Vertica Reviews

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.

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.

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.

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.

The compute and processing engine returns the queries fast and let us use our analysis resources in a better utilization.

The concurrency got better in this version and we are able to run more queries and load concurrently.

We built an internal dashboard using the MicroStrategyto increase visibility to our management and our employees. Also, we built tool to expose the data to our selected partners and users to create better engagement with our platform.

• Loading times for “real time” sources - for example, loading from Spark creates a load on the DB at high scale
• Connectors to other sources such as Kafka or AWS Kinesis
• Better monitoring tools
• Better integration with cloud providers - we were missing some documentation regarding running Vertica on AWS

We've been using Vertica for a year.

In case of one HD failure in the cluster, the entire cluster got slower. We feel that it should be able to handle such issues.

No.

The support was slow and didn’t provide a solution in most cases. The community proved to be the better source for knowledge and problem solving.

Pretty straightforward, the installation was simple and we added more nodes easily as we grew.

Vertica is pretty expensive, take into account the servers and network costs before committing.

We evaluated both AWS Redshift and Google BigQuery.

Redshift didn’t fulfill our expectations regarding query latency at high scale (over 60 TB). Regarding BigQuery, we found the pricing structure pretty complex (payment per query and data processed) and harder to control.

Don't plan a production usage on high-scale straight on Vertica, use caching or other buffers between the users and the DB. Get yourself familiar with the DB architecture before planing your model (specifically, make sure you know ROS/WOS and projections). Try to avoid LAP before your schema gets stabilized.

Analytic functions.

We are trying to data mine customer event data. Having the ability invoke analytic functions without having write self join SQL statements ... just brilliant.

Ability to use analytic functions in where clauses, being able to use aliases in the where and order by clauses will make query writing/reading a lot easier.

2 years.