Last month, MapR announced the integration of Apache Spark into the MapR platform, becoming the first and only Hadoop distribution to support the complete Spark stack, including Spark, Spark Streaming, Shark, MLLib, and GraphX.
MapR embarked on this integration with its customers in mind. During meetings with the MapR team over the past few months, customers expressed increased interest in Spark; some were interested in the advantages of this new open source project, while others were already running Spark in production clusters. The integration of Spark and MapR reflects the ongoing commitment of the company to its customers and their priorities and now that the integration is complete, customers can expect 24x7 support on all Spark projects.
In addition, MapR and Databricks (the company spearheading Spark) have combined forces to develop the roadmap and accelerate innovation in these open source projects. This will benefit not only MapR customers, but also the broader Hadoop community in the coming years, starting with the upcoming release of Apache Spark 1.0.
While a consensus is emerging that Apache Spark provides unprecedented value when it comes to fast, iterative, in-memory processing, there is still more to discover about Spark. How has it earned this reputation and why has its success spread like wildfire?
To answer these questions, MapR has set up a 10-city U.S. tour over the next few weeks. MapR architects and data scientists will explain what Apache Spark is and what it brings to the Hadoop ecosystem. They will also explain in detail why Spark has lightning-fast results, how its jobs require as little as 1/5th of the lines of code normally required, how developers can build applications more easily in the programming language of their choice (including Java, Scala and Python), and much more.
Learn for yourself! Join us at one of the following meetups:
- May 13th – Westlake Village, CA
Westlake Village Data Science (http://bit.ly/1ldyDtF)
- May 20th– St. Louis, MO
St. Louis Hadoop Users Group (http://bit.ly/1iIRpaM)
- May 28th – Menlo Park, CA
Real-time Big Data (http://bit.ly/1jDCGDo)
- June 11th – Houston, TX
Houston Hadoop Users Group (http://bit.ly/1joIdbO)
- June 11th – Salt Lake City, UT
Utah Hadoop Users Group (http://bit.ly/1mSluNB)
- June 18th – Chicago, IL
Chicago Area Hadoop User Group (http://bit.ly/1nF8uu4)
- June 19th – Portland, OR
Portland Big Data User Group (http://bit.ly/1sJHTf0)
- July 9th – Atlanta, GA
Atlanta Hadoop Users Group (http://bit.ly/1mVfaR7)
- July 10th – Kansas City, KS
Kansas City Big Data Group (http://bit.ly/1mmPj63)
- July 16th – Los Angeles, CA
Los Angeles Big Data Users Group (http://bit.ly/1v07QsR)
In the meantime, learn more about the features and benefits of Apache Spark here.