Aster Data Announces Seamless Connectivity with Hadoop

New Hadoop Connector Enables Ultra-Fast Transfer of Data between Hadoop and Aster Data's MPP Data Warehouse

San Carlos, Calif. – October 1, 2009 - Aster Data, a proven leader dedicated to providing the best data analytics and management platform for ‘Big Data’ applications, today announced the availability of the new Aster-Hadoop Data Connector, which utilizes Aster's patent-pending SQL-MapReduce capabilities for two-way, high-speed, data transfer between Apache Hadoop and Aster Data's massively parallel data warehouse. A number of Aster customers are already using both Aster and Hadoop in their environments.

Aster’s new Hadoop Connector allows businesses to leverage open-source Hadoop for data collection and preparation, alongside Aster, to perform complex data analytics and processing. For example, companies can now seamlessly use Hadoop for ETL processing or data munging, and then pull that data into Aster for interactive queries or ad-hoc analytics on massive data scales. The Connector utilizes key new SQL-MapReduce functions to provide ultra-fast, two-way data loading between HDFS (Hadoop Distributed File System) and Aster Data's MPP Database.

Key advantages of Aster's Hadoop Connector include:

  • High-performance: Fast, parallel data transfer between Hadoop and Aster nCluster.
  • Ease-of-use: Analysts can now seamlessly invoke a SQL command for ultra-simple import of Hadoop-MapReduce jobs, for deeper data analysis. Aster intelligently and automatically parallelizes the load.
  • Data Consistency: Aster Data's data integrity and transactional consistency capabilities treat the data load as a 'transaction,' ensuring that the data load or export is always consistent and can be carried out while other queries are running in parallel in Aster.
  • Extensibility: Customers can easily further extend the Connector using SQL-MapReduce, to provide further customization for their specific environment.

"Aster's connectivity to Hadoop is unique in that it is based on the simplicity and ease-of-use of Aster's SQL-MapReduce functionality, which speeds integration between the two systems,” said Tasso Argyros, CTO of Aster Data. “Further, Aster's MPP architecture adds capabilities like data consistency and parallel processing. These capabilities are crucial for business-critical applications, enabling large volumes of data to move between the systems, while Aster performs deep data analysis in parallel."

The industry’s first big data event, Big Data Summit ‘09, being held this evening in New York City, will showcase Hadoop’s fit with MPP data warehouses. Aster Data will be presenting alongside Colin White, President and Founder of BI Research, and Jonathan Goldman, who represents LinkedIn.

About Aster Data
Aster Data is a proven leader dedicated to providing the best data analytics and management platform for ‘Big Data’ applications – the first DBMS to tightly integrate SQL with MapReduce - providing rich insights on data managed on clusters of inexpensive commodity hardware. The Aster nCluster database cost-effectively powers rich analytic applications for companies such as Coremetrics, MySpace, aCerno (an Akamai company), and ShareThis. Running on low-cost off-the-shelf hardware, and providing 'hands-free' administration, Aster enables enterprises to meet their data warehousing and analytics needs within their budget. Aster is headquartered in San Carlos, California and is backed by Sequoia Capital, JAFCO Ventures, IVP, Cambrian Ventures, and First Round Capital, as well as industry visionaries including David Cheriton and Ron Conway. For more information please visit http://www.asterdata.com, or call 650-232-4400.

Aster Data, Aster nCluster and the Aster logo 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.

Press Contact:
Michelle Van Jura
ZAG Communications for Aster Data Systems

The Best Insights Possible
Whitepaper: A Revolutionary Approach for Advanced Analytics and Big Data Management
Whitepaper: Deriving Deep Insights from Big Datasets
Research Report: MapReduce and the Data Scientist