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Syncsort Simplifies Mainframe Big Data Access and Governance in Hadoop and Spark


Syncsort is introducing new capabilities to its data integration software, DMX-h, that allow organizations to work with mainframe data in Hadoop or Spark in its native format, which is necessary for preserving data lineage and maintaining compliance.

“As we were working with our customers we started hearing that there are actually more things that we can take advantage of and tap into in this mainframe data,” said Tendü Yogurtçu, general manager of Syncsort’s big data business. “We could do more advanced analytics and enable some of this data exploration if we were able to operate on this data in its mainframe native form.”

This updated to the software allows organizations to now leverage the benefits of big data platforms to analyze mainframe data just as they do with data from any other source, without requiring specialized skills to do so.

“Now, you can use your Hadoop or Apache Spark cluster and we will access and integrate this mainframe data without changing the format,” Yogurtçu said. The ability to work with  data without changing the format is necessary for compliance and governance, he added.  

According to Syncsort, the combination of the new capabilities allows customers to rapidly bring data in its raw form into a central Hadoop repository, supporting many downstream use cases and facilitating management of operational data in Hadoop.

With new support for Fujitsu NetCOBOL, Syncsort delivers these benefits for both IBM z Systems and Fujitsu mainframes, responding to strong demand in the Asia Pacific and CEMEA markets. “We are liberating that mainframe data by making it possible to access from this distributed emerging platforms,” Yogurtçu said.

Along with this latest release, Syncsort also introduced the new high-speed DMX Data FunneI, which allows customers to quickly ingest hundreds of database tables from sources such as DB2 at the push of a button.

For more information about this update, visit www.syncsort.com


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