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E13 - SparkSQL for DB2z Scientists

Session Number: 3403
Track: App Dev
Session Type: Podium Presentation
Primary Presenter: RAVI KALYANASUNDARAM KUMAR [IBM]
Room(s)/
Time(s):

San Antonio => Wed, May 25, 2016 (03:30 PM - 04:30 PM)

Speaker Bio: Ravi has 24 years of I/T experience. He is currently working as a Senior Managing Consultant at IBM Analytics Platform -Lab Services division, providing technical leadership and consulting services to clients world wide.
He also co-authored the following redbooks:
1. Optimizing Restore and Recovery Solutions with DB2 Recovery Expert for z/OS v2.1
2. DB2 9 for z/OS Resource Serialization and Concurrency Control
3. DB2 10 for z/OS Performance Topics
4. Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS V2.1
5. Hybrid Analytics IBM DB2 Analytics Accelerator for z/OS V3.1
6. DB2 11 for z/OS Technical Overview
7. Reliability and Performance with IBM DB2 Analytics Accelerator V4.1
8. Enabling real-time analytics on IBM z Systems
9. Apache Spark on z/OS Implementation (to be published)
Audience experience level: Beginner, Intermediate
Presentation Category: Data Management, Emerging Technology, Big Data
Presentation Platform: Select a Value
Audiences this presentation will apply to: Application Developers, Data Architects, Database Administrators, Systems Programmers, New Users, IT Managers
Technical areas this presentation will apply to: Database Performance (DB2 for z/OS)
If Tools and Utilities was selected which products: SPSS and IBM Analytics Accelerator. Primarily Apache Spark on z/OS
Objective 1: Introduce Apache Spark SQL
Objective 2: Introduce Data Analytics on z systems with DB2 for z/OS data along with IDAA, SPSS and Spark on z/OS
Objective 3: Introduce Apache Spark SQL usage scenarios - The Good, Bad, and the Ugly.

Abstract:  This session demonstrates the conceptual use of Apache Spark SQL and Analytics on z/OS to DB2 for z/OS developers and Database Administrators. It shows how IDAA, SPSS, Apache Spark SQL and ML Libraries can all be leveraged to conduct more data science experiments in less time and build predictive models with operational real-time and historical DB2 for z/OS data - optionally joined with other non-relational data.

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