having write self join SQL statements ... just brilliant.

    Room for Improvement:

    Ability to use analytic functions in where clauses, being able to use aliases in the where and order by clauses will make query writing/reading a lot easier.

    Use of Solution:

    2 years.

    Valuable Features:

    Analytic functions.

    Improvements to My Organization:

    We are trying to data mine customer event data. Having the ability invoke analytic functions without having write self join SQL statements ... just brilliant.

    Room for Improvement:

    Ability to use analytic functions in where clauses, being able to use aliases in the where and order by clauses will make query writing/reading a lot easier.

    Use of Solution:

    2 years.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Owner at a tech vendor
    Vendor
    We switched from our previous solution because SQL Server did not scale.

    What is most valuable?

    Geospatial

    What needs improvement?

    Profiling, query optimize, management.

    For how long have I used the solution?

    1 year

    What was my experience with deployment of the solution?

    Some bugs, they were rapidly fixed.

    What do I think about the stability of the solution?

    Minute

    What do I think about the scalability of the solution?

    Not yet

    How are customer service and technical support?

    Customer Service: Excellent Technical Support: Very good

    Which solution did I use previously and why did I switch?

    SQL Server did not scale.

    What is most valuable?

    Geospatial

    What needs improvement?

    Profiling, query optimize, management.

    For how long have I used the solution?

    1 year

    What was my experience with deployment of the solution?

    Some bugs, they were rapidly fixed.

    What do I think about the stability of the solution?

    Minute

    What do I think about the scalability of the solution?

    Not yet

    How are customer service and technical support?

    Customer Service:

    Excellent

    Technical Support:

    Very good

    Which solution did I use previously and why did I switch?

    SQL Server did not scale.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Infrastructure, Data Center and PMO Coordinator at a comms service provider with 501-1,000 employees
    Vendor
    We could use it to offer Analytics As A Service to our customers.

    Valuable Features

    Manage big data fast and easy.

    Room for Improvement

    The time that the mediation process takes and historical information that I can store.

    Deployment Issues

    Yes, there is a functionality in Vertica "Broadcast" that high the process level of our Network Switch Core. I had a serious problem with this because I interrupted the network service in the company. We have to change "Point to point".

    Customer Service and Technical Support

    Customer Service: Excellent! Technical Support: Excellent!

    Other Solutions Considered

    Yes, Netezza and SAP HANA.

    Other Advice

    I would like HP to help me to do more uses cases with Vertica. We are very interested in becoming a Business Partner in order to offer Analytics As A Service to our customers.

    Valuable Features

    Manage big data fast and easy.

    Room for Improvement

    The time that the mediation process takes and historical information that I can store.

    Deployment Issues

    Yes, there is a functionality in Vertica "Broadcast" that high the process level of our Network Switch Core. I had a serious problem with this because I interrupted the network service in the company. We have to change "Point to point".

    Customer Service and Technical Support

    Customer Service:

    Excellent!

    Technical Support:

    Excellent!

    Other Solutions Considered

    Yes, Netezza and SAP HANA.

    Other Advice

    I would like HP to help me to do more uses cases with Vertica. We are very interested in becoming a Business Partner in order to offer Analytics As A Service to our customers.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Data Scientist at a tech vendor with 201-500 employees
    Vendor
    We were able to implement new algorithms without having to move data out of Vertica into a compute cluster.

    Valuable Features

    User Defined Extensions Analytic Functions

    Improvements to My Organization

    We were able to implement new algorithms without having to move data out of Vertica into a compute cluster. This allowed us to offer Analytics for Cybersecurity to our customers.

    Room for Improvement

    More Machine Learning algorithms--Random Forest for sure!

    Customer Service and Technical Support

    Customer Service: Very responsive Technical Support: Excellent

    Implementation Team

    In-house

    Valuable Features

    User Defined Extensions

    Analytic Functions

    Improvements to My Organization

    We were able to implement new algorithms without having to move data out of Vertica into a compute cluster. This allowed us to offer Analytics for Cybersecurity to our customers.

    Room for Improvement

    More Machine Learning algorithms--Random Forest for sure!

    Customer Service and Technical Support

    Customer Service:

    Very responsive

    Technical Support:

    Excellent

    Implementation Team

    In-house

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Data Scientist at a tech services company with 501-1,000 employees
    Consultant
    We're able to test more models and improve accuracy.

    Valuable Features

    Group by performance Analytic functions

    Improvements to My Organization

    We could run group by queries thousand of times faster, we are able to test more models and improve accuracy.

    Room for Improvement

    Debug custom functions in r.

    Use of Solution

    One year

    Deployment Issues

    None

    Stability Issues

    None

    Scalability Issues

    None

    Customer Service and Technical Support

    Customer Service: Great! Email response is quickly and also within reported issues are resolved. Technical Support: Ggreat, they really understand what they are talking about.

    Initial Setup

    Straightforward, very easy.

    Implementation Team

    In house

    Valuable Features

    Group by performance

    Analytic functions

    Improvements to My Organization

    We could run group by queries thousand of times faster, we are able to test more models and improve accuracy.

    Room for Improvement

    Debug custom functions in r.

    Use of Solution

    One year

    Deployment Issues

    None

    Stability Issues

    None

    Scalability Issues

    None

    Customer Service and Technical Support

    Customer Service:

    Great! Email response is quickly and also within reported issues are resolved.

    Technical Support:

    Ggreat, they really understand what they are talking about.

    Initial Setup

    Straightforward, very easy.

    Implementation Team

    In house

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Senior Database Administrator at a financial services firm with 51-200 employees
    Vendor
    I like Recovery by table. I would like messages to Vertica startup commands improved.

    Valuable Features:

    Recovery by table Copy cluster

    Improvements to My Organization:

    VBR backup used to take more than one week to back up 70 TB of data. After upgrading to latest version, it is taking about 48 hours.

    Room for Improvement:

    Improve Vertica logging and messages to Vertica startup commands.

    Use of Solution:

    4 years

    Valuable Features:

    Recovery by table

    Copy cluster

    Improvements to My Organization:

    VBR backup used to take more than one week to back up 70 TB of data. After upgrading to latest version, it is taking about 48 hours.

    Room for Improvement:

    Improve Vertica logging and messages to Vertica startup commands.

    Use of Solution:

    4 years

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Sr. Developer, Big Data at a comms service provider with 51-200 employees
    Vendor
    The most valuable feature for me is the columnar data store.

    Valuable Features:

    Columnar data store

    Room for Improvement:

    Add geospatial indexes (sounds like they have done it in version 8.0)

    Deployment Issues:

    No

    Stability Issues:

    No

    Scalability Issues:

    No

    Customer Service:

    Above average

    Initial Setup:

    Setup was very simple

    Valuable Features:

    Columnar data store

    Room for Improvement:

    Add geospatial indexes (sounds like they have done it in version 8.0)

    Deployment Issues:

    No

    Stability Issues:

    No

    Scalability Issues:

    No

    Customer Service:

    Above average

    Initial Setup:

    Setup was very simple

    Disclosure: My company has a business relationship with this vendor other than being a customer: We are partners with HPE
    ITCS user
    CIO with 1,001-5,000 employees
    Vendor
    Features valuable to me include: massive data ingestion performance and SQL standard query engine.

