Databricks Bi-Weekly Digest: 8/31/16

Published: August 31, 2016

Open SourceLess than a minute

Here’s our recap of what’s transpired with Apache Spark since our previous digest.

Databricks CTO and Co-founder Matei Zaharia presented “Unifying big data workloads in Apache Spark” at @Scale Conference.
Databricks CTO and Co-founder Matei Zaharia talked with SiliconAngle/theCUBE about How Apache Spark is transforming apps with data streaming.
Tim Hunter released two Apache Spark packages: GraphFrames Package and scikit-learn package 0.2.0 (with support for fitting keyed models—e.g., fitting 1 model per customer).
Tim Hunter presented at the Bay Area Spark Meetup @ Salesforce: TensorFrames on Google’s TensorFlow and Apache Spark.
Joseph Bradley answered on SiliconAngle/theCUBE: Can Apache Spark do for machine learning what it’s has done for data?
Spark Community Evangelist Jules Damji blogged on How to use SparkSession in Apache Spark 2.0.
Pavel Hardak of Basho demonstrated the Riak Integration and connector to Apache Spark 2.0
Burak Yavuz showed in a webinar how Apache Spark is used to simplify log ETL Pipeline.
Michael Armbrust explained aspects of declarative programming on SiliconAngle/theCUBE: Just do it: Declarative programming for simplifying data queries.
Databricks VP of Engineering Patrick Wendell interviewed by SiliconAngle/theCUBE: Double-team: The software/hardware sandwich that’s taking data up a level.