Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
Holden Karau

Holden Karau
Software Engineer, Independent

@holdenkarau

Holden Karau is a transgender Canadian software engineer working in the bay area. Previously, she worked at IBM, Alpine, Databricks, Google (twice), Foursquare, and Amazon. Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date. She’s a committer on the Apache Spark, SystemML, and Mahout projects. When not in San Francisco, Holden speaks internationally about different big data technologies (mostly Spark). She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal. Outside of work, she enjoys playing with fire, riding scooters, and dancing.

Sessions

17:25–18:05 Wednesday, 23 May 2018
Understanding Spark tuning with auto-tuning; or, Magical spells to stop your pager going off at 2:00am
Data engineering and architecture, Streaming systems and real-time applications
 Location: S11B Level: Intermediate
Holden Karau (Independent), Rachel B Warren (Salesforce Einstein)
Average rating: ****.
(4.00, 2 ratings)
Apache Spark is an amazing distributed system, but part of the bargain we've made with the infrastructure deamons involves providing the correct set of magic numbers (aka tuning) or our jobs may be eaten by Cthulhu. Holden Karau, Rachel Warren, and Anya Bida explore auto-tuning jobs using systems like Apache BEAM, Mahout, and internal Spark ML jobs as workloads. Read more.