Aster Data Upgrades MPP Analytic DatabaseAster Data Upgrades MPP Analytic Database
The new version of the massively parallel processing analytic database includes a 'data-application server' that acts as a host for application processing, while also managing workloads, security and fault isolation.
November 6, 2009
Aster Data has launched a new version of its massively parallel processing analytic database that enables users to embed application logic in the database to dramatically speed up processing of large amounts of data.
The "data-application server" in Aster Data Version 4 is separate from the database in that it has its own supporting services. The server acts as a host for application processing, while also managing workloads, security and fault isolation. Separating data management and application processing provides maximum code portability, according to the vendor. The server supports packaged or custom applications written in Java, C, C++, C#, .Net, Perl and Python. The application server supports hundreds of concurrent mixed workloads through a rules-based prioritization and dynamic allocation of resources. Analyst Curt Monash of Monash Research points out in his blog that the server requires applications to to be parallelized and invoked via Aster's SQL/MapReduce. "Of course, Aster is hardly the only analytic DBMS (database management system) vendor to have the idea of explicitly enhancing general analytic processing; that’s why we see lots of MapReduce announcements," Monash said. "But I don’t know of anybody else whose approach is quite so elegant and general at this time." Executing an application in the MPP database in a parallel manner results in "ultra-fast data processing on massive data scales," Aster Data says. The need for speed is the result of businesses looking to store and analyze terabytes, and even petabytes, of data. Aster Data argues that today's pipes between the database and application logic running on a separate server can't handle such massive data flows effectively. Bringing applicatons to the data is the "only way to handle today's big data," Mayank Bawa, Aster Data chief executive and co-founder, said. "Fundamentally, we are bringing to an end the stranglehold of the traditional data pipeline – where large data sets were summarized, sampled and reduced to small data sets upon which basic analytics were performed – a process in itself that took days to weeks at best," Bawa said in a statement released this week. Aster Data is targeting a number of "big data" applications for its MPP database. They include near real-time fraud detection, customer behavior modeling, granular pattern analysis, micro-targeting, merchandising optimization, affinity marketing, trending and simulations, and trading surveillance.
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