Hadoop is a software framework that can be used to manage big data. It can also be used in the cloud to keep data flowing for companies and has the ability to produce a comprehensive report that incorporates data stored in countless records.
Allowing companies to process more data in less time on Hadoop, Syncsort, a provider of big data integration solutions, has announced its spring '13 release, which includes two brand new Hadoop products and some improvements to DMX.
These new products help Hadoop become a dependable, feature-rich extract, transform, load (ETL) solution. Syncsort's recent contribution to Apache Hadoop is used by the new DMX-h solutions to offer improved data integration feature and Sort acceleration for Apache Hadoop distributions.
DMX-h has the only ETL engine that runs natively within MapReduce. The Windows GUI is simple to use and can be easily deployed into Hadoop. It also provides a library of pre-built templates which helps developers rapidly deploy Hadoop ETL.
To deploy ETL in Hadoop, companies need to get a whole new set of state-of-the-art programming skills. DMX-h Hadoop ETL Edition equips them with this skill and can be used even by non-data-scientists to create ETL jobs in Hadoop.
“Analyzing big data is critical to our customers' ability to sustain competitiveness, but the avalanche of information is breaking traditional data integration architectures - many of the tools are too code and resource intensive and ultimately drive costs too high," said Josh Rogers, senior vice president, data integration business, Syncsort. "With our new DMX editions, we are strengthening Hadoop by providing seamless and powerful ETL and sort capabilities and at the same time, reinvigorating the value proposition of ETL.”
Recently, the company introduced a new feature that strengthens Apache Hadoop's big data integration and ETL features that allows external sort implementations within the Hadoop MapReduce framework, helping organizations accelerate development, build complex ETL flows and MapReduce jobs without coding and seamlessly optimize Hadoop.
Edited by Rachel Ramsey