What's new in SAP Data Hub 2.4?
Introduced in 2017, SAP Data Hub is the all-in-one data orchestration solution discovers, refines, enriches, and governs any type, variety, and volume of data across your entire distributed data landscape. It supports your intelligent enterprise by rapidly delivering trustworthy data to the right users with the right context at the right time.
SAP Data Hub 2.4 was released in Janaury 2019. While this is an incremental release, following the Introduction of SAP Data Hub 2.3 major release, it is more than just corrections and bug fixes. There are several new features and key enhancements which provide greater flexibility and more protection for customers.
What's new in SAP Data Hub 2.4:
Extending native connectivity to support more databases and applications Today, SAP Data Hub already provides a broad spectrum of connectivity to big data and enterprise sources. As integration remains a building block for the digital transformation, our top priorities is to continuously grow the native connectivity with more enterprise applications.
In this release, SAP added direct integration with several structured data sources including MS SQL Server, MySQL, IBM DB2, and Google BigQuery. Once a connection is established, SAP Data Hub will automatically crawl the metadata for these connected sources. You can then browse, view, profile, catalog, and share the data directly within the Metadata Explorer. In addition, there are more than 350 predefined operators and data pipelines that already exist ready to be used for supporting broader scenarios.
Enabling Data Lineage for disparate & distributed data sources SAP introduced the SAP Data Hub Metadata Explorer in the previous release. The goal is to provide a centralized location for all data professionals to gain insights on diverse datasets in today’s modern distributed landscape. This release we are increasing our investment in metadata governance by offering end-to-end support for data lineage at the schema level. You can use the new lineage analysis feature to view a graphical representation of the source, transformations, and dependencies of a dataset. Lineage information can be extracted from computed datasets such as SQL View, and other types of computations including stored procedures, BW transformations, datastores, and the Data Hub pipeline modeler.