sap hana platform sps 11 introduces new sap hana hadoop integration features

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SAP HANA Platform SPS 11 introduces new SAP HANA Hadoop integration features. SAP HANA Spark Controller 1.5 offers improved performance and broader SQL support. In addition, you can now set up: Username and password authentication in SAP HANA Spark Controller Mutual SSL authentication between SAP HANA and SAP HANA Spark Controller SAP HANA Platform SPS 11 extends SAP HANA Hadoop infrastructure by integrating with Data Lifecycle Manager and SAP HANA Vora Data Lifecycle Manager SAP HANA Data Warehousing Foundation Data Lifecycle Manager supports bi-directional data relocation, allowing you to relocate data in native SAP HANA use cases from SAP HANA persistency to storage locations including Hadoop (Spark SQL) and from Hadoop (Spark SQL) to SAP HANA. ( SAP HANA Vora SAP HANA Vora provides an in-memory processing engine that runs on a Hadoop cluster and Spark execution framework. Able to scale to thousands of nodes, it is designed for use in large distributed clusters and for handling big data.)

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Post on 11-Apr-2017

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SAP HANA Platform SPS 11 introduces new SAP HANA Hadoop integration features.

SAP HANA Spark Controller 1.5 offers improved performance and broader SQL support. In addition, you can now set up:

Username and password authentication in SAP HANA Spark Controller Mutual SSL authentication between SAP HANA and SAP HANA Spark Controller SAP HANA Platform SPS 11 extends SAP HANA Hadoop infrastructure by integrating with Data

Lifecycle Manager and SAP HANA Vora Data Lifecycle Manager SAP HANA Data Warehousing Foundation Data Lifecycle Manager

supports bi-directional data relocation, allowing you to relocate data in native SAP HANA use cases from SAP HANA persistency to storage locations including Hadoop (Spark SQL) and from Hadoop (Spark SQL) to SAP HANA. ( SAP HANA Vora SAP HANA Vora provides an in-memory processing engine that runs on a Hadoop cluster and Spark execution framework. Able to scale to thousands of nodes, it is designed for use in large distributed clusters and for handling big data.)