#1 most frequent
United States of America
51.0%517 installations of 1,013
What is Vertica Analytics Database?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica was acquired and supported by OpenText, then sold to Rocket Software in 2026.
Categories & Use Cases
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Who Buys & Uses Vertica Analytics Database
#2 most frequent
India
5.3%54 installations of 1,013
#3 most frequent
France
4.1%42 installations of 1,013
Analysis at Scale
Pros
- Analytical querying due to built in analytical functions that actually perform across TB of data.
- Ingestion of data. We can send billions of rows to Vertica easily via the WOS system and it is ready for use immediately.
- Efficient storage of data. What raw is TB of data, once ingested into Vertica only takes up GB of disk space.
- Management! The management console is intuitive and useful making keeping an eye on your cluster easier than any other product like this I have used.
Cons
- Deletion is tough in Vertica. Because one of our larger fact tables is rapidly changing we have a need to run purges on a regular basis. Those purges can take a day and delays the other processes while that is happening. It would be nice if when I hit delete, it really deleted.
- Permissions on table manipulation is a bit lacking. In order to edit a table structure you have to be the owner, ie the creator, of the table. It means setting up true administrators who can maintain each other's work is tough.
Return on Investment
- For our internal business we have insights we could never have had without Vertica. We can actually see where our money is coming from and point our marketing and sales strategies in the correct direction thereby returning far more than we pay.
- For our customers we can offer services they had been begging for. Before implementing Vertica we had no insight on a client's marketing across all their activities because the data was just too large. Now, there is no question we can't answer.
Alternatives Considered
Teradata Database, IBM Netezza Data Warehouse Appliances, Infobright, MySQL and Microsoft SQL Server
Other Software Used
Pentaho, TIBCO Jaspersoft, MySQL, Microsoft SQL Server
Good analytical database
Pros
- Column-oriented storage organization, which increases performance of queries.
- Compression, which reduces storage costs and I/O bandwidth. High compression is possible because columns of homogeneous datatypes are stored together and because updates to the main store are batched.
- Shared nothing architecture, which reduces system contention for shared resources and allows gradual degradation of performance in the face of hardware failure.
- Easy to use and maintain through automated data replication, server recovery, query optimization, and storage optimization.
- Support for standard programming interfaces ODBC, JDBC, ADO.NET, and OLEDB.
- Integration to Hadoop with the capability to perform analytics on ORC and Parquet files directly.
Cons
- Per TB licensing. Users have to worry about license usage at all times which becomes a challenge with you are working in an organization with huge amounts of data.
- The geospatial functionality could be designed better.
- Support for containerization and flexibility from the deployment standpoint.
Most Important Features
- ML libs and it's inbuilt analytical functions.
- Vertica python is a great library for data scientists.
- Vertica is one of the fastest query engines.
Return on Investment
- Vertica UDX is one of the best capabilities from which you can extend Vertica based on your custom needs. Its integrated environment for ML models is pretty good which brings analytics on the plate in just a matter of steps.
- Distributed computing, analytics functions and its continuous improvement of the product.
- As far as concurrency is concerned, earlier versions of the platform struggled with concurrency, but enhancements such as cascading resource pools have dramatically improved concurrency and resource management.
Alternatives Considered
Microsoft SQL Server, Oracle Database, MySQL and Teradata Vantage - Enterprise Data Warehousing
Other Software Used
MySQL, Oracle Database, Microsoft SQL Server
Robust Vertica Experience
Pros
- After the initial setup and performance tuning phase, Vertica database cluster pretty much runs on its own. We haven't had too much maintenance to do.
- When we had to scale up the cluster from 6 nodes to 12 nodes, it was an easy task.
- At one time, because of some issues with a server, we had to take a node out and could do it on the fly.
Cons
- One time, one of the nodes wasn't coming up because of some ambiguity with the local data. Vertica wasn't able to fix it by itself and we were trying to remove the node out of the database and we couldn't do it. It would be great if that could be addressed. Luckily when we rebooted the whole server, some of the dead transaction got flushed because of which vertica was able to recover and the node came up.
Return on Investment
- For the most part, I would say it's a positive impact as it helped the developers to build a better strategy for working with the rendering data.
Alternatives Considered
Pivotal Greenplum
Other Software Used
Azure Kubernetes Service (AKS), Oracle Database, Couchbase
Vertica's Strengths and Weakness
Pros
- Extremely fast query performance - Vertica is one of the fastest query engines out there.
- Scales to TBs - Scales reasonably well up to 10-20 nodes and 10 - 100s of TB of data.
- Easy to Use - Fairly easy to user, we made quite some headway with just 1 person running it for a while.
Cons
- PetaByte Scale data - Vertica Just cannot deal with this, it starts to crumble beyond 100s of TB of data.
- Concurrent Usage - Vertica starts to have significant backpressure as your concurrent users grow quickly. We had trouble scaling post 20-30 users and had to invent our our queuing strategies.
- Vertical stack - storage + compute tier in one stack, this doesn't help the cause of scaling. Other systems leverage the advantage of storage and compute being different tiers (eg: HDFS + Presto)
Return on Investment
- We've been using vertica to derive a lot of valuable ad-hoc human insights
- Used to run periodic batch jobs that generate production results in the past, now moved to Hadoop for such use-cases
- We had a couple of big outages due to vertica unable to keep up with the load of queries and data (however were mitigated w/ leveraging hadoop).
Alternatives Considered
Hadoop and Presto
Other Software Used
Presto, Apache Hive, MemSQL
Vertica Review
Pros
- It is able to intake real-time streaming data without much pre-processing and latency.
- Easy to integrate with real-time streaming ingestion engine.
Cons
- Vertica does not perform well when you have a lot of schemata.
- The management console including GUI is lacking features and can be improved with features that are typical of a database.
Return on Investment
- Positive impact on ROI by being able to get customer insights in real-time.
- Positive ROI through reduced time to set-up and maintain Vertica instances.
Alternatives Considered
SAP HANA, MySQL, PostgreSQL, Oracle Data Warehouse, Snowflake, Google BigQuery and Cloudera Distribution Hadoop (CDH)
Other Software Used
Google BigQuery, Apple iCloud, iOS
