The San Carlos, Calif. company's new offering will allow users to choose the data format best suited to their needs and benefit from the power of Aster Data’s SQL-MapReduce analytic capabilities, as well as Aster Data’s suite of 1000+ MapReduce-ready analytic functions. [Disclosure: Aster Data is a sponsor of BriefingsDirect podcasts.]
Row stores traditionally have been optimized for look-up style queries, while column stores are traditionally optimized for scan-style queries. Providing both a row store and a column store within nCluster and delivering a unified SQL-MapReduce framework across both stores enables both query types.
Universal query framework
For example, a retailer using historical customer purchases to derive customer behavior indicators may store each customer purchase in a row store to ease retrieval of any individual customer order. This is a look-up style query. This same retailer can see a 5-15x performance improvement by using a column store to provide access to the data for a scan-style query, such as 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 universal query framework.
Other features include:
- Choice of storage, implemented per-table partition, which provides customers flexible performance optimization based on analytical workloads.
- Such services as dynamic workload management, fault tolerance, Online Precision Scaling on commodity hardware, compression, indexing, automatic partitioning, SQL-MapReduce, SQL constructs, and cross-storage queries, among others.
- New statistical functions popular in decision analysis, operations research, and quality management including decision trees and histograms.