OpenText Analytics Database (Vertica) Reviews

We are resellers and we provide products for our customers.

Our clients are using this solution in two ways; one is for a data warehouse, and the second is for analytics in the database.

The data warehouse has exceedingly high performance and has the ability to do large scale queries very effectively. It fits well in large enterprises.

All features are valuable. It's a combination of capabilities that's all in one place, which is incredibly powerful.

For me, it's performance, scalability, low cost, and it's integrated into enterprise and big data environments.

Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint. They don't currently offer this as a platform as a service. Snowflake is offering this capability.

They're available in the cloud. They're available on every cloud, but they're not available as a managed platform as a service offering.

We have been dealing with Vertica for two years. 

We use both version 9.3 and version 10. Version 10 is the latest one.

Vertica is a stable solution, it's rock solid. Production workloads being run on it are super steady. It's high availability, massively parallel processing. Nodes go down and you don't even notice.

This is a scalable solution. For example, if we look at Uber drivers, they are able to monitor the position and availability of the drivers and match that against the number of customers for every customer and every driver worldwide globally.

They do a geospatial analysis and calculate their search pricing. They are defined by geographical boundaries, they are defined by where the people are and where the drivers are. This is done for every city in the world for every driver. That gives you an idea of the scale they are able to do in this particular use case.

This gives you the idea of the scale, the performance, and the ability to do analytics in the database. They do this so much more cost-effectively than they would on any other platform.

Technical support is extremely good. They know the product and they're very good.
I don't have any complaints regarding technical support.

Vertica is known for its ease of administration. I would say that the initial setup is easier than most.

The price varies completely. Cost information is available publically where you can compare with other solutions.

From a cost perspective, the software is less than most of its competitors.

Customers save money by a smaller hardware footprint, fewer nodes, less storage, lower-cost storage, and no appliances. So it is typically a lot less money than an Oracle, Teradata, or Snowflake. 

Overall, they are highly competitive when it comes to pricing.

The customers love them. They absolutely love them.

Before implementing this solution, make sure that it is on the list and that you evaluate it.

I would rate Vertica a ten out of ten.

The primary use of Vertica is as a data warehouse to perform aggregate and summary reports.

This solution has allowed us to reduce the creation of summarized tables, as the user can perform queries on the fly. It has provided for the faster retrieval of records.

The performance is very good and the aggregate records are fast.

Vertica Auto-Projections reduce the knowledge required by the DBA for tuning performance.

When it is about to reach the maximum storage capacity, it becomes slow.

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.

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.

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

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

• 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.

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.

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

• 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.

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.

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.

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.

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.