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

San Carlos, CA - September 15, 2010 - Aster Data, a proven leader dedicated to providing the best data management and data processing platform for big data management and analytics, today announced Aster Data nCluster 4.6, which includes a column data store, making 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 1000+ MapReduce-ready analytic functions, delivers a substantial breakthrough in richer, 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 powerful 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. Today’s announcement of Aster Data nCluster 4.6 takes that one step further by allowing 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. The delivery of Aster Data nCluster 4.6 continues Aster Data’s industry innovation and market leadership by integrating the power of MapReduce MPP analytics within a column store and being the first-ever tightly integrated SQL-MapReduce implementation for both column and row store.

"In optimizing the performance of the storage and retrieval of analytic data, column-store DBMS technology, built with a mixture of mature products, is gaining market share as an analytic engine,” said Donald Feinberg, Vice President, Gartner. “In addition, new techniques for managing large amounts of data, such as MapReduce and noSQL DBMS, are beginning to gain interest. As organizations realize they need more holistic capabilities to manage data, tools with consolidated functionalities, optimized to meet specific data management needs, will help reduce technology silos and streamline a portfolio of data management capabilities deployable across the enterprise."*

Organizations faced with performance, scale, complexity, and cost limitations now have compelling reasons to evolve 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, 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.  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 like the behavioral indicators of customer purchase history, e.g. 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.  Aster Data led the way in bringing together the fundamental technology innovations required to make big data analytics possible and continues that innovation today by extending the power of MapReduce-style MPP analytics to column store within a tightly integrated SQL-MapReduce environment.

Aster Data nCluster 4.6 feature highlights include:
Hybrid Row and Column Store:
New column-based storage option includes support for both standard SQL and SQL-MapReduce analytics in Aster Data nCluster 4.6. Choice of storage, implemented per-table partition, provides customers flexible performance optimization based on analytical workloads.

Enterprise-Grade Analytic Platform Services:
Aster Data nCluster 4.6 platform-level services are extended to the new column store. Services include: 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.  Online Precision Scaling™ provides on-demand, incremental scaling on commodity hardware of each functional server group for different workloads like queries, loads and back-up, significantly decreasing the cost and hassle of scaling the system to meet growing data volumes.

Data Model Express Tool:
New recommendation engine evaluates query workloads and automatically recommends which data store, row or column, would be optimal for the type of workloads provided.  Database administrators can select to store data in a row store, column store, or in both types of stores as necessary for optimal performance of their organization’s specific query loads.

Additional Packaged Analytic Functions:
In addition to the 1000+ MapReduce-ready analytic functions already available, Aster Data nCluster 4.6 provides several new statistical functions popular in decision analysis, operations research, and quality management including decision trees and histograms.

“Today’s announcement further underscores that companies of all sizes who need richer analytics can easily experience the power of MapReduce and quickly build powerful analytic applications with 1000’s of packaged analytic functions and the extensibility of the first-ever tightly integrated SQL-MapReduce implementation for both column- and row-store,” said Tasso Argyros, CTO and co-founder, Aster Data. “Extending the MapReduce analytical capability to a column-store within our data-analytics server is the logical next step in our continued path of innovation to enable customers to get more value more quickly from their data which is too massive to be stored in their existing data warehouse architectures.”

For more information on Aster Data, please visit http://www.asterdata.com/ or call 1.888.Aster.Data (US) or +1.650.232.4400 (INTL).

*Gartner Inc., Hype Cycle for Data Management, 2010” by Eric Thoo, Ted Friedman, Donald Feinberg, Mark A. Beyer, July 22, 2010.

About Aster Data
Aster Data is a proven leader in big data management and big data analysis for data-driven applications.  Aster Data’s nCluster is the first MPP data warehouse architecture that allows applications to be fully embedded within the database engine to enable ultra-fast, deep analysis of massive data sets. Aster Data's unique “applications-within™” approach allows application logic to exist and execute with the data itself. Termed a “Data-Analytics Server,” Aster Data’s solution effectively utilizes Aster Data’s patent-pending SQL-MapReduce together with parallelized data processing and applications to address the big data challenge. Companies using Aster Data include Coremetrics, MySpace, Akamai, Full Tilt Poker, and ShareThis. Aster Data is headquartered in San Carlos, California and is backed by Sequoia Capital, JAFCO Ventures, IVP, and Cambrian Ventures, as well as industry visionaries including David Cheriton and Ron Conway. For more information please visit http://www.asterdata.com, or call 1.888.Aster.Data.

Aster Data, Aster Data nCluster, SQL-MapReduce, the Aster logo, and Applications-Within™ are registered trademarks of Aster Data. All other brands and trademarks referenced herein are acknowledged to be trademarks or registered trademarks of their respective holders.

Media Contact
Stacey Collins Burbach
Point Communications Group for Aster Data
602.279.1137
sburbach@pointcgroup.com

 

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