    Valuable Features:

    Massive data ingestion performance Performance SQL standard query engine

    Improvements to My Organization:

    DWH core platform is based on it

    Room for Improvement:

    Machine learning implementations Support for Cloud based environments like Google Compute Engine

    Use of Solution:

    3 years

    Valuable Features:

    • Massive data ingestion performance
    • Performance
    • SQL standard query engine

    Improvements to My Organization:

    DWH core platform is based on it

    Room for Improvement:

    Use of Solution:

    3 years

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    BD
    System Architect at a comms service provider with 10,001+ employees
    Real User
    We can quickly identify with the root cause analysis where trends are.

    Valuable Features:

    We're just now getting into Vertica, but it allows us to store and access big data very quickly. It comes down to being able to quickly identify where the root cause analysis is and where trends are, so you can actually try to almost predict where problems are before they really become a problem.

    Improvements to My Organization:

    The ability to access in-store, big data, and be able to create keywords for faster resolution and look up an individual, hey we did this problem before. It'll show you all the steps and everything, along with different products. Vertica is pretty much the database behind it. It really does the performance aspect of it.

    Room for Improvement:

    I guess really the only thing there is if you get a server big enough to handle Vertica, it does just fine. If you're working in a small business, it will tend to overtake most of their budget from a cost perspective because you need so many servers, so much storage, to be able to handle all that stuff.

    Stability Issues:

    It's very stable.

    Initial Setup:

    We had no issues deploying it.

    Other Solutions Considered:

    I did not really look at any competition. Basically, it's like I said, we're an HP shop and a lot of their applications are going to a Vertica database for its storage and processing of data. We were doing a lot of Oracle, but Oracle was actually moving towards Vertica in our environment.

    Other Advice:

    Make sure you understand how much data that you're going to be incorporating into the big data, so you can actually define the amount of storage and redundant storage appropriately.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user471384
    CIO at a tech services company with 1,001-5,000 employees
    Consultant
    It works well. When we ran into issues, there seemed to be a lot of different opinions for how to resolve them.

    What is most valuable?

    We use Vertica as our primary data warehouse. It works well, relatively, most of the time.

    What needs improvement?

    I just expect it to work and be serviceable. When we ran into issues, there seemed to be a lot of different opinions for how to resolve the issues and that was the feedback I gave to them. You talked to one tech, you talk to a different tech they had a much different approach. That was a big frustration point for us.

    The upgrade path and which way we should go. So at the end it created a lot of confusion for us, so I wouldn't upgrade it again lightly. We're going to remain on it for the next year, but we'll probably re-evaluate at that point if we want to continue with Vertica or something else.

    What do I think about the stability of the solution?

    It's been stable since November and before that, to be fair, it was stable for quite a while.

    What do I think about the scalability of the solution?

    The reason we like Hadoop and others is because they scale up, pricing doesn't scale up at the same level. Vertica is a license per terabyte product. They do give you discounts the more volume you get, but it adds up over time fast. We could scale at a lower cost with than other solutions.

    Scaling was a pain point. Getting recommendation on how to set it up ultimately to provide the best performance, how many notes, other things. We got different answers from them.

    Which solution did I use previously and why did I switch?

    We use MongoDB for some of our other internal production apps. It's a lot more involved and more complex than we like to go for a, just standard data warehouse, but we might look at Hadoop or similar for that.

    How was the initial setup?

    There's a lot of complexities with the upgrade and costs of data failures. That was last year. It was kind of good that I forgot about those pain points.

    What other advice do I have?

    I would recommend that they highly evaluate all their options. If they're just going to run a small data warehouse, it's probably not a bad solution. If it's something they know is going to grow dramatically and unpredictably? I don't know. I would evaluate hard.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Senior Data Warehouse Architect at a media company
    Vendor
    The Workload Analyzer helps you easily to analyze your database workloads and recommends tuning opportunities to maximize the database performance.

    Valuable Features

    Storage abstraction through projections. It gives you the possibility to react to any kind of query with an optimal performance.

    The Workload Analyzer helps you easily to analyze your database workloads and recommends tuning opportunities to maximize the database performance. This in turn reduces your operational costs.

    I love the hybrid storage model and due to that the full control of load and query behavior. I also like the ability to read semistructured data with FlexTables for DataExploration.

    Improvements to My Organization

    We are now able to procde real-time insights into our tracking data, and with that show how our customers are using the products that we have. Furthermore, it is now possible for our Data Science department to easily, and quickly train their new data mining models and get answers faster than ever before.

    With the hybrid storage model along with well designed resource pools and storage abstraction through projections, we are now able to easily load new data constantly throughout the whole day. While doing this, we can still be available to perform data analytics on new and legacy data quickly, and even Microstrategy for enterprise reporting doesn’t need to cache data. Most reports can be generated with live queries and still finish within seconds.

    So in a nutshell:
    - Faster Information Insight (Data to Insight cycle)
    - Less complexity on data modeling
    - Less operational costs

    Room for Improvement

    I would love to see direct connections to other DMSs. Something like a direct connector to Oracle, MySQL, MS SQL, MongoDB, etc. so that you can copy data between Vertica and other vendors directly and more easily without an ETL tool, dump, transport, or load data.

    Use of Solution

    I've been using Vertica for two and a half years.

    Scalability Issues

    We had an issue caused by adding nodes, but this error was caused by ourselves, as we didn’t use the proper process for adding nodes. That led to some problems that needed to be solved. Even though we did something bad, the instance was still working properly from an outside point of view.

    Customer Service and Technical Support

    We had to contact support for the above mentioned issues with adding nodes, and some other minor questions. All pf our questions were been answered in an appropriate time, and for the complicated problem we needed to solve, we were provided a direct contact and solved this during a conference call with a technician from Boston. So all in all, I would rate the customer service and technical support team from HPE Vertica as one of the best.

    Initial Setup

    The documentation and install procedures cannot be any more straightforward. You get all the information you need from the documentation in a well structured form. We also got support from Vertica for the first setup. They made hardware configuration suggestions and involved us in any details to help us to understand the overall process. During installation, the scripts were check numerous hardware and software settings to help you achieve the best performance for your environment.

    Implementation Team

    We implemented our first cluster in collaboration with the HPE Vertica team. I would always suggest this step, as you will be able to better understand the details about Vertica and how to operate the system efficiently.

    Pricing, Setup Cost and Licensing

    My advice for pricing/licensing/ROI in a "proprietary proprietary“ comparison. You won’t achieve a better cost effectiveness with a different vendor.

    Other Solutions Considered

    We did a PoC between competitors and Vertica. Throughout the whole PoC, Vertica performed much better in terms of its stability, flexibility, performance and ease of use. We didn’t encounter any problems or downsides, and it didn’t matter what we tested. At that stage, just the Management Console had some minor issues, but even those are now fixed and are not important for the core database engine. I would name HPE Vertica as the most mature columnar database with a best of class data storage and query engine.

    Other Advice

    From the beginning, work closely with HPE Vertica. There's a great Vertica community and a great network to many other companies in the world using this system. Vertica is the most flexible columnar storage with an outstanding performance for any kind of situation.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PI
    Vertica Database Architect at a tech consulting company with 51-200 employees
    Consultant
    It's pretty straightforward to get the cluster up and running.

    Valuable Features

    • Speed
    • Parallelization
    • SQL language
    • High Availability

    Improvements to My Organization

    I have seen queries that take over 24 hours on MS SQL Server to complete, complete in less than 10 minutes on Vertica. I have seen queries that take several minutes, up to an hour, on MS SQL Server, complete in less than 10 seconds, sometime less than one second on Vertica. That allows analysts to spend their time analyzing results instead of waiting for results. Certain types of analysis weren’t even possible before, simply because it took too long.

