Greenplum and Aster Data Systems, two startups involved in large-scale data analysis, announced this week that their products will support MapReduce, a programming technique originally developed by Google for parallel processing of large data sets across commodity hardware.
Software developers tend to be more comfortable with languages such as Java and C++ than the database language SQL, said Mayank Bawa, cofounder and CEO of Aster, maker of a cluster database system that splits workloads into multiple discrete tiers.
“Most developers struggle with the nuances of making a database dance well to their directions,” he wrote in a blog post. “Indeed, a SQL maestro is required to perform interesting queries for data transformations (during ETL processing or Extract-Load-Transform processing) or data mining (during analytics).”
Enter MapReduce, the goal of which was to provide a “trivially parallelizable framework so that even novice developers (a.k.a interns) could write programs in a variety of languages (Java/C/C++/Perl/Python) to analyze data independent of scale,” Bawa wrote.
Meanwhile, Greenplum, maker of a database it says can scale to a petabyte of information, said this week that a MapReduce framework will be part of its dataflow engine as of September.
The twin announcements brought a nod of approval from one close observer of the database world.
“On its own, MapReduce can do a lot of important work in data manipulation and analysis. Integrating it with SQL should just increase its applicability and power,” wrote Curt Monash of Monash Research, on the DBMS2 blog.
“MapReduce isn’t needed for tabular data management. That’s been efficiently parallelized in other ways,” he added. “But if you want to build non-tabular structures such as text indexes or graphs, MapReduce turns out to be a big help.”
