OpenText Analytics Database (Vertica) Reviews

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.

Before we used Vertica we used another columnar database which turned out to be very unstable and its performance was inconsistent. Vertica turned that around, to the point that it is now our go-to database. We became Vertica partners.

Vertica is a columnar database, this support our developments in analytics, advanced analytics, and our ETL process with large sets of data.

I believe the installation process could be streamlined.

No stability issues.

No scalability issues.

They are very professional and responsive.

See "Improvements to organization," above.

There are some considerations to be evaluated before you start the installation, but the installer does the respective checks so things will function properly. And there are a lot of options.

The first TB is free and you can use all the Vertica features. After 1TB you have to pay for licensing. The product is worth it, but be aware of this condition, and plan. The compression ratio is explained in the documentation.

Infobright and MonetDB.

The technical requirements for the product are really important. The design tool for vertica is the core of the database for performance. Never forget to use it to create projections to optimize the storage compression and response times. Better compression means the 1TB mark takes longer to be reached.

Partition and join back to node are easy and simple for DBAs.

DBAs don’t need to add a partition every month/quarter like with other DBs.

There are a lot of limitations within this product and it makes things extremely hard for developers. It lacks Stored Procedure, packages, and triggers like other RDBMs.

Yes, we have encountered issues with Projections and performance.

Very bad support, I would rate it two out of 10.

We use DB2, Oracle , MySQL, MSSQL. We switched to Vertica to explore it for  future projects.

Easy setup. Much easier than setting up Oracle RAC.

Licensing is based on size of the database.

They did a good PoC and we were impressed with Vertica. However, when we implemented, it was nightmare with bad support.

My advice regarding this product is a definite "no", due to bad support.

It enabled delivery of a new Agile Data Warehousing Service.

It enabled us to close large deals. Customers with large data sets had to be migrated from PostgreSQL to Vertica due to performance.

• Clustered database
• Horizontal scaling
• Disaster recovery
• Columnar Storage
• Compression (you read only columns you need)
• Immutable storage
• Fast ingesting

Performance of management of metadata layer (database catalog) needs improvement. We still have to have smaller customers on PostgreSQL; Vertica cannot manage thousands of schemata.

Query performance: Improve either Database Designer (automation of projection design) or performance of queries using suboptimal projection design.

Scaling of execution independently on storage: Upcoming Eon Mode (now Beta in Amazon) will hopefully solves this.

Encountered stability issues three times during last three years.

Suboptimal projection design causes queries to not scale linearly.

The metadata layer does not scale linearly.

Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes.

I have experience with legacy vendors of enterprise RDBMS solutions, and I rate Vertica support to be much better.

In my current company I was not responsible for the switch. As far as I know, they switched from PostgreSQL, especially because of performance of analytical queries processing large data.

Just getting Vertica running is straightforward. However, with an increasing number of customers, we had to develop our own tooling. For example:

• Automated deployment
• Monitoring, alerting
• Backup/restore.

Start with license per 1TB. Starting from hundreds of TB there is unlimited licensing to be considered.

Move historical data to HDFS/S3 which are significantly cheaper or even free.

Vertica is delivering more and more features to support load/unload for external storages.

2012 - Detailed evaluation including benchmarks of: Greenplum, Vectorwise.

2017 - Evaluation of features and initial communication with vendors, if needed, for: Greenplum, EXASOL, Amazon Redshift, Spark, SAP HANA, IBM dashDB, Snowflake, Azure SQL.

It is easy to implement this solution for one customer. By tuning the model (projection design) you get incredible performance. You won’t face issues with metadata (catalog) layer up to tens of thousands of tables.

It can be a challenge to operate clusters for many customers with varied data pipelines. Consider using Database Designer.

Don't hesitate to push Vertica (through support/product management) to improve it.

Consider implementing your own tools to automate performance tuning tasks.

• I found the columnar storage, which increases performance of sequential record access, to be the most valuable feature. 
• I also like the projection feature, which increases query performance.

• The workload on our ETL tools were reduced. 
• All joint operations were enhanced by creating identically segmented projections.

Limitations in group by projections is where I would like to see an improvement. 

We have not had any issues with deployment.

We have not had any issues with stability.

We have been able to scale it for our needs.

It is a good database that can be used for ad hoc queries as well as analytical queries.