    Room for Improvement

    While the documentation is very extensive and relatively complete, it’s poorly organized and there are way too few examples. It’s come a long way since the first version I saw, but it still has a long way to go. Plus, there is very little information on the internet. I can find a solution to nearly any MS SQL Server problem using Google. Not so for Vertica.

    Use of Solution

    I've been using it for five years. I started with version 4, which was prior to the HP acquisition.

    Deployment Issues

    It’s a breeze to setup if you’re using hardware and an OS that meet the minimum requirements. If you try straying from the recommendations, you can find yourself in trouble.

    Stability Issues

    If your queries and projections are optimized properly, it’s rare that you’ll run into stability issues. Stability issues are usually caused by improperly configured hardware/OS, or poorly written queries/projections.

    Scalability Issues

    Scalability is great if you size it correctly to start with. Resizing a cluster isn’t for the faint of heart. All the data needs to be redistributed across the cluster when the cluster size changes, and that can take a very long time, depending on how much data you’re storing.

    Customer Service and Technical Support

    The technical support for Vertica specifically is great. They still have lots of the original (pre-HP acquisition) support people working there who know the product inside and out.

    Initial Setup

    It's pretty straightforward to get the cluster up and running - assuming you follow the vendor recommendations closely. Getting your data in, setting up projections, optimizing queries, etc. is not as straightforward. If you’ve never used it before, save yourself hours of frustration and hire a Vertica consultant.

    Implementation Team

    The first time I used Vertica, we tried doing it ourselves in the beginning. We learned a lot from our failures, but still weren’t getting the results we’d hoped for. After getting professional services help, we were pointed in the right direction, and that made a world of difference. I highly recommend bringing in someone who knows what they’re doing to get you started on the right foot.

    Pricing, Setup Cost and Licensing

    It’s expensive, but it’s good once you get it working properly. Like any complicated software product, you’re paying for years of research and development, support, etc. Everyone’s use case is different, and sometimes it’s difficult to put a price on speed. You pay for the storage, not the number of processors or nodes. They have a community edition that allows up to three nodes with up to one TB of storage. You can try it out for free that way, and once you realize how well it works, you can purchase a commercial license as your storage footprint grows.

    Other Solutions Considered

    At a previous company, we looked at Greenplum as an alternative to Vertica. For our specific use-case, Vertica won the majority of our benchmark tests. If we had a design that required lots of updates and deletes, we may have compromised and gone with Greenplum.

    Other Advice

    How useful it is depends upon your use case. It’s not a be-all and end-all solution, and it’s great for data that doesn’t change. If you have massive fact and dimension tables, and you need to do analytics on them, this is the Cadillac. If you’re trying to replace your OLTP system, there are better suited solutions out there.

    These days, there are lots of alternative solutions in the big data space. Open source vs. Commercial. Every imaginable use case. Just like any project, there is the right tool for the job, but you don’t always know what tools are available. You end up using something because it worked before on a different job, or it’s the cheapest solution. Your best bet is always to closely determine your requirements, then find the best match.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    BI Manager, Vertica ASE Certified DBA at a marketing services firm with 1,001-5,000 employees
    Real User
    The ability to view running queries and cancel problem ones from the Management Console is a very nice feature.

    Pros and Cons

    • "The fast columnar store database structure allows our query times to be at least 10x faster than on any other database."
    • "We are able to integrate our Vertica data warehouse with Tableau to create numerous reports quickly and efficiently."
    • "We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds. ​"
    • "Integrated R and geospatial functions are helping us improve efficiency and explore new revenue streams. ​"
    • "Documentation has become much better, but can always use some improvement."

    What is our primary use case?

    Vertica is our sole data warehouse solution. It is our single point of access to all data loaded from disparate data sources across the organization, and is the single point of truth for all business rules encapsulated in our fact and dimension tables. All of our reporting to all business departments originates from Vertica.  We are also using Vertica's inherent analytic functions, most notably geospatial, and are automating much of our analytics team's R libraries and functions into Vertica for faster processing.

    How has it helped my organization?

    The fast columnar store database structure allows our query times to be at least 10x faster than on any other database. This enables us to get answers to data questions as well as numerous analytics on our data out to our internal and external clients quickly. We are able to integrate our Vertica data warehouse with Tableau to create numerous reports quickly and efficiently. What was once a two year backlog of report requests on our old data system has been virtually eliminated now that we are using Vertica to provide the solutions.

    We are able to create complex reports in Tableau by crunching the data in Vertica first and simply extracting the data to Tableau. We have used Vertica to automate manual processes across our business that previously used mostly Excel, and now R, improving efficiency company-wide. We have saved our Analytics Department days worth of man hours each month by using Vertica's Integrated R package instead of their local R Studio implementations. We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds.  

    What is most valuable?

    I have found great use out of many features, most notably the Management Console and the Database Designer. Many people with lots of experience creating table projections can get frustrated trying to optimize some complex queries, however, in Vertica, the Database Designer is normally a big help in these situations. You can feed it your problem queries and it will make projection design suggestions for you. The ability to have multiple projections on a table to work with different queries is a big bonus.

    The Management Console is an invaluable tool for monitoring the health of our Production and DR clusters. Copy Cluster and Cluster Replication help us keep both easily in sync on a daily basis. Integrated R and geospatial functions are helping us improve efficiency and explore new revenue streams.  

    What needs improvement?

    Documentation has become much better, but can always use some improvement. Love the tech support, but hoping Micro Focus will invest in some additional training for the Level 1 responders so they are much more familiar with more areas of the product.

    For how long have I used the solution?

    More than five years.

    What do I think about the stability of the solution?

    Our system is very stable. In the two years I have administered Vertica at this job, I have had 100% uptime outside of planned outages for upgrades and hotfix applications.

    What do I think about the scalability of the solution?

    No issues. Amazingly scalable.

    Adding one node was very easy, as was adding memory to all nodes. We are currently in the process of setting up a Dev / DR environment which is going very smoothly.

    How are customer service and technical support?

    Customer Service:

    I have a great relationship with Vertica customer support. They are friendly, knowledgeable, and are quick to respond.

    Technical Support:

    HPE Professional Services have also been a huge help to us when needed. They are well worth the investment.

    It is extremely rare that I ever have an issue with Technical Support. My requests are always given a very quick initial response. Almost always get rapid feedback on my issues, and immediate escalation to the appropriate engineering team, either upon request or when the first level support rep needs additional insights on their own. On rare occasion, I have gotten a rep who is likely newer and almost reading off the script, but I am always able to give them enough info upfront so they avoid most of that, and they accommodate my escalation requests, if necessary.

    Which solution did I use previously and why did I switch?

    No, not at this company.

    At my last company, we initially used Aster Data (now owned by Teradata). Once our database grew too large, it was unable to handle the number of transactions we were completing per day. Many queries on our largest table were taking from 20 minutes to over an hour to complete. Right out of the box, our longest queries went down to under a minute, most completing in a matter of seconds.

    How was the initial setup?

    The initial setup was straightforward. We used an HPE-affiliated vendor to purchase and properly set up the equipment, completed a PoC, and then we had HPE Professional Services assist with the transition from our old system to Vertica.

    Our Linux team loves it as one of the best installation packages. Initiate on one node, and the RPM propagates automatically to all other nodes.  

    What about the implementation team?

    We implemented through a vendor. I highly recommend using IIS, they are amazing.

    I do all business through IIS. Top notch vendor, they are not just a "call and send a quote" company. I have developed a great professional relationship with my reps over the last five years over two Vertica admin jobs. They come onsite, enable access to the highest levels of Vertica engineering and management when needed, and also have found us opportunities at many of Vertica/HPE/Micro Focus trusted partners, such as Docker and Ormuco.

