Vertica Reviews

• HA Clustering
• Speed / Performance

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

More frequent updates.

1 year

None.

None.

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

MonetDB -- unstable, frequent crashes.

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

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

MonetDB, Cassandra, Amazon RedShift.

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

Analytical features are amazing, the integration is wonderful.

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

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.

3 years

Yes, cluster migration takes time.

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

Not yet.

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.

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

In-house.

Yes, Netezza.

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

Scalability, query speed.

It is the foundation of our new Data Warehouse platform.

Data velocity and manageability.

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

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

No

No

Customer Service:

I have not been in contact with their customer service.

Technical Support:

I have not been in contact with their tech support.

We used Oracle but it did not scale well enough

It was straightforward.

I was not involved with the implementation.

Don't know.

The pricing is very flexible

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

It looks promising.

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

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.

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.

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

Three to five years.

There were no stability issues.

There were no scalability issues.

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

NA

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

NO

Read the fine print carefully.

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

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

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

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

3 years.

Not really.... Vertica shines on stability.

No, scalability is also a strength of the solution.

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

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.

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

Negotiate when their fiscal year is about to close :)

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'.

• 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.

• 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.

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.

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

• 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.

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

It needs integration with multiple clouds.

One to three years.

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

It has an elastic scalability solution.

It is easy to integrate with EC2 instances.

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.

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

I have not tried to integrate it with other products.

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

DWH core platform is based on it

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

3 years