Introduction to Spark In-memory Computing

Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. First developed in 2009, Spark provides several advantages over other big data and MapReduce technologies. With Spark, Hadoop clusters can run up to 100 times faster in memory, and 10 times faster when running on disk. With the ability to write applications in Java, Scala or even Python, Spark is a flexible and powerful paradigm.

With IBM's "Open for Data" cloud computing initiative, current and prospective users can provision an instance of Spark in the cloud in a matter of minutes without up-front infrastructure investment. The power of the cloud empowers users to scale up massively based on business needs. De-provisioning resources when those needs change is equally as agile, giving our users the best value for performance.

Please join us on Tuesday, June 28, 2016 at 12:00 noon as we discuss Spark both deployed as a service and on premise. After an introduction to Spark, we'll talk about how to decide if your project is well suited for processing in the cloud. Finally we'll perform a brief demonstration. Food and beverages will be provided. Seats are limited, so sign up today!

Presenter: Andrew Levine