TrustRadius Insights for OpenText Vertica are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Impressive Analytical Querying Capabilities: Several reviewers have praised Vertica for its impressive analytical querying capabilities. Users have found the built-in analytical functions to be powerful, allowing them to perform complex analyses across terabytes of data. This feature has enabled users to gain interesting insights and make data-driven decisions.
Efficient Data Ingestion: Many users have highlighted Vertica's efficient data ingestion process as a major advantage. According to reviewers, billions of rows can be easily sent to Vertica via the WOS system, and the data is ready for immediate use. This streamlined data ingestion process not only saves time but also enables quick analysis, enhancing productivity.
Scalability and Performance: The scalability and performance of Vertica have been widely appreciated by reviewers. Users have mentioned that Vertica can scale reasonably well up to 10-20 nodes and handle hundreds of terabytes of data effectively. Additionally, many reviewers consider Vertica as one of the fastest query engines available, with tables containing billions of rows still delivering speedy results for analytical tasks.
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OpenText Vertica Reviews
2 Reviews
Engineering
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Vertica serves a database niche that is highly ingested with fast query analytics (MPP). It competes with platforms such as Teradata, Greenplum, Exadata, and Netezza. It does not compete with pseudo column stores such as a SQL Server column store, as those types of "features" are immature and still built on an OLTP platform. Vertica is quick with a large amount of data ingestion.
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.
Likelihood to Recommend
Its performance, scalability, low cost, and it's integration into enterprise big data environments is a plus. Queries are not optimized compared to Teradata and sometimes it takes down the database with very limited detail. Vertica Just cannot deal with scaling data, it starts to crumble beyond 100s of TB of data.
Vertica is used by uber for data analytics use cases. We have a vertica based data mart (subset of business data) for analytics insight and data science across the entire organization. We use it as a complementary solution to Hadoop. We initially started our with Vertica which worked for our needs, but over the last couple of years have started leveraging hadoop in addition to vertica to help our data efforts with high scale.
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)
Likelihood to Recommend
As someone just starting out with data analytics and warehousing vertica is a great tool for a small scale business. It has amazing performance and can scale upto TBs of data. It works well for any organization which has about 100 - 500 DAUs of the system. The system doesn't require a lot of ops overhead.
Scaling for PB data and 1000s of DAU is vertica's weak point. The system is just not designed for large scale usage and still has a long way to go to improve scalability. There are experiments to run Vertica query engine on top of HDFS which seem promising, however - if you have the the hadoop ecosystem you are better off going the HDFS + Presto/Impala/SparkSQL route. But if you are in the hadoop ecosystem, you probably are already investing a lot in ops.
