Vertica Reviews

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.

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.

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.

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

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.

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.

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

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.

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.

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

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

There have been no issues with the deployment.

There have been no issues with the stability.

We have had no issues scaling it for our needs.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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

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!

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.

We've never had to contact them.

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.

Initial setup of the database was straightforward.

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.

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.

We evaluated Netezza and Teradata alongside Vertica.

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.

The primary use case is machine learning and currently, I am working on IOT projects.

With Vertica, I am able to make changes using other Vertica features and I do not have to start the project over. The Vertica tool is very powerful but you cannot purchase the product based on individual features. 

I am highly trained on Vertica and I am resistant to using other products because I do not have experience with those products. Some of the most valuable features are cybersecurity and backup.

The biggest problem is the cost of cloud deployment.

I have worked with Vertica for the past five years.

The initial setup is straightforward and easy deployment.

I would rate Vertica an eight out of ten.

I use Vertica for traditional data warehouse reporting and some advanced analytics as well as real-time data processing or real-time analytics. I sell Vertica, so my clients' use cases vary. There are several types of users, and each company is going to have different requirements. And the number of users is not necessarily a great measure of usage if you're doing clickstream-type data on a website with massive numbers of queries coming in very quickly. But in other cases, you have people hammering it for reporting and various kinds of business use cases.

Vertica has a few features that I like. From an architecture standpoint, they have separated compute and storage. So you have low-cost object storage for primary storage and the ability to have several sub-clusters working off the same ObjectStore. So it provides workload isolation. Vertica handles analytics and large queries extremely well — very high performance —and on top of all of these features, Vertica has a very low total cost ownership.

Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible. I know that's in their roadmap, but I can't wait.

I've been using Vertica for almost three years.

Vertica is rock-solid.

Vertica is massively scalable. My customers' workloads vary. While the number of users is not necessarily the ideal performance measure, the user bases range from a few dozen in smaller setups to thousands in the largest setups. And some of my customers have several hundred users on the platform concurrently.

I deal directly with Vertica's engineers, so I don't have much experience with their frontline tech support. However, the more senior tech support on the backline is just world-class. These guys are amazing. And they are exceedingly committed to their customers. I've seen them do crazy stuff to support a customer.

Setting up Vertica is pretty straightforward. There's nothing super fancy about deploying Vertica. Depending on what you're doing, deployment can take anywhere from 10 to 15 minutes to several hours. It's quick if you want to use the automatic tools, which work very well. And if you want to do your own thing or do it on-prem, then it's going to take you a little bit longer. And you don't need large teams to administer it afterward.

I rate Vertica 10 out of 10. Anyone thinking about using Vertica should try to understand more than its basic functionality. They should look at its more powerful features like advanced, predictive, and real-time analytics. And just to add, that in the world of data warehousing, it's more accurate to say "near real-time" rather than "real-time" analytics. 

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

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

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.

One year.

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.

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

N/A, not used.

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

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.

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.

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.

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