Syncsort Updates Platform to Enable Integration of Streaming Data

Syncsort is adding new capabilities to its platform, including native integration with Apache Spark and Apache Kafka.

DMX-h v9 allows organizations to access and integrate enterprise-wide data with streams from real-time sources.

“Our latest release is important to us at Syncsort because, with it, we continue to help organizations solve the biggest challenges for integrating big data, and to achieve faster time to value,” said Tendü Yogurtçu, general manager of big data, Syncsort.

Modern data architectures have to manage more than growing data volumes, Yogurtçu explained; they must also adapt to integrate data from new and diverse sources inside and outside the organization – both batch and streaming, manage and secure the entire process, and keep current on the latest technology.

“Each of these areas is extremely complex in its own right – and when you put them together, the complexity grows exponentially” Yogurtçu said. “We are focused on delivering a way to significantly simplify the modern data architecture. DMX-h is a single software environment that streamlines all of these processes, and more.  It is extremely gratifying to see customers leverage our solutions in innovative new use cases and drive business value from their data more quickly.”

Additionally, the update simplifies Spark application development, allowing applications to leverage the increasing power of a rapidly evolving big data technology stack.

Syncsort’s integration with the Kafka distributed messaging system allows users to leverage DMX-h’s graphical interface to subscribe, transform, and enrich enterprise-wide data coming from real-time Kafka queues.

Now, DMX-h can also publish these enriched datasets to Kafka to simplify the creation of real-time analytical applications by cleansing, pre-processing, and transforming data in motion.

The latest release is important for organizations, such as financial, telecommunications, insurance, and healthcare industries, that need to easily gather, transform and distribute batch and streaming data from multiple data sources to perform advanced analytics in Hadoop and Spark.

“Users who want to migrate to new platforms – from standalone servers or from MapReduce to Spark – are particularly excited about Release 9,” Yogurtçu said. “The new capabilities in DMX-h enable them to take the same jobs initially designed for MapReduce – or Linux/Unix/Windows – and run them natively in Spark by simply choosing the execution framework from a drop-down menu, without needing to rewrite or recompile.” The other set of users seeing a big benefit from this new release are those investing in real-time analytics because they can leverage the new support of Kafka, said Yogurtçu.

For more information about this latest release, visit

Image courtesy of Shutterstock.


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