|Industry||Enterprise Software & Database Management & Data Warehousing|
|Founder||Andrew Palmer and Michael Stonebraker|
Nga Tran & Stephen Walkauskas (Engineering), Amy Miller (Customer Success)
|Products||Vertica Analytics Platform Enterprise Edition, Vertica SQL on Hadoop, Vertica Analytics Platform Community Edition|
Vertica Systems is an analytic database management software company. Vertica was founded in 2005 by database researcher Michael Stonebraker and Andrew Palmer. Palmer was the founding CEO; later, Ralph Breslauer and Christopher P. Lynch served as CEOs.
Lynch joined as Chairman and CEO in 2010 and was responsible for Vertica's acquisition by Hewlett Packard in March, 2011. The acquisition expanded the HP Software software portfolio for enterprise companies and the public sector group. As part of the Micro Focus-Hewlett Packard Enterprise merger, Vertica joined Micro Focus in September, 2017.
The column-oriented Vertica Analytics Platform was designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications. The product claims to greatly improve query performance over traditional relational database systems, and to provide high availability and exabyte scalability on commodity enterprise servers. Vertica is infrastructure-independent, supporting deployments on multiple cloud platforms (AWS, Google, Azure), on-premise and natively on Hadoop nodes. Vertica's Eon Mode, available on Amazon Web Services, separates compute from storage and leverages low cost Amazon S3 object storage and the ability to apply compute to variable workloads, capitalizing on cloud economics.
Its design features include:
- Column-oriented storage organization, which increases performance of sequential record access at the expense of common transactional operations such as single record retrieval, updates, and deletes.
- Massively parallel processing (MPP) architecture to distribute queries on independent nodes and scale performance linearly.
- Standard SQL interface with many analytics capabilities built-in, such as time series gap filling/interpolation, event-based windowing and sessionization, pattern matching, event series joins, statistical computation (e.g., regression analysis), and geospatial analysis.
- In-database machine learning including categorization, fitting and prediction to enhance processing speed by eliminating the need for down-sampling and data movement. Vertica offers a variety of in-database algorithms, including linear regression, logistic regression, k-means clustering, Naive Bayes classification, random forest decision trees, and support vector machine regression and classification. Vertica also allows deployment of ML models to multiple clusters.
- Compression, which reduces storage costs and I/O bandwidth. High compression is possible because columns of homogeneous datatype 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 workload management, data replication, server recovery, query optimization, and storage optimization.
- Native integration with open source big data technologies like Apache Kafka and Apache Spark.
- Support for standard programming interfaces, including ODBC, JDBC, ADO.NET, and OLEDB.
- High-performance and parallel data transfer to statistical tools such as built-in machine learning algorithms based on R, and the ability to store machine learning models, and use them for in-database scoring.
Vertica's specialized approach aims to significantly increase query performance in data warehouses, while reducing the total cost of ownership by reducing the hardware footprint. One example of a use case detailed in a research paper shows a performance improvement of hundreds of times with Vertica in a specific application due to the use of the vertical DBMS approach.
In late 2011, the Vertica Analytics Platform Community Edition was made available for free with certain limitations, such as a maximum of one terabyte of raw data, three-node (servers) cluster, and community-based support.
The Vertica Analytics Platform runs on clusters of Linux-based commodity servers. It is also available on the Amazon Elastic Compute Cloud , Microsoft Azure and the Google Cloud Platform, ensuring no infrastructure or platform lock in. The product integrates with Hadoop to leverage HDFS via External Tables with ORC and Parquet Readers and can be installed on Hadoop nodes in a co-located manner as Vertica for SQL on Hadoop (a separate offering, priced by per node). These combined capabilities allow users to choose where to analyze their data, including across multiple data lakes.
A range of BI, data visualization, and ETL tools are certified to work with and integrate with the Vertica Analytics Platform. Vertica also offers a certified and secure interface with the popular Kafka message bus, allowing streaming data ingestion. This capability combined with Vertica's high performance analytics supports use cases like Internet of Things, Edge Analytics and near real time Fraud Prevention.
Several of Vertica’s features were originally prototyped within the C-Store column-oriented database, an academic open source research project at MIT and other universities. The system's architecture is described in a 2012 VLDB paper.