    What's my experience with pricing, setup cost, and licensing?

    The pricing, based on raw TB of data stored, is fair and affordable. You can have multiple projections per table without incurring a cost beyond the initial data load. The fact that a Dev and a DR cluster are included in the license cost is a great value!

    Work with a vendor, if possible, and take advantage of more aggressive discounts at mid-fiscal year (April) and fiscal year-end (October).

    Which other solutions did I evaluate?

    We evaluated Vertica and Greenplum, and chose Vertica due to cost and a number of existing use cases that were nearly identical to ours.

    What other advice do I have?

    My only advice is to seriously consider using Vertica for your data warehouse needs. I have normally just gone with the flow and learned whatever tools our company chose. When we switched from Aster Data to Vertica, I made the initial recommendation to do so. I am so happy with this product that I am now an HP ASE Certified Vertica Administrator, and moved to a new job that is also using Vertica. I would not have changed jobs if I were not able to continue using this product. I am also recommending to management that we purchase HPE IDOL for our upcoming audio and video analytics needs. HPE Big Data Solutions is a great product suite, and I have bet my career on its future growth.

    I can't recommend Vertica highly enough. While no solution is perfect, Vertica offered the most right out-of-the-box, and continues to expand on its offerings with every release. I am looking forward to see what changes come as a result of the Micro Focus spin merger.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Lead Software Engineer - Theatrical Global at a marketing services firm with 1,001-5,000 employees
    Vendor
    The biggest performance improvements are for queries that have to analyze a large amount of historical data.

    Valuable Features:

    Fast query processing for historical data analytics. Write Optimized Store (WOS) continuous data loading without drastically impacting performance of OLAP queries. It's one of the few columnar databases that has the capability to provide near real time data delivery for analytics with minimal delay sourcing data from traditional databases or NoSQL data stores or any unstructured data sources.

    Improvements to My Organization:

    With traditional RDBMS historical data analysis or any complex queries took minutes to complete. With the addition of Vertica to handle big data queries, these reports are now returned in under 15 seconds. The biggest performance improvements obviously are for queries that have to analyze a large amount of historical data.

    Room for Improvement:

    Stability, scalability (3 node Community Edition) and backup/restore all need to be worked on. Without proper work load management and resource pool allocation, any batch/ETL or streaming jobs which refreshes data frequently will impair OLAP query performance.

    Use of Solution:

    We've been using the three node cluster for about one and a half years.

    Stability Issues:

    We had several incidents where SQL queries with UDF predicates would shutdown the cluster or sometimes a single node. We worked with HP support to get these things fixed with subsequent versions of Vertica.

    Scalability Issues:

    With the Community Edition we are restricted to three nodes. We have a lot of enterprise clients who stress our cluster to its limits. The only advice I would give to new adopters is that if you want superior performance and reliability you are better off going all-in with the enterprise edition and a large number of nodes; assuming you have a lot of clients who run queries concurrently.

    Initial Setup:

    Setup and administration are very easy. Vertica was designed to be operational with minimal Database Administrator effort.

    Other Solutions Considered:

    We evaluated various other solutions but we chose Vertica because its SQL implementation is very similar to PostgreSQL, and therefore it saved us lot of development time re-writing SQL queries. Vertica seems to be one of the few columnar database which can handle both ETL/Batch jobs and OLAP queries simultaneously. We stream data into Vertica from RDBMS frequently than what is typically recommended for Columnar databases.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user467523
    BI and Reporting Platform Teams and Tech leader at a computer software company with 1,001-5,000 employees
    Real User
    It's enabled us to develop our new reporting system which is used as a SaaS by our users. Greater query concurrency is needed.

    Valuable Features

    MPP

    Analytical functions

    HDFS Copy

    Resource management

    Improvements to My Organization

    It's enabled us to develop our new reporting system which is used as a SaaS by hundreds of users. We can also load massive amounts of data in seconds and query it with SLA for online dashboards.

    Room for Improvement

    • Active-Active clusters with online replication.
    • Greater query concurrency.
    • Better documentation/white papers as there arte lots of undocumented issues.

    Use of Solution

    I've used it for three to four years.

    Scalability Issues

    Rebalancing after adding nodes is an issue in terms of resources and especially locking of tables. It would be nice if this could be more transparent.

    Customer Service and Technical Support

    8/10

    Other Advice

    If your product has lots of concurrent queries this solution is not suitable for you, or you need to implement a cache layer.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user464844
    Big Data DBA & DevOps at a tech vendor with 51-200 employees
    Vendor
    It's fast and built for complex analytics queries with a large amount of data.

    What is most valuable?

    The speed of Vertica out of the box with the ability it has to perform complex analytics queries. In other databases, information will return in hours or even days while in Vertica it will be finished in minutes or even seconds. This is the best feature it has.

    How has it helped my organization?

    Vertica is in our core technology stack. We are serving reports and dashboard to clients from it. It's very important to us that it fulfills its function correctly and provides us with an advantage over our competitors.

    What needs improvement?

    The internal documentation. As a DBA, I really want to understand how its internals works. It needs to handle high concurrency short queries better as Vertica is not handling these well, and we have had to develop our own tool to help us with our dashboard.

    For how long have I used the solution?

    I've been using it for five years.

    What do I think about the stability of the solution?

    The only thing is that it is very sensitive to network glitches and every time it happens, a node will leave the cluster and we need to re-connect it. Apart from this, the stability is very good.

    What do I think about the scalability of the solution?

    It is very easy to scale Vertica as you need.

    How are customer service and technical support?

    It used to be 10/10 but now it's 8/10. Maybe it's because they were bought by HP which is a big company and the transition is hard.

    Which solution did I use previously and why did I switch?

    The last company I worked for shifted from Oracle to Vertica. For our BI queries it's a huge win. Vertica is much better than any other raw store as it is built exactly for complex analytic queries with a huge amount of data.

    How was the initial setup?

    The earlier version wasn't that good to deploy, but now it's pretty easy to install. If you follow the documentation, you will be OK.

    What about the implementation team?

    We implemented it in-house. Since it's different than a raw store database, you need to understand the architecture in order to get the most out of Vertica. This means that you will need to design you data model to suit the Vertica architecture otherwise you will get the same performance as the solution you're replacing, or worse. If your implementation is not complex you can just put it in and you will get out of the box improvements, but for complex ones you need to know how Vertica works and build the right design.

    What other advice do I have?

    It's a great tool but to get the most out of it you will have to design your models to fit it.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user450444
    BI Architect / Software Engineer at a tech services company with 51-200 employees
    Consultant
    We thought the Management Console was a nice feature, but it turns out it gives us insight on what is happening behind the scenes.

    What is most valuable?

    Speed of query response time for complicated queries on tables with billions of rows including joins on varchar columns. There is no limitation on which columns can be queried or joined on and we see query times in the milliseconds for a lot of queries that just won't return at all from other products.

    Ease of administration. The Management Console we thought was a nice to have turns out to give us insight on what is happening behind the scenes so easily it has sped up query tuning, insight as to what jobs are running, and resource use on the boxes the product sits on.

    Style of deployment. We were able to build out a server farm exactly as we are accustomed to. We did not have to buy fancy hardware. Our first cluster was deployed on servers we had sitting around from other migrations and replaced products. As we grow also the growth is native to how we do business.

    How has it helped my organization?

