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StreamSets Announces Support for New Microsoft SQL Server 2019 Big Data Clusters
StreamSets, provider of the industry's first DataOps platform, announced today support and platform integration for Microsoft's recently announced SQL Server 2019 Big Data Clusters. With this integration, SQL users are empowered to design and operationalize data pipelines for big data workloads without the complexities of coding for big data systems. 

Announced at Ignite conference last November, Microsoft SQL Server 2019 Big Data Clusters allows users to deploy scalable clusters of SQL Server, Apache Spark and HDFS containers running on Kubernetes. With StreamSets DataOps Platform's new capabilities for Big Data Clusters, SQL developers can accelerate their analytics use cases by:

  • Design and operate continuous data flows with intuitive, visual tools, eliminating the need to know how to code for big data systems
  • Develop complex analytics applications on Apache Spark using intuitive drag-and-drop user interface
  • Ingest and process data at scale from a wide variety of data sources with native integration into SQL Server and HDFS
  • Accelerate the migration from relational databases, Hadoop clusters and NoSQL stores into Big Data Clusters

"By building native support for Microsoft SQL Server 2019 Big Data Clusters, the StreamSets DataOps Platform is bridging big data and SQL Server use cases," said Jobi George, general manager, StreamSets Cloud. "We are excited to continue to bring innovative solutions to our joint Microsoft customers and empower them to deliver data faster and with confidence through our DataOps platform."

"StreamSets enables data professionals to leverage the advanced capabilities of our new SQL Server 2019 Big Data Clusters," said Asad Khan, director of Program Management, SQL Server at Microsoft. "With this support, StreamSets continues to demonstrate its value in helping joint customers quickly increase productivity with Big Data Clusters and accelerate critical business objectives."

Published Thursday, January 30, 2020 1:44 PM by David Marshall
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