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Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA
Jacob Parr

Jacob Parr
Apache Spark instructor, Databricks


Jacob Parr is the owner of JParr Productions, where he writes couseware, leads one-on-one training for companies like Databricks, Nike, Comcast, Cisco, AOL, and Moody’s Analytics, and speaks at conferences like Spark Summit. Jacob became interested in software development at the age of 11, and just two years later, he began programming his own video games—he’s been developing software ever since. Over his 20-year career, he has worked in software testing and test automation for Sierra On-Line (aka The ImagiNation Network aka AOL Entertainment), where he also developed software for Sierra Telephone, first as an engineer and eventually as an architect and senior developer, and in custom software development for websites, ecommerce systems, real-estate applications, and even the occasional enterprise tax consultant. His background includes telecommunications, billing systems, service order systems, trouble ticketing systems, and enterprise integration, and he has built everything from swing apps to monoliths to REST and microservices architecture. He participates in a number of open source projects. Jacob lives in Oakhurst, CA, with his lovely wife. As empty-nesters of three adult children, they enjoy spoiling their Boston terriers. He loves to play practical jokes, fly drones, chase his nephews and nieces with an arsenal of Nerf guns, and work on his n-scale train set. In his little spare time, he loves to (you guessed it) work on his pet software projects.


9:00am - 5:00pm Monday, March 13 & Tuesday, March 14
Spark & beyond
Location: 212 C
Secondary topics:  Streaming
Jacob Parr (Databricks)
The real power and value proposition of Apache Spark is in building a unified use case that combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. Jacob Parr employs hands-on exercises using various Wikipedia datasets to illustrate the variety of ideal programming paradigms Spark makes possible. Read more.