Vertica

Vertica 5.0 Software Release

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Who's Winning With Vertica?

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Vertica 5.0 brings a host of new features to the Vertica Analytics Platform, delivering greater analytical power, cloud enablement, availability and ease of use to the analytic marketplace:

Features and Benefits

Here is a list of the major new features included in the Vertica 5.0 release:

  • Software Development Kit. Vertica 5.0 provides users, developers and partners with a robust SDK to write custom methods for analyzing data directly in the Vertica database. Users can implement MapReduce and other user-defined functions through standard SQL queries.
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  • Enhanced Native In-Database Analytics. Extending Vertica’s implementation of standard SQL, new advanced native SQL analytic functions include Geospatial, Event-Series Pattern Matching, Event-Series Joins, and Advanced Aggregate Statistical and Regression.
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  • Performance Improvements. Extending Vertica’s lead in real-time analytics for real-world applications, Vertica 5.0 delivers performance enhancements throughout the Vertica Analytics Platform in areas such as subqueries, database statistics, lifecycle management, query optimization, data resegmentation and join filtering.
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  • Manageability Enhancements. Responding to the availability needs of mission-critical analytics at petabyte scale, Vertica 5.0 offers features that yield unique operational and disaster recovery capabilities while providing performance advantages as data replicas are fully available. In addition, Vertica’s backup capabilities have been expanded with greater operational flexibility and improved performance.
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  • Elasticity and Cluster Cloning. Vertica 5.0 delivers enhanced elasticity features that enable dynamic expansion and contraction of clusters over 20x faster in every deployment scenario – cloud, virtual and physical – allowing users to quickly create additional capacity as needed. In addition, Vertica 5.0 offers full cluster cloning capabilities for on-demand creation of Vertica databases for any workload with auto-sizing and auto-tuning across different topologies and physical designs. This allows users to quickly spin off subsets of their databases for analytical sandboxes or focused analysis of complex data.