Rahul Sharma

Vertica

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Website  www.vertica.com
Founded  2005
Vertica httpslh6googleusercontentcomdCmNqqE9GMAAA
Industry  Enterprise Software & Database Management & Data Warehousing
Key people  Colin Mahony (SVP and General Manager) Joy King (VP of Product Marketing & Product Management) Misha Davidson (Director of Engineering)
Products  Vertica Analytics Platform Enterprise Edition, Vertica SQL on Hadoop, Vertica Analytics Platform Community Edition
Headquarters  Cambridge, Massachusetts, United States
Founders  Michael Stonebraker, Stanley Zdonik, Andy Palmer, Andrew Palmer
Parent organization  Hewlett Packard Enterprise
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Vertica sql on hadoop install tutorial


Vertica Systems was an analytic database management software company. Vertica was founded in 2005 by database researcher Michael Stonebraker, and Andrew Palmer. Former CEOs include Ralph Breslauer and Christopher P. Lynch.

Contents

Vertica was acquired by Hewlett Packard on March 22, 2011. The acquisition expanded the HP Software software portfolio for enterprise companies and the public sector group.

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Products

The cluster-, 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 drastically improve query performance over traditional relational database systems, provide high-availability, and petabyte scalability on commodity enterprise servers.

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.
  • 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.
  • Out-of-place updates and hybrid storage organization, which increase the performance of queries, insertions, and loads, but at the expense of updates and deletes.
  • 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 data replication, server recovery, query optimization, and storage optimization.
  • Support for standard programming interfaces 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.

    Optimizations

    The Vertica Analytics Platform runs on cluster of Linux-based commodity servers. It is also available as a hosted DBMS provisioned by and running on the Amazon Elastic Compute Cloud and Microsoft Azure, 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).

    A range of BI, data visualization, and ETL tools are certified to work with and integrate with the Vertica Analytics Platform. For example, the MicroStrategy business intelligence platform is optimized for the Vertica database through Vertica-specific SQL syntax.The Vertica Marketplace lists many of these.

    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

  • HPE Vertica Analytics Platform 8.0.x
  • HPE Vertica Analytics Platform 7.2.x
  • HP Vertica Analytics Platform 7.1.x
  • HP Vertica Analytics Platform 7.0.x
  • HP Vertica Analytics Platform 6.1.x
  • HP Vertica 6.0.x Enterprise Edition
  • HP Vertica 5.1 Enterprise Edition
  • HP Vertica Enterprise Edition 5.0
  • HP 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, HP Vertica held its first Big Data conference event in Boston, MA USA. This event was held again in 2014, 2015, and 2016, and is scheduled for 2017.

    In 2016, HPE published its first O'Reilly book, The Big Data Transformation - Understanding Why Change is Actually Good for Your Business.

    References

    Vertica Wikipedia


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