    We can have insight into data we never had before. We can provide that insight to internal users so we do not have to generate reports for them all the time. With response times like these there is no concern of having them wait for results to return and so they do not think things are broken.

    What needs improvement?

    Getting the Management Console up and running as expected was a bit of a challenge.

    For how long have I used the solution?

    We've been using it for one and a half years.

    What do I think about the stability of the solution?

    We have amazing stability. We even had to migrate the databases to other boxes and found it moved the data without much intervention from us and no down time. It worked exactly as a cluster should. We joke here all the time that we would love to say we like Vertica support but since we never need them, we actually do not know!

    What do I think about the scalability of the solution?

    Scalability is one of the huge strengths of this product, and scalable in a way, as I said before, that is native to how we do business.

    How are customer service and technical support?

    We've never had to contact them.

    Which solution did I use previously and why did I switch?

    We switched off of Infobright because it was not performant at all at the scale we needed. The number of limitations on Infobright are too many to list in a small review like this.

    How was the initial setup?

    Initial setup of the database was straightforward.

    What about the implementation team?

    We did need support though for the initial installation. They were incredibly responsive and helpful and deployment was completed in a very reasonable amount of time despite issues initially getting the Management Console up and running.

    What's my experience with pricing, setup cost, and licensing?

    Pricing is more than fair. This is very reasonably priced and since it is a perpetual license you are not stuck paying it again and again.

    Which other solutions did I evaluate?

    We evaluated Netezza and Teradata alongside Vertica.

    What other advice do I have?

    Do you want to stand up a data warehouse in a reasonable amount of time using the in-house skills accustomed to dealing with an RDBMS? If that is the case, nothing beats Vertica, hands down.

    Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
    it_user417525
    Software Engineer at a marketing services firm with 51-200 employees
    Vendor
    Having projections as a parallel for indexes in a simple MySQL helped keep our data access fast and optimized. More insight into what the product is doing would help debugging.

    What is most valuable?

    Having projections as a parallel for indexes in a simple MySQL helped keep our data access fast and optimized.

    How has it helped my organization?

    This product has enabled us to keep very large amounts of data at hand for fast querying. With enough hardware force behind it, we were able to use Vertica as our primary reporting database without having to aggregate data, thus enabling us to provide many reports without having duplicated data or large aggregation steps.

    What needs improvement?

    We would like to see better documentation and examples as well as further simplicity in creating clusters, adding nodes, etc. I understand the GUI is very simple but sometimes more insight into what the product is doing and where errors are occurring would help debugging.

    For how long have I used the solution?

    We have used HP Vertica for three years.

    What was my experience with deployment of the solution?

    We have found multiple issues with deployment. Deployment was by far the hardest step in the process. We have very little knowledge of how to set up projects, how they affect query times, and how much additional storage they require.

    What do I think about the stability of the solution?

    We have had no stability issues.

    What do I think about the scalability of the solution?

    Scalability was a problem given we had to host the solution ourselves. It would be great to have a cloud-based solution around Vertica. Also, we found it difficult to modify and update our schema as we grew. Part of the problem may have been that when we first started using Vertica we were inexperienced.

    How are customer service and technical support?

    We paid for technical support for one year but did not use it very much so we discontinued its use.

    Which solution did I use previously and why did I switch?

    Choosing Vertica was the first time we used a data warehouse solution for handling the large amounts of data we were starting to gather. Since then, we have switched from an internally hosted Vertica to Spark managed externally.

    How was the initial setup?

    The initial setup was complex.

    What about the implementation team?

    We implemented it in-house. I would advise anyone to use a vendor unless you have an in-house expert.

    What was our ROI?

    I do not have an ROI. It is fair to say that we could not have provided our product to customers without Vertica.

    What's my experience with pricing, setup cost, and licensing?

    I found paying for the amount of storage we used simple. It was a surprise because we underestimated how much storage projections use and definitely did not purchase the correct license for the amount of data we estimated we would be handling.

    What other advice do I have?

    The product is great to use, but there is a steep learning curve initially. Also, we found limited resources for basic operations such as setup and deployment. Most tutorials and documentation were regarding how to run queries and use external tools such as Pentaho, which we weren’t using. We just wanted good explanations of how to optimize using projections, etc. I think it can be a great product if used correctly and implemented by a team who is familiar with the product.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user427470
    Technical Team Lead, Business Intelligence at a tech company with 501-1,000 employees
    Vendor
    The most valuable feature is the merge function, which is essentially the upsert function. We've had issues with query time taking longer than expected for our volume of data.

    What is most valuable?

    The most valuable feature is the merge function, which is essentially the upsert function. It's become our ELT pattern. Previously, when we used the ETL tool to manage upserts, the load time was significantly longer. The merge function load time is pretty much flat relative to the volume of records processed.

    How has it helped my organization?

    HP Vertica has helped us democratize data, making it available to users across the organization.

    What needs improvement?

    We've had issues with query time taking longer than expected for our volume of data. However, this is due to not understanding the characteristics of the database and how to better tune its performance.

    For how long have I used the solution?

    We've been using HP Vertica for three years, but only in the last year have we really started to leverage it more. We're moving to a clustered environment to support the scale out of our data warehouse.

    We use it as the database for the our data warehouse. In it's current configuration, we use it as a single node, but we're moving to a clustered environment, which is what the vendor recommends.

    What was my experience with deployment of the solution?

    We had no issues with the deployment.

    What do I think about the stability of the solution?

    We've had no issues with the stability.

    What do I think about the scalability of the solution?

    We've had no issues scaling it.

    How are customer service and technical support?

    I'd rate technical support as low to average. The tech support provides the usual canned response. We've had to learn most of how to harness the tool on our own.

    Which solution did I use previously and why did I switch?

    I haven't used anything similar.

    How was the initial setup?

    HP Vertica was in place when I joined the company, but it wasn't used as extensively as it is now.

    What about the implementation team?

    We implemented it in-house, I believe.

    What other advice do I have?

    Loading into HP Vertica is straightforward, similar to other data warehouse appliance databases such as Netezza. However, tuning it for querying requires a lot more thought. It uses projections that are similar to indexes. Knowing how to properly use projections does take time. One thing to be mindful of with columnar databases is that the fewer the columns in your query, the faster the performance. The number of rows impacts query time less.

    My advice would be to try out the database connecting to your ETL tools and perform time studies on the load and query times. It's a good database. It works similar to Netezza from my experience but it is a lot cheaper. Pricing is based on the size of the database.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Big Data, Analytics and Hadoop Expert, Vertica DBA (Technical Leader), Architecture Group at a tech vendor with 5,001-10,000 employees
    MSP
    Simple setup and responsive support.

    Valuable Features

    Ability to get top performance for in-advance known aggregative SQL queries.

    Improvements to My Organization

    HP Vertica is an outstanding backend for Big Data-scale interactive dashboards/BI. Achieving top performance however requires a deep understanding of the product architecture and experience in fine tuning of Vertica.

    Room for Improvement

    I really would like to see Vertica able to use heterogeneous storage (RAM, SSD, HDD). Another issue I have seen is that the SQL optimizer fails to make optimizations that competing products are able to do. That’s something that should be improved as well.

    Use of Solution

    I've been using it for two years.

    Deployment Issues

    We have had no issues with deployment.

    Stability Issues

    They should provide HA with Vertica, the cluster must be put behind Load Balancers.

    Scalability Issues

    There have been no issues scaling it for our needs.

    Customer Service and Technical Support

    I have no complaints, the HP guys were very responsive.

    Initial Setup

    The initial Vertica setup was really simple.

