OpenText Analytics Database (Vertica) Reviews

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. 

Our use case is a typical data warehouse. We just use the data warehouse for reporting and the storage of data. Our users are the staff team who do the reporting and data analysis.

The feature I like best is performance. We use Red Tool and Red Job for the data warehouse and reporting. It's perfect. Performance is good, and it can return ad hoc queries very quickly. Of course, it's a cluster, so it's easy to scale.

It's hard to make it slow for a small data volume. For large volumes, it's hard to make it work. It's also hard to make it faster, and to make it scale.

I have been using Vertica for about five years.

It's a stable solution.

Vertica is a scalable solution.

Technical support is good, and they react quickly.

The initial setup is okay. You will need some knowledge and some training. I'd say learning takes a couple of months. We use one person to maintain the database side. With the DevOps team, everyone has a different role. But for our database, it's just one person.

The price is reasonable. We use a pay per license model. Firstly, you need to buy a license. After that, you mainly pay the annual support fee of around 20% or 25%. I think their prices are quite reasonable.

We tried to use data lake kind of stuff for machine learning, but for the key functionality of the data warehouse, it's great. Personally, I feel they are over-marketing the machine learning feature and for something like the semi-structured data. But for the data warehouse, it's truly a good solution. I want to recommend it highly.

I would tell potential users that it's hard to make it slow for small data volumes. For large volumes, it's hard to make it work, make it faster, and make it scale. Depending on your workload and your use case, you need to first purchase the Red Tool. After that, you need to follow the best practices to have an efficient design.

On a scale from one to ten, I would give Vertica a nine.

We are using Vertica in our warehouse.

I have found that coding support could be simplified.

I have been using this solution for approximately five years.

The solution is stable.

I have found the solution to be scalable.

I have used the local support and I prefer them.

The price could be cheaper and it is best to negotiate the price.

I rate Vertica an eight out of ten.

The solution is a BI solution that includes machine learning. Our company is involved in the distribution of water and we use it to capturing data for several points and to discover where there might have been a loss of drinkable water. 

There is a problem with the water distribution because the company that I'm working for has an index of 32% of water loss during the process of the distribution. These losses can be from a different source. It can be from leakage, it can be an error or on the meter read which can have many issues, sometimes the problem occurs in different hours depending on the pressure of the water network.  We need to use artificial intelligence to collect millions of the data points to detect where the problem might be coming from.

The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful. 

The product could be less expensive and could benefit from a better marketing strategy. 
In a future release, I would like to have one application to help create intelligent models.

We have been testing and developing the solution for two years.

We did not have any technical problem with the solution.

The solution has great scalability. We started with one terabyte of compressed data, this is a lot of data and we never had problems with the scalability. You can have hundreds of terabytes with the solution if you want, it all depends on your needs.

The customer service is very good. They could improve on their expertise and knowledge of bigger projects and their support for them. Some of their information about collecting data I was not satisfied with their help. They could improve on customer service a bit.

I rate the technical support an eight out of ten.

We currently use IDOL as well as Vertica.

The product is not easy to set up because you need a lot of training. It has less to do about the product itself, but the knowledge on how to use it. For example, the spreadsheet product Excel, If you don't know mathematics, you will have difficulty to make big Excel model and that's the same with the Vertica. It's just only a tool and depending on your capabilities to design what you need. 

We are the developers of a solution and it is a very sophisticated project. It's something that we spent two years to develop. We already tested it and we are only waiting for the customer to try it and then purchase it. It has taken some time to implement the solution to the way we wanted for our company.

It's difficult today to compete with open-source solutions. In these areas, there is a lot of competition and the price of this solution is a bit pricy.

We use another product called IDOL and use them both together as our solution. Sometimes you use both or sometimes you use each one separately. The two products are machine learning products but with different uses. IDOL has a more developed application and is much bigger than in Vertica. 

This solution is used by several big companies such as Bank of America, Uber, and Facebook. Where you need a BI with intelligence. We use the solution because it is very good, you can make interconnections with anything to collect the data, any type of data. I have tried other products and they did not fit as well as this one did, I recommend Vertica.

I rate Vertica a nine out of ten.

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.