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Join us for this webinar series as we discuss how to implement data management strategies for a Big Data-enabled Enterprise Data Warehouse

Big data represents a significant paradigm shift in today’s enterprise technology. Big data has fundamentally changed the nature of data management, introducing new challenges with the volume, velocity and variety of corporate data. This change is driving organizations to adjust their enterprise data warehousing technologies and strategies in order to turn the massive amounts of data into valuable and actionable information. Big data enables companies to gain new insight into business opportunities, transforming enterprises for the new real-time world.

SAP understands the importance of Big Data but we also understand that you can’t take of advantage of it without a data-management platform to help find relevancies within your data to turn them into business processes – essentially turning your big data into a key enterprise asset. See how by tying together your organization’s data assets – from operational data to external feeds and Big Data – SAP dramatically simplifies data management landscapes for both current and next-generation business applications, delivering information at unprecedented speeds and empowering a Big Data-enabled Enterprise Data Warehouse.

November 12th 12:00PM -1:00 PM (ET) Presented by: Courtney Claussen, Director Product Management

SAP recognizes that not all data in your enterprise will exist in your SAP data warehouse, and that there are also different processing environments for handling big data that the SAP data-management platform needs to interact with. In this session learn how to utilize Big Data for interesting insights into your business, using the SAP data platform and Hadoop.  We will show how these solutions have been engineered to work together through data federation and data integration methods for a Big Data-enabled enterprise data warehouse.

Increasingly, data sources are able to deliver data in real-time. How do you collect data that is arriving continuously at very high speeds, and in a way that it is most useful? Even more importantly, how can you extract insight from that data and respond as things happen, rather than only being able to respond much later, once you’ve had a chance to analyze the historical data?  See how event stream processing adds critical capabilities to your big data architecture.

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