    Implementation Team

    In-house. The vendor team had many persons working on our project and we got an impression that it is difficult for them to focus on our requirements.

    Other Solutions Considered

    I have evaluated numerous competing products. HP Vertica was chosen for the top performance of aggregative queries.

    Other Advice

    It is very easy to start using Vertica, however getting the maximum performance from it is a fine art.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Architect with 501-1,000 employees
    Vendor
    You don’t have to worry about “load time slots” since you can load data into reporting tables at all times without worrying about their query load.

    What is most valuable?

    It provides very fast query performance after good designs of projections.

    It's easy to implement for 24/7 data load and usage because you don’t have to worry about “load time slots” since you can load data into reporting tables at all times without worrying about their query load.

    It just keeps up and running all the time.

    How has it helped my organization?

    We have been able to move from nightly batch loads to continuous data flow and usage. This hasn’t happened just because of Vertica, we have renewed our data platform pretty thoroughly, but definitely Vertica is one major part of our new data platform.

    What needs improvement?

    We are running our data transformations as an ELT process inside Vertica; we have data at least on the landing area, temporary staging area, and final data model. Data transformations require lots of deletes and updates (which are actually delete/insets in Vertica). Delete in Vertica doesn’t actually delete data from tables, it just marks them as deleted. For us to keep the performance up, purge procedures are needed and a good delete strategy needs to be designed and implemented. This can be time consuming and is a hard task to complete, so more ‘out-of-the-box’ delete strategies would be a nice improvement.

    For how long have I used the solution?

    We've been using it since January 2015.

    What was my experience with deployment of the solution?

    We haven't had any issues with the deployment.

    What do I think about the stability of the solution?

    Stability is good, however the database crashed once because a query ran against a large XML data element.

    What do I think about the scalability of the solution?

    We haven’t yet scaled out our system. So far performance has been good (taking into consideration that delete strategy mentioned in the Areas for Improvement question).

    How are customer service and technical support?

    We haven’t needed tech support too much. So far so good.

    Which solution did I use previously and why did I switch?

    We used Oracle for our DWH. When selecting a new database, we evaluated -- based both on written documentation and hands-on experimenting -- quite a lot of databases, such as Exadata, Teradata, and IBM Netezza. We selected HP Vertica as it runs on bulk hardware since it has “open interfaces”. It performed really well during hands-on experimenting and its “theories in practice” is good. Performance is excellent, development is easy (however, you need to re-think some things that you may have gotten used to when using other SQL databases), and its license model is simple.

    How was the initial setup?

    It seemed to be very straightforward. However, we had an experienced consult to do the setup.

    What about the implementation team?

    We had a joint team consisting of both an in-house team and external consultants. It’s very important to build up the internal knowledge by participating in actual project work.

    What was our ROI?

    We have ran so little time in production that we don’t yet have a decent ROI or other calculations done.

    What's my experience with pricing, setup cost, and licensing?

    The license model of HP Vertica is simple and transparent.

    What other advice do I have?

    Just go for it and try it out; you can download the free Community edition from the HP Vertica website.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    CTO at a tech services company with 51-200 employees
    Consultant
    Top 5
    A Good Option for Big Data

    Valuable Features

    Easy Installation, Easy to add and quit nodes...

    Improvements to My Organization

    SQLs querys 10 to 1 more fast that another commercial databases

    Use of Solution

    1 Year

    Deployment Issues

    Vertica support only SQL ANSI 99

    Stability Issues

    None

    Scalability Issues

    None

    Customer Service and Technical Support

    Customer Service: 10/10Technical Support: 5/10

    Initial Setup

    Easy

    ROI

    30%

    Valuable Features

    Easy Installation, Easy to add and quit nodes...

    Improvements to My Organization

    SQLs querys 10 to 1 more fast that another commercial databases

    Use of Solution

    1 Year

    Deployment Issues

    Vertica support only SQL ANSI 99

    Stability Issues

    None

    Scalability Issues

    None

    Customer Service and Technical Support

    Customer Service: 10/10Technical Support: 5/10

    Initial Setup

    Easy

    ROI

    30%
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    ITCS user
    Chief Datamonger at a media company with 51-200 employees
    Vendor
    100,000x faster: gnarly queries reduced from 22 hours to 800 milliseconds
    Part I: The Pilot A/B testing is part of our company’s DNA; we test every change to our platform and games. When we were small, this was easy, but as we grew into the tens and hundreds of millions of users, query speed ground to a halt. (Familiar story, right?) So in 2011 we piloted Vertica for our A/B testing suite. Our nastiest query used to take up to 22 hours to run on [name of old vendor - but don't want to mention them and be mean]. On Vertica, it ran in… 800 ms. That’s right, a scan and aggregation of over 100 billion records could be done in under one second. We were hooked! Part II: The Rollout Yeah we rolled it out. Boring. No interesting story here. Part III: The Impact Not having to worry about speed or data volume changes you. Suddenly we began logging and reporting on…

    Part I: The Pilot

    A/B testing is part of our company’s DNA; we test every change to our platform and games. When we were small, this was easy, but as we grew into the tens and hundreds of millions of users, query speed ground to a halt. (Familiar story, right?)

    So in 2011 we piloted Vertica for our A/B testing suite. Our nastiest query used to take up to 22 hours to run on [name of old vendor - but don't want to mention them and be mean]. On Vertica, it ran in… 800 ms. That’s right, a scan and aggregation of over 100 billion records could be done in under one second. We were hooked!

    Part II: The Rollout

    Yeah we rolled it out. Boring. No interesting story here.

    Part III: The Impact

    Not having to worry about speed or data volume changes you. Suddenly we began logging and reporting on everything. Where did users click? How long between clicks? How long does it take to type in a credit card number when you’re ready to pay? How much free memory does an iPad 1 have, and how does that change every second?

    Like all software engineers, we solve problems under constraints, and we had conditioned ourselves to think of logged data volume as a constraint. Suddenly that was no longer a constraint, but I would say it took us a full year to fully appreciate how powerful that was.

    Part IV: Today

    Today we record every customer interaction with our games and platforms – on phones, tablets, Facebook, and the web. Every department at the company consumes this data.

    Marketing: Monitor ad campaigns in realtime, and throttle campaigns up/down based on performance of the users who are acquired via those campaigns.

    Game design: Monitor game difficulty and tune in realtime.

    Operations: Monitor for changes in customer service volume, exception logging, etc.

    Creative services: Test different artwork and themes and monitor impact on game KPIs

    Finance: How much money did we make in the last 60 seconds? (Bonus tip: finance gets very happy when they see this, and a happy finance department makes for a happy company. Me: “Hey Bob, can I buy an Oculus Rift for my team to play with?” Bob: “Hold on let me check the reports… whoopee! Sure thing, request approved”.)

    Part V: Conclusion

    We love speed, unlimited data, and Vertica!

    Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
    ITCS user
    Architect at a tech services company with 51-200 employees
    Consultant
    Vertica allows for thousands of users to run an analysis at the same time. Great aggressive compression.
    At the tech company I work , we were looking for new ways to allow end users (a couple of thousand external users)  to crunch through their detailed data in real time as well as enabling internal users and data analysts to gain the information they needed to run and optimize their business processes.  Unfortunately our current system had  become slower and slower over time due to the tremendous increase in data to be managed so a new approach had to be taken to accomplish this goal.  Our existing data warehouse/data management infrastructure just could not handle big data. We evaluated a variety of different solutions such as Amazon Redshift, Infobright and Microsoft. Vertica won out above all these other solutions. Our dataset is several hundred million rows and our avg. response time…

    At the tech company I work , we were looking for new ways to allow end users (a couple of thousand external users)  to crunch through their detailed data in real time as well as enabling internal users and data analysts to gain the information they needed to run and optimize their business processes. 

