Vectorwise is a database that provides fast performance for analyzing big data through vector processing and columnar data storage. It performs over 100x faster than traditional databases on commodity hardware through techniques like processing multiple data vectors simultaneously using CPU cache. Vectorwise also minimizes storage needs through automatic data compression. It offers simplicity of use with ANSI SQL and industry standard connectivity while providing fast insights into large datasets.
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Actian Vectorwise Datasheet
1. › DATA SHEET
Vectorwise
Simply fAST
Data is one of your most important assets. The ability to analyze your
company, customers and marketplace can give you a competitive advantage
by helping you take better actions. However data volume is exploding and
traditional data analysis is slow and expensive. you must take action on
Big Data, but existing technologies don’t enable you to do so.
KEY BENEFITS
Vectorwise introduces revolutionary technology that leverages the latest performance
› pERfORmANCE features in modern CpUs. As a result, Vectorwise performs much faster than any other
› SimpliCiTy analytical database on equivalent commodity hardware. in addition, Vectorwise stores data
in columns rather than rows to ensure that columns that are not accessed by a query do
› AffORDABiliTy not have to be retrieved, and data is always automatically compressed. parallel execution
› lOW RiSK also ensures that you get maximum performance out of your hardware.
Vectorwise supports industry standards for relational databases including ANSi SQl
and connectivity through JDBC, ODBC and .Net. industry-leading Bi tools support the
Vectorwise database enabling insight into Big Data.
Vectorwise is available from Actian Corporation, formerly ingres Corporation. Actian
provides a 24/7 world-wide support infrastructure to support mission critical data
management applications running the ingres and Vectorwise databases.
Benefits of Vectorwise database features
Performance
› Vector-based processing enables up to 100 instructions to be processed
simultaneously.
› Vectors of data are processed using the chip cache memory which is more than
100x faster than using RAm.
› i/O and storage requirements are minimized with automatic optimum compression
technology.
› parallel execution ensures the fastest possible query execution times.
› fast performance enables insight into Big Data.