Mixing Analytic Workloads with Greenplum and Apache Spark

August 16, 2018
Apache Spark is a popular in-memory data analytics engine because of its speed, scalability, and ease of use. It also fits well with DevOps practices and cloud-native software platforms. It’s good for data exploration, interactive analytics, and streaming use cases. However, Spark, like other data-processing platforms, is not one size fits all. Different versions of Spark support different feature sets, and Spark’s machine-learning libraries can also vary in important ways between versions, or may lack the right algorithm. In this webinar, you’ll learn: - How to integrate data warehouse workloads with Spark - Which workloads are better for Greenplum and for Spark - How to use the Greenplum-Spark connector Presenter: Kong Yew Chan, Product Manager, Pivotal
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