Beyond Shuffling & Streaming Preview, by Holden Karau from IBM Spark Tech Center

Next week is the Apache Big Data 2016 (http://events.linuxfoundation.org/events/apache-big-data-europe) in Sevilla and we are delighted to welcome one of their speakers that will stop by in Barcelona, Holden Karau (http://www.holdenkarau.com/).

She's not only an active contributor (https://github.com/apache/spark/commits?author=holdenk), but also co-author of several books on Spark such as Learning Spark (https://www.amazon.es/Learning-Spark-Lightning-Fast-Data-Analysis/dp/1449358624) and High Performance Spark (http://shop.oreilly.com/product/0636920046967.do).

We look forward to seeing you all there!

Title:

Beyond Shuffling & Streaming Preview

Abstract:

This talk walks through a number of common mistakes which can keep our Spark programs from scaling and examines the solutions, as well as general techniques useful for moving from beyond a prof of concept to production. It covers topics like effective RDD re-use, considerations for working with key/value data, and finishes up with a an introduction to Datasets with Structured Streaming (new in Spark 2.0) and how to do weird things with them.

Bio:

Holden Karau is transgender Canadian, an active open source contributor, and co-author of Learning Spark & High Performance Spark. When not in San Francisco working as a software development engineer at IBM's Spark Technology Center, Holden talks internationally on Spark and holds office hours at coffee shops at home and abroad. She makes frequent contributions to Spark, specializing in PySpark and Machine Learning. Prior to IBM she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science. Outside of software she enjoys playing with fire, welding, scooters, poutine, and dancing.