Vertica Reviews

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.

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

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.

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.

We had no issues with the deployment.

We've had no issues with the stability.

We've had no issues scaling it.

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.

I haven't used anything similar.

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

We implemented it in-house, I believe.

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.

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

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

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

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.

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.

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.

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.

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

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

We have used this solution for three years.

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

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

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

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

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

We evaluated SQL Server and Teradata.

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

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

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.

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

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.

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.

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.

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

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.

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.

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.

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.

I have used it for two years.

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

I have not encountered any scalability issues.

Technical support is very good and knowledgeable.

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

Initial setup was straightforward through the command line.

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.

Before choosing this product, we evaluated Netezza and ParAccel.

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.

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.

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.

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.

It's very stable.

We had no issues deploying it.

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.

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.

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

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.

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.

We have used HP Vertica for three years.

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.

We have had no stability issues.

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.

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

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.

The initial setup was complex.

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

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

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.

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.