    Unfortunately our current system had  become slower and slower over time due to the tremendous increase in data to be managed so a new approach had to be taken to accomplish this goal.  Our existing data warehouse/data management infrastructure just could not handle big data.

    We evaluated a variety of different solutions such as Amazon Redshift, Infobright and Microsoft. Vertica won out above all these other solutions. Our dataset is several hundred million rows and our avg. response time goal was less than 5 secs. We are building our environment for the future so another requirement was to be able to scale horizontally. 

    Redshift came close in response time but failed in concurrency, meaning multiple users running an analysis at the same time. Infobright came close in response time and concurrency but didn’t provide sufficient scalability. Vertica checked all boxes at a very competitive price-point.

    We found that the extreme speed, performance and flexibility is superior to all the other solutions out there. The massive scalability on industry-standard hardware, standard SQL interface and database designer and administration tools are excellent features of Vertica. I also really value the simplicity, concurrency for hundreds or thousands of users, and aggressive compression.

    This new environment allowed us to implement applications such as clickstream and predictive analysis which have added tremendous value for us. Currently there is about 500 GB – 1 TB of data that I am managing and I have found that Vertica is able to be integrated very well with a variety of Business Intelligence (BI), visualization, and ETL tools in their environment. I use Hadoop, Tableau and Birst and using all these solutions with Vertica has been overall quite smooth.

    Our query performance has increased by 500 – 1,000% through improvements in response time and I am now able to compress our data by more than 50%. The simultaneous loading and querying and aggressive compression has helped us become more efficient and productive. Furthermore the high availability without hardware redundancy, optimizer and execution engine, and high availability for analytics systems has saved us both time and money. 

    Disclosure: IT Central Station has made contact with the reviewer to validate that the person is a real user. The information in the posting is based upon a vendor-supplied case study, but the reviewer has confirmed the content's accuracy.
    it_user99918
    Chief Data Scientist at a tech vendor with 10,001+ employees
    Vendor
    We're using Vertica, just because of the performance benefits. On big queries, we're getting sub-10 second latencies.
    My company recognized early, near the inception of the product, that if we were able to collect enough operational data about how our products are performing in the field, get it back home and analyze it, we'd be able to dramatically reduce support costs. Also, we can create a feedback loop that allows engineering to improve the product very quickly, according to the demands that are being placed on the product in the field. Looking at it from that perspective, to get it right, you need to do it from the inception of the product. If you take a look at how much data we get back for every array we sell in the field, we could be receiving anywhere from 10,000 to 100,000 data points per minute from each array. Then, we bring those back home, we put them into a database, and we run…

    My company recognized early, near the inception of the product, that if we were able to collect enough operational data about how our products are performing in the field, get it back home and analyze it, we'd be able to dramatically reduce support costs. Also, we can create a feedback loop that allows engineering to improve the product very quickly, according to the demands that are being placed on the product in the field.

    Looking at it from that perspective, to get it right, you need to do it from the inception of the product. If you take a look at how much data we get back for every array we sell in the field, we could be receiving anywhere from 10,000 to 100,000 data points per minute from each array. Then, we bring those back home, we put them into a database, and we run a lot of intensive analytics on those data.

    Once you're doing that, you realize that as soon as you do something, you have this data you're starting to leverage. You're making support recommendations and so on, but then you realize you could do a lot more with it. We can do dynamic cache sizing. We can figure out how much cache a customer needs based on an analysis of their real workloads.

    We found that big data is really paying off for us. We want to continue to increase how much it's paying off for us, but to do that we need to be able to do bigger queries faster. We have a team of data scientists and we don't want them sitting here twiddling their thumbs. That’s what brought us to Vertica.

    We have a very tight feedback loop. In one release we put out, we may make some changes in the way certain things happen on the back end, for example, the way NVRAM is drained. There are some very particular details around that, and we can observe very quickly how that performs under different workloads. We can make tweaks and do a lot of tuning.

    Without the kind of data we have, we might have to have multiple cases being opened on performance in the field and escalations, looking at cores, and then simulating things in the lab.

    It's a very labor-intensive, slow process with very little data to base the decision on. When you bring home operational data from all your products in the field, you're now talking about being able to figure out in near real-time the distribution of workloads in the field and how people access their storage. I think we have a better understanding of the way storage works in the real world than any other storage vendor, simply because we have the data.

    I don’t remember the exact year, but it may have been eight years ago roughly that I became aware of Vertica. At some point, there was an announcement that Mike Stonebraker was involved in a group that was going to productize the C-Store Database, which was sort of an academic experiment at UC Berkeley, to understand the benefits and capabilities of real column store.

    I was immediately interested and contacted them. I was working at another storage company at the time. I had a 20 terabyte (TB) data warehouse, which at the time was one of the largest Oracle on Linux data warehouses in the world.

    They didn't want to touch that opportunity just yet, because they were just starting out in alpha mode. I hooked up with them again a few years later, when I was CTO at a different company, where we developed what's substantially an extract, transform, and load (ETL) platform.

    By then, they were well along the road. They had a great product and it was solid. So we tried it out, and I have to tell you, I fell in love with Vertica because of the performance benefits that it provided.

    When you start thinking about collecting as many different data points as we like to collect, you have to recognize that you’re going to end up with a couple choices on a row store. Either you're going to have very narrow tables and a lot of them or else you're going to be wasting a lot of I/O overhead, retrieving entire rows where you just need a couple fields.

    That was what piqued my interest at first. But as I began to use it more and more, I realized that the performance benefits you could gain by using Vertica properly were another order of magnitude beyond what you would expect just with the column-store efficiency.

    That's because of certain features that Vertica allows, such as something called pre-join projections. At a high-level, it lets you maintain the normalized logical integrity of your schema, while having under the hood, an optimized denormalized query performance physically on disk.

    Can you be efficient if you have a denormalized structure on disk because Vertica allows you to do some very efficient types of encoding on your data. So all of the low cardinality columns that would have been wasting space in a row store end up taking almost no space at all.

    It's been my impression, that Vertica is the data warehouse that you would have wanted to have built 10 or 20 years ago, but nobody had done it yet.

    Nowadays, when I'm evaluating other big data platforms, I always have to look at it from the perspective of it's great, we can get some parallelism here, and there are certain operations that we can do that might be difficult on other platforms, but I always have to compare it to Vertica. Frankly, I always find that Vertica comes out on top in terms of features, performance, and usability.

    I built the environment at my current company from the ground up. When I got here, there were roughly 30 people. It's a very small company. We started with Postgres. We started with something free. We didn’t want to have a large budget dedicated to the backing infrastructure just yet. We weren’t ready to monetize it yet.

    So, we started on Postgres and we've scaled up now to the point where we have about 100 TBs on Postgres. We get decent performance out of the database for the things that we absolutely need to do, which are micro-batch updates and transactional activity. We get that performance because the database lives here.

    I don't know what the largest unsharded Postgres instance is in the world, but I feel like I have one of them. It's a challenge to manage and leverage. Now, we've gotten to the point where we're really enjoying doing larger queries. We really want to understand the entire installed base of how we want to do analyses that extend across the entire base.

    We want to understand the lifecycle of a volume. We want to understand how it grows, how it lives, what its performance characteristics are, and then how gradually it falls into senescence when people stop using it. It turns out there is a lot of really rich information that we now have access to to understand storage lifecycles in a way I don't think was possible before.