Versions and documentation
- Vertica Analytics Platform 9.1.x
- Vertica Analytics Platform 9.0.x
- Vertica Analytics Platform 8.1.x
- Vertica Analytics Platform 8.0.x
- Vertica Analytics Platform 7.2.x
- Vertica Analytics Platform 7.1.x
- Vertica Analytics Platform 7.0.x
- Vertica Analytics Platform 6.1.x
- Vertica 6.0.x Enterprise Edition
- Vertica 5.1 Enterprise Edition
- Vertica Enterprise Edition 5.0
- Vertica Enterprise Edition 4.1
In January 2008, Sybase filed a patent-infringement lawsuit against Vertica. In January 2010, Vertica prevailed in a preliminary hearing, and in June, 2010, Sybase and Vertica resolved the suit, with the court dismissing all infringement claims. Under the leadership of Colin Mahony, Vertica has sponsored various technological events in the database industry.
In 2016, Vertica published The Big Data Transformation: Understanding Why Change is Actually Good for Your Business.
- Column-oriented database
- Massively Parallel Processing
- Machine Learning
- R (programming language)
- Distributed R
- Shared nothing architecture
- SAP IQ
- Snowflake Computing
- Network World staff: "New database company raises funds, nabs ex-Oracle bigwigs”,  LinuxWorld, February 14, 2007
- Brodkin, J: "10 enterprise software companies to watch",  Archived 2007-05-18 at the Wayback Machine. Network World, April 11, 2007
- HP News Release: “HP to Acquire Vertica: Customers Can Analyze Massive Amounts of Big Data at Speed and Scale” Feb. 2011
- HP News Release: “HP Completes Acquisition of Vertica Systems, Inc.” March 22, 2011.
- ComputerWorld.com: “Update: HP to buy Vertica for analytics.” Kanaracus. Feb. 2011.
- Monash, C: "Are row-oriented RDBMS obsolete?"  DBMS2, January 22, 2007
- Monash, C: "Mike Stonebraker on database compression – comments”,DBMS2, March 24, 2007
- Gagliordi, Natalie. "HP adds scale to open-source R in latest big data platform". ZDNet. Retrieved 17 February 2015.
- Prasad, Shreya; Fard, Arash; Gupta, Vishrut; Martinez, Jorge; LeFevre, Jeff; Xu, Vincent; Hsu, Meichun; Roy, Indrajit (2015). "Enabling predictive analytics in Vertica: Fast data transfer, distributed model creation and in-database prediction". ACM SIGMOD International Conference on Management of Data (SIGMOD).
- One Size Fits All? Part 2: Benchmarking Results (sect. 3.1)
- "Vertica Announces Community Edition Version of Vertica Analytic Database". Archived from the original on July 4, 2015. Retrieved August 17, 2016.
- "Vertica-Hadoop integration". DBMS2. October 12, 2010.
- "The Vertica Analytic Database: C-Store 7 Years Later" (PDF). VLDB. August 28, 2012.
- Documentation https://my.vertica.com/docs/9.1.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/9.0.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/8.1.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/8.0.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/7.2.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/7.1.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/7.0.x/HTML/index.htm
- Documentation https://my.vertica.com/docs/6.1.x/HTML/index.htm
- Documentation http://www.vertica.com/documentation/hp-vertica-documentation-6-0-x/
- Documentation http://www.vertica.com/documentation/hp-vertica-5-1-x-enterprise-edition-product-documentation/
- Documentation http://www.vertica.com/documentation/hp-vertica-enterprise-edition-5-0-product-documentation/
- Documentation http://www.vertica.com/documentation/hp-vertica-documentation-5-1/
- Sybase, Inc. v. Vertica Systems, Inc. (Texas Eastern District Court January 30, 2008). Text
- Monash, C: "Vertica slaughters Sybase in patent litigation”,DBMS2, January 14, 2010
- Vertica Press Release, "Vertica Resolves Sybase Patent Lawsuits" http://www.vertica.com/news/press/vertica-resolves-sybase-patent-lawsuits/
- HP Vertica Big Data Conference 2013 http://www.vertica.com/hp-vertica-big-data-conference-2013/