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.
Video Vertica
Products
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.
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.
Maps Vertica
Optimizations
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 9.0.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
Company events
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 August 2013, Vertica held its first Big Data conference event in Boston, MA USA. This event was held again in 2014, 2015, 2016, and 2017.
In 2016, Vertica published The Big Data Transformation: Understanding Why Change is Actually Good for Your Business.
See also
- C-Store
- Column-oriented database
- Massively Parallel Processing
- Machine Learning
- Datawarehouse
- R (programming language)
- Distributed R
- Greenplum
- MapReduce
- Shared nothing architecture
- SAP IQ
- ClickHouse
- Teradata
References
External link
- Official website
- Micro Focus Software official site
- Official user community
- Unofficial Vertica User Google Group
- Ex-Vertica CEO pledges to build high-speed railway for Big Data
- The Vertica Database Forums - User 2 User Support
- Independent Blog for the HPE Vertica Platform
Source of the article : Wikipedia
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