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Vertica has become a crucial tool for businesses looking to analyze large volumes of data for various use cases. Users have found it …
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What is OpenText Vertica?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.
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What is OpenText Vertica?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.
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Product Demos
Vertica in-DB Machine Learning Demo
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How to recover a HP Vertica Database Node from a Corrupted Catalog
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Vertica Optimized for Multiple Clouds Using Attunity Replicate
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vertica and elastic search demo
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WEBINAR: Predictive Analytics with Vertica
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Utilizing Tableau and HP Vertica Demo - Consolidating Worksheets into a Single Dashboard
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What is OpenText Vertica?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.
OpenText Vertica Video
Big Data has a big history. A history built upon many innovations and countless discoveries. Pushing humanity into new ways of thinking. New capabilities. A new way of seeing how the future can be. New ways of finding value in the massive amounts of data being created. And whe...
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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Vertica has become a crucial tool for businesses looking to analyze large volumes of data for various use cases. Users have found it particularly valuable as a data warehouse for analyzing internal business data and marketing results of clients. Its ability to handle large data sizes enables analysis at a level that would not have been possible otherwise. Uber, for example, has successfully employed Vertica for their data analytics needs. Additionally, companies have created Vertica-based data marts to provide analytics insights and support data science across their entire organizations.
One key advantage of Vertica is its complementary nature with other technologies like Hadoop. By leveraging its high scale capabilities, Vertica enhances data efforts when used alongside Hadoop. The software also serves as the main data warehouse, acting as a source for analytic reports and facilitating data analysis activities. Interestingly, users have discovered non-traditional applications for Vertica, utilizing it as a powerful data processing engine to solve problems at scale. For instance, in the entertainment industry, Vertica is instrumental in rendering data and performing big data analysis tasks efficiently.
The speed of Vertica is highly beneficial to users, allowing them to quickly complete ad-hoc queries and conduct more in-depth analyses. This speed sets Vertica apart from competitors in the highly ingested, fast query analytics niche, including platforms like Teradata, Greenplum, Exadata, and Netezza. Moreover, Vertica excels in handling large amounts of data ingestion quickly, making it a reliable tool for organizations dealing with vast quantities of information.
Furthermore, Vertica serves as an analytics database that can handle real-time streaming data from sources like Apache Kafka. This capability enables organizations to gain near real-time customer insights for their consumer-facing web portals and mobile applications. Overall, users have come to rely on Vertica as an essential analytics database for reporting, ad-hoc queries, and more in-depth analyses across a wide range of industries and use cases.
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.
Deletion Process: Users have expressed frustration with the deletion process in Vertica, stating that it does not fully delete when prompted and can cause delays in other processes. Some users have reported this issue.
Permissions on Table Manipulation: Reviewers find the permissions on table manipulation lacking in Vertica, as only the owner of the table can edit its structure. This makes it difficult to set up true administrators who can maintain each other's work. Several users have mentioned this limitation.
Handling Petabyte-Scale Data: Vertica struggles to handle petabyte-scale data according to user feedback. It starts to crumble beyond hundreds of terabytes of data. Numerous reviewers have noted this scalability issue.
Users have made several recommendations based on their experience with Vertica. The most common recommendations are:
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Proper Testing and Preparation: Users suggest that before releasing a major version of Vertica, it is crucial to have thorough testing in place. This ensures that any potential issues or bugs are identified and resolved prior to deployment.
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Follow Vendor Configuration Instructions: It is advised to closely follow the vendor's configuration instructions when setting up Vertica. This helps ensure optimal performance and stability of the tool.
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Training and Familiarity: Users recommend sending database administrators (DBAs) for training and studying the SQL limitations of Vertica. It is important to have a good understanding of Vertica and its capabilities to effectively leverage the tool for solving specific business problems.
It is important to note that while Vertica is highly recommended for data warehousing, solving Big Data solutions, and analytical data warehousing, users also suggest considering other database systems if there is not a significant amount of data that needs to be accessed quickly or if a more common/easier-to-set-up system would suffice.
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May 11, 2021
Good analytical database
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.
- 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.
- 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.
- 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.
- 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.
Vertica performs well when the query has good stats and is tuned well. Options for GUI clients are ugly and outdated. IO optimized: it's a columnar store with 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.
May 07, 2021
Robust Vertica Experience
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.
- 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.
- 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.
- 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.
We have been evaluating Greenplum comparing with Vertica.
December 04, 2017
Analysis at Scale
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.
- 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.
- 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.
- 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.
MySQL and MS SQL Server are both fantastic RDBMS products. MS SQL Server goes a bit further since it has the builtin analytical functions. But it only scales so far. Once the data goes beyond capacity, getting results out just does not happen anymore. IBM Netezza and Teradata were both appliances that required different expertise than we had in house. Vertica was able to do the same, and in some cases better, on commodity hardware (frankly in our case old servers that were slated for recycling!) and at a small scale. In other words, Vertica we could grow slowly over time. Infobright is a great log processing database but for the functions we were looking to serve it just didn't have some of the features Vertica had that we felt were show stoppers.