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This presentation explores how SQL developers can deliver powerful machine learning applications by leveraging Spark's SQL and MLlib libraries. A brief overview covering Spark components and architecture kicks things off, and then we dive right in with a live demonstration of loading and querying data using Spark SQL. Next, we'll examine the basics of machine learning algorithms and workflows before getting under the hood of a Spark MLlib-based recommendation engine. Our final demonstration looks at how familiar tools can be used to query our recommendation data before we wrap up with a survey of real-world use cases.
This presentation is cross-posted on GitHub here: https://github.com/crwarman/SparkSqlAndMachineLearning