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.
What’s Next
To stay abreast with what’s happening with Apache Spark, follow us on Twitter @databricks and visit SparkHub.
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