Aster Data has introduced Aster Data nCluster 4.6, which includes a column data store. This release, the company says, makes Aster Data nCluster 4.6 the first 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 more than 1,000 MapReduce-ready analytic functions, delivers richer, high performance analytics on large data volumes where data can be stored in either a row or column format.
"Customers are likely to see a number of benefits with this release," Stephanie McReynolds, director of product marketing at Aster Data, tells 5 Minute Briefing. Not only did Aster Data introduce a column data store option for higher performance analytics but it is also providing, on top of the two data storage techniques, a unified computation layer for much richer analytics, she notes. "Our customers are going to see much higher performance and richer analytics by being able to execute queries that are supported by both the row and the column store and this means that they will have wider diversity of analytics use cases that they can support."
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 facilitates selection of the appropriate storage strategy 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 1,000-plus 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 seamlessly supports the hybrid row and column store in Aster Data nCluster 4.6.
Row stores have traditionally optimized more for ad hoc, interactive queries, while 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.
There are two key market trends that have led to the need for the new capabilities, says McReynolds. One is the exponential growth of data volumes, the "big data" trend. But there is another even more interesting trend in the market which is to perform even more advanced, deeper analysis on that big data, she notes. "Customers are looking to not only perform analysis on more data than they had in the past but that analysis is getting richer and richer in order to address their business challenges."
Organizations have tried to sample data to do some of that advanced analytics but what happens when they sample data is that they often miss the data points that can lead to interesting conclusions. For example, McReynolds observes, for something like fraud prevention or fraud analysis, if an organization is only looking at a sample of the data they are going to miss some early indicators of a fraudulent transaction and may not catch the fraud in process. By now being able to analyze all the data that is coming in to an organization for some of these use cases, they are able to not only catch more fraud but provide support for more interesting analytic algorithms, she says, pointing out, "Those algorithms get more accurate with the more data that you have."
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