    But to do that, we need to take our infrastructure to the next level. So we've been doing that and we've loaded a large number of our sensor data that’s the numerical data I have talked about into Vertica, started to compare the queries, and then started to use Vertica more and more for all the analysis we're doing.

    Internally, we're using Vertica, just because of the performance benefits. I can give you an example. We had a particular query, a particularly large query. It was to look at certain aspects of latency over a month across the entire installed base to understand a little bit about the distribution, depending on different factors, and so on.

    We ran that query in Postgres, and depending on how busy the server was, it took anywhere from 12 to 24 hours to run. On Vertica, to run the same query on the same data takes anywhere from three to seven seconds.

    I anticipated that because we were aware upfront of the benefits we'd be getting. I've seen it before. We knew how to structure our projections to get that kind of performance. We knew what kind of infrastructure we'd need under it. I'm really excited. We're getting exactly what we wanted and better.

    This is only a three node cluster. Look at the performance we're getting. On the smaller queries, we're getting sub-second latencies. On the big ones, we're getting sub-10 second latencies. It's absolutely amazing. It's game changing.

    People can sit at their desktops now, manipulate data, come up with new ideas and iterate without having to run a batch and go home. It's adramatic productivity increase. Data scientists tend to be fairly impatient. They're highly paid people, and you don’t want them sitting at their desk waiting to get an answer out of the database. It's not the best use of their time.

    When it comes to the cloud model for deployment, there's the ease of adding nodes without downtime, the fact that you can create a K-safe cluster. If my cluster is 16 nodes wide now, and I want two nodes redundancy, it's very similar to RAID. You can specify that, and the database will take care of that for you. You don’t have to worry about the database going down and losing data as a result of the node failure every time or two.

    I love the fact that you don’t have to pay extra for that. If I want to put more cores or nodes on it or I want to put more redundancy into my design, I can do that without paying more for it. Wow! That’s kind of revolutionary in itself.

    It's great to see a database company incented to give you great performance. They're incented to help you work better with more nodes and more cores. They don't have to worry about people not being able to pay the additional license fees to deploy more resources. In that sense, it's great.

    We have our own private cloud -- that’s how I like to think of it -- at an offsite colocation facility. We do DR here. At the same time, we have a K-safe cluster. We had a hardware glitch on one of the nodes last week, and the other two nodes stayed up, served data, and everything was fine.


    Those kinds of features are critical, and that ability to be flexible and expand is critical for someone who is trying to build a large cloud infrastructure, because you're never going to know in advance exactly how much you're going to need.

    If you do your job right as a cloud provider, people just want more and more and more. You want to get them hooked and you want to get them enjoying the experience. Vertica lets you do that.

    Disclosure: IT Central Station has made contact with the reviewer to validate that the person is a real user. The information in the posting is based upon a vendor-supplied case study, but the reviewer has confirmed the content's accuracy.
    ITCS user
    Senior Product Manager (Data Infrastructure) and Security Researcher at a tech company
    Vendor
    Great Platform
    If you are in the topic of Databases, you should know who is Dr. Michael Stonebraker, who is right now an adjunct professor in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, considered like one of the world experts in this field. Why I began in that way? Because Dr. Stonebraker co-founded Vertica Systems, seeing the innovation behind this amazing product. But, What is Vertica? Vertica Analytic Database is a high performance MPP (Massive Parallel Processing) columnar engine optimized to deliver faster query results in the shortest time. I said optimized because this is a keyword inside the Vertica team: every piece of code in Vertica has a lot of research and innovation, which I will discuss later. I heard abut this database…

    If you are in the topic of Databases, you should know who is Dr. Michael Stonebraker, who is right now an adjunct professor in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, considered like one of the world experts in this field. Why I began in that way? Because Dr. Stonebraker co-founded Vertica Systems, seeing the innovation behind this amazing product.

    But, What is Vertica?

    Vertica Analytic Database is a high performance MPP (Massive Parallel Processing) columnar engine optimized to deliver faster query results in the shortest time. I said optimized because this is a keyword inside the Vertica team: every piece of code in Vertica has a lot of research and innovation, which I will discuss later. I heard abut this database when I was writing a research paper for my organization about MPP systems, and I found that Vertica was one of the good players in this Big Data Analytics game (the other good players are the Greenplum Database and Teradata’s Aster Data Platform). Then, HP saw the great opportunity that this product represented for the Big Data business and acquired the company in 2011.

    OK, let’s talk now about some of the Vertica’s features

    • Column-based storage:

      Vertica use a patented architecture called FlexStoreTM, created based on three principles: the grouping of multiples columns in a single file, the selection of disk storage format based on data load patterns automatically, and the ability to differentiate storage media by their performance characteristics and to enable intelligent placement of data based on usage patterns

    • Advanced Data compression:

      Based on the choosed architecture by Vertica team of grouping columns in a single file; the data compression follows the same principle: Vertica organizes values of similar data types contiguously in memory and on disk, enabling to select the best compression algorithm depending of the data type. This improves dramatically the query execution and parallel load times

    • Built-in Analytics functions:

      Vertica comes with a completed packages of useful functions for Analytics, divided by topics like Natural Language Processing, Data Mining, Logistic Regression, etc. This is called User-Defined Extensions. You can read more about this here in this whitepaper

    • Automatic High Availability:

      Vertica allows to scale your data almost without limits, with remarkable features like automatic failover and redundancy, fast recovery, and fast query performance, executing queries 50x-1000x faster eliminating costly disk I/O.

    • Native integration with Hadoop, BI and ETL tools:

      Seamless integration with a robust and ever growing ecosystem of analytics solutions.

    You can read deeply about all these features here. The last version of the platform is Vertica 6, and here you can find some of the new features and improvements in this version, or you can view this video, where Luis Maldonado, Director of Product Management at Vertica, explaining a quick overview of this version.

    Ok, it’s a great platform, but who are using it today?

    There are a lot of companies that are trusting in Vertica today: Twitter, Zynga (th number # 1 company in the Social Gaming industry), Groupon, JPMorgan Chase, Mozilla, AT&T, Verizon, Diio, Capital IQ, Guess Inc,and many more. Read its testimonials here.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user4518
    Head of Databases at a retailer with 501-1,000 employees
    Vendor
    Great DW value for the money, but needs better workload management

    Valuable Features:

    • As it can load a lot of data very quickly, it is very helpful for our organization in managing our VLDB. • It has the lowest cost per TB compared to other DW solutions. • Provides us clustering for our commodity hardware and a good data compression ratio compared to other DW tools I've looked into. • It is very scalable. We didn’t face any problems in terms of installation. • Allows concurrent users access. Our staff can process and send query requests simultaneously -- it responds to all requests very efficiently. • It is very easy to use! It also is good in providing OLAP support to our database system. We can also use traditional SQL queries and tables.

    Room for Improvement:

    • We can’t use vertica as a transactional database because it is unable to handle lot of transactions. • While copying tables straight across from MYSQL, we observe poor performance. Queries take more time for execution. • Our team is challenged during peak loads because of a lack of work load management options. • It doesn’t offer us any configuration options for reducing the size of our database system and limiting our resource usage.

    Other Advice:

    We are using Vertica because it is a fast and reliable data warehousing tool and provides us a very good data compression ratio. It is cheap in terms of overall cost per TB and also requires less human resource for maintenance and tuning. Vertica I have been told has improved cloud deployment capabilities on any infrastructure.
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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