Real-ish Time Predictive Analytics with Spark Structured Streaming

Abstract: In this workshop we will dive deep into what it takes to build and deliver an always-on "real-ish time" predictive analytics pipeline with Spark Structured Streaming.

The core focus of the workshop material will be on how to solve a common complex problem in which we have no labeled data in an unbounded timeseries dataset and need to understand the substructure of said chaos in order to apply common supervised and statistical modeling techniques to our data in a streaming fashion.

The example problem for the workshop will come from the telecommunications space but the skills you will leave with can be applied to almost any domain as long as you sprinkle in a little creativity and inject a bit of domain knowledge.

Skills Acquired:
1. Structured Streaming experience with Apace Spark.
2. Understand how to use supervised modeling techniques on unsupervised data (caveat: requires some domain knowledge and the good ol human touch).
3. Have fun for 90 minutes.

Bio: Scott Haines is a Principal Software Engineer / Tech Lead on the Voice Insights team at Twilio. His focus has been on the architecture and development of a real-time (sub 250ms), highly available, trust-worthy analytics system. His team is providing near real-time analytics that processes / aggregates and analyzes multiple terabytes of global sensor data daily. Scott helped drive Apache Spark adoption at Twilio and actively teaches and consulting teams internally. Scott’s past experience was at Yahoo! where he built a real-time recommendation engine and targeted ranking / ratings analytics which helped serve personalized page content for millions of customers of Yahoo Games. He worked to build a real-time click / install tracking system that helped deliver customized push marketing and ad attribution for Yahoo Sports and lastly Scott finished his tenure at Yahoo working for Flurry Analytics where he wrote the an auto-regressive smart alerting and notification system which integrated into the Flurry mobile app for ios/android.