OpenText Vertica Reviews & Insights

Score9.4 out of 10

29 Reviews and Ratings

Community insights

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.

OpenText Vertica Reviews

7 Reviews

Good analytical database

Rating: 9 out of 10

Incentivized

May 11, 2021

Use Cases and Deployment Scope

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.

Verified User

Engineer in Engineering (5001-10,000 employees)

Vetted Review

2 years of experience

Robust Vertica Experience

Rating: 7 out of 10

Incentivized

September 1, 2020

Use Cases and Deployment Scope

It's used by couple of departments. I work in the entertainment industry, so it is used to deal with the rendering data. It is also used for big data analysis.

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.

Likelihood to Recommend

It's definitely good for working with larger amount of data and easy scaling. In my experience, Veritca is the only cluster that I have dealt with terabytes of data.

Verified User

Administrator in Information Technology (1001-5000 employees)

Vetted Review

6 years of experience

Vertica Review

Rating: 7 out of 10

Incentivized

December 15, 2019

Use Cases and Deployment Scope

Vertica forms the analytics database that takes in realtime streaming data (from Apache Kafka) and is used to provide customer insights in near real-time. It is used for the consumer-facing web portal and mobile applications.

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.

Likelihood to Recommend

Vertica is well suited when latency from incoming data is key and you need Strickland timing guarantees to process the real-time streaming data. It is very well suited if you are using Confluent/Apache Kafka as the set-up and install is super easy and there best practice documentation available for it. It is less appropriate where you are looking at complex queries and schemas.

Verified User

Analyst in Information Technology (51-200 employees)

Vetted Review

4 years of experience

Analysis at Scale

Rating: 10 out of 10

Incentivized

December 4, 2017

Use Cases and Deployment Scope

Vertica is our data warehouse. We use it for analysis of our internal business as well as the marketing results of our clients. Due to the size of our data, without Vertica analysis at this level would not be possible.

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.

Likelihood to Recommend

Vertica as a data warehouse to deliver analytics in-house and even to your client base on scale is not rivaled anywhere in the market. Frankly, in my experience it is not even close to equaled. Because it is such a powerful data warehouse, some people attempt to use it as a transactional database. It certainly is not one of those. Individual row inserts are slow and do not perform well. Deletes are a whole other story. RDBMS it is definitely not. OLAP it rocks.

Eva Donaldson

Business Intelligence Manager in Information Technology at iContact (51-200 employees)

Vetted Review

3 years of experience

View profile

Fast with some limitations

Rating: 7 out of 10

Incentivized

December 14, 2016

Use Cases and Deployment Scope

We use Vertica as an analytics database for reporting, ad-hoc queries, regular reporting and more in-depth analyses. It is primarily used for querying by the analytics org and indirectly, through Looker, by the entire company. Vertica is most helpful because of its speed. Ad-hoc queries and analyses can be completed relatively quickly.

Pros

Speed. Even with tables with 20 Billion+ rows, Vertica performs reasonably well.
Analytical functions. Some of the advanced functions in Vertica enable/facilitate interesting and complex analyses.
Reliability. We never run into reliability issues with Vertica.

Cons

Data size limitations. Beyond a certain threshold Vertica breaks down. Because of this, we are not able to put all our data in Vertica and have to resort to Scala/Hive on Hadoop.
Pricing: Vertica can get pretty expensive with large data sizes.
Speed: Queries could always be faster!
Limited options for querying clients: We primarily use Vertica from our terminals. Options for GUI clients are ugly and outdated. Using the terminal for querying is sometimes annoying, with problems like showing query runtime only in milliseconds and not being able to change it, columns being hard to read when there are more columns than the display space etc.

Likelihood to Recommend

It is appropriate for interactive querying. It is not appropriate for complete storage of all your data for bigger companies.

Verified User

Analyst in Marketing (501-1000 employees)

Vetted Review

1 year of experience

Vertica's Strengths and Weakness

Rating: 7 out of 10

Incentivized

November 4, 2016

Use Cases and Deployment Scope

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.

Praveen Murugesan

Engineering Manager - Ride Experience in Engineering at Uber (5001-10,000 employees)

Vetted Review

2 years of experience

View profile

Fast and powerful analytics platform

Rating: 9 out of 10

Incentivized

December 8, 2015

Use Cases and Deployment Scope

Vertica is our main data warehouse. Is used as a source for most of our analytic reports as well as for all data analysis activities. We also use it in a non-traditional fashion, more like a data processing engine for solving problems at scale (matching, statistics, correlate sources, etc.). It runs in AWS with data loaded/unloaded from/to S3.

Pros

IO optimized - it's a columnar store, no indexing structures to maintain like traditional databases, the indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process.
Reduced data storage footprint through advanced encoding schemas (RLE, common-delta, etc.) as well as compression algorithms ability to operate directly on the encoded data.

Cons

Could use some work on better integrating with cloud providers and open source technologies. For AWS you will find an AMI in the marketplace and recently a connector for loading data from S3 directly was created. With last release, integration with Kafka was added that can help.
Managing large workloads (concurrent queries) is a bit challenging.
Having a way to provide an estimate on the duration for currently executing queries / etc. can be helpful. Vertica provides some counters for the query execution engine that are helpful but some may find confusing.
Unloading data over JDBC is very slow. We've had to come up with alternatives based on vsql, etc. Not a very clean, official on how to unload data.

Likelihood to Recommend

Vertica is not the silver bullet but based on my experience in 9/10 cases in which you need an analytical database, Vertica is probably the answer.

Currently we're using Vertica more as a data processing engine in conjunction with a Hadoop cluster as some of the steps are way more efficient than doing them in Hadoop and easier to manage (e.g. iterative processing steps). We also had a pretty good experience using it with Storm and Hadoop.

At the same time, using Vertica as a traditional OLTP database, with many small transactions inserting/deleting/updating data is not going to take you very far so that’s an obvious case where Vertica is not recommended.

Traian Antonescu

Big Data Technical Director in Information Technology at CircleBack, Inc. (11-50 employees)

Vetted Review

5 years of experience

View profile

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Fast and powerful analytics platform

Rating: 9 out of 10

Incentivized

December 8, 2015

Use Cases and Deployment Scope

Pros

IO optimized - it's a columnar store, no indexing structures to maintain like traditional databases, the indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process.
Reduced data storage footprint through advanced encoding schemas (RLE, common-delta, etc.) as well as compression algorithms ability to operate directly on the encoded data.

Cons

Could use some work on better integrating with cloud providers and open source technologies. For AWS you will find an AMI in the marketplace and recently a connector for loading data from S3 directly was created. With last release, integration with Kafka was added that can help.
Managing large workloads (concurrent queries) is a bit challenging.
Having a way to provide an estimate on the duration for currently executing queries / etc. can be helpful. Vertica provides some counters for the query execution engine that are helpful but some may find confusing.
Unloading data over JDBC is very slow. We've had to come up with alternatives based on vsql, etc. Not a very clean, official on how to unload data.

Likelihood to Recommend

Vertica is not the silver bullet but based on my experience in 9/10 cases in which you need an analytical database, Vertica is probably the answer.

Traian Antonescu

Big Data Technical Director in Information Technology at CircleBack, Inc. (11-50 employees)

Vetted Review

5 years of experience

View profile