Structured Streaming is new in Apache Spark 2.0, and work is being done to integrate the machine-learning interfaces with this new streaming system. Holden Karau and Seth Hendrickson look at the current state of Structured Streaming and machine learning before walking you through creating your own streaming model. Holden and Seth will also cover how to use structured machine-learning algorithms (if they are merged by the talk). By the end of this session, you’ll have a better understanding of Spark’s Structured Streaming API as well as how machine learning works in Spark.
Holden Karau is a software development engineer at IBM and is active in open source. Previously, she worked on a variety of big data, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. Holden is the author of Learning Spark and has assisted with Spark workshops. She holds a bachelor of mathematics in computer science from the University of Waterloo.
Seth Hendrickson is a top Apache Spark contributor and data scientist at Cloudera. He implemented multinomial logistic regression with elastic-net regularization in Spark’s ML library and one-pass elastic-net linear regression, contributed several other performance improvements to linear models in Spark, and made extensive contributions to Spark ML decision trees and ensemble algorithms. Previously, he worked on Spark ML as a machine-learning engineer at IBM. He holds an MS in electrical engineering from the Georgia Institute of Technology.
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