Aster Data Introduces Advanced MapReduce Analytics on Column Store DBMS
New Aster Data nCluster 4.6 extends the power of SQL-MapReduce to hybrid row and column DBMS, enabling richer analytic applications
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Aster Data has released Aster Data nCluster 4.6 that includes a column data store, providing a platform with a unified SQL-MapReduce analytic framework on a hybrid row and column massively parallel processing (MPP) database management system (DBMS). The unified SQL-MapReduce analytic framework and Aster Data’s suite of 1000+ MapReduce-ready analytic functions, delivers rich, high-performance analytics on large data volumes where data can be stored in either a row or column format.
With Aster Data nCluster 4.6, customers can choose the data format best suited to their needs and benefit from the power of Aster Data’s SQL-MapReduce analytic capabilities, providing maximum query performance by leveraging row-only, column-only, or hybrid storage strategies. Aster Data makes selection of the appropriate storage strategy easy with the new Data Model Express tool that determines the optimal data model based on a customer’s query workloads. Both row and column stores in Aster Data nCluster 4.6 benefit from platform-level services including Online Precision Scaling on commodity hardware, dynamic workload management, and always-on availability, all of which now operate on both row and column stores.
All 1000+ MapReduce-ready analytic functions released previously through Aster Data Analytic Foundation -- a suite of pre-built MapReduce analytic software building blocks — now run on a hybrid row and column architecture. Aster Data nCluster 4.6 also includes new, pre-built analytic functions, including decision trees and histograms. For custom analytic application development, the Aster Data IDE, Aster Data Developer Express, also fully and seamlessly supports the hybrid row and column store in Aster Data nCluster 4.6.
Aster Data’s previous releases eased the development and management of sophisticated analytics for data exploration on massive data volumes, enabling richer and deeper business insights. Version 4.6 allows customers to leverage the power of Aster Data’s data-analytics platform to manage multiple storage formats while providing a powerful platform for advanced analytics -- integrating the power of MapReduce MPP analytics within a column store and offering a tightly integrated SQL-MapReduce implementation for both column and row store.
Organizations faced with performance, scale, complexity, and cost limitations can now advance their analytic strategy with a solution that delivers rapid and rich analytics on large volumes of data, regardless of whether they require row or column store or a hybrid of both. Row stores have traditionally optimized more for ad hoc, interactive queries; column stores are traditionally optimized for reporting-style queries. Now providing both a row store and a column store within Aster Data nCluster 4.6 and delivering a unified SQL-MapReduce framework across both stores, Aster Data delivers a solution across the complete continuum of interactive to reporting style queries.
For example, a retailer using historical customer purchases to derive customer behavior indicators may often perform interactive queries against customer purchase data in a row store to analyze trends in individual customer orders over time. This ad hoc interactivity to identify patterns in the data is well-suited to the properties of a row store. Yet, this same retailer can see a 5-15x performance improvement from using a column store to provide access to the data for reporting-style queries such as the behavioral indicators of customer purchase history (for example, the number of purchases completed per brand or category of product). The Aster Data platform now supports both query types with natively optimized stores and a unified query framework.
More information is available at www.asterdata.com.