See it. Do it. Learn it! Spark in Motion teaches you to use Spark for big data analytics through high-quality video-based lessons and built-in exercises, so you can put what you learn into practice.
Spark in Motion teaches you how to use Spark for batch and streaming data analytics. In nearly 3 hours of hands-on video lessons, you'll get up and running with Spark, starting with the basic architecture of a Spark application. You'll explore data partitioning and accessing common application state, and then you'll deep-dive into using Spark SQL and dataframes for structured analytics. Finally, you'll use Spark Streaming to handle and process real-time data flowing into your application.
"Quick, no nonsense. What more can you wish?"
~ Jonathan Rioux, Senior Analyst
"Best course I have seen so far."
~ Peter J. Hampton, AI Researcher
"Spark is a very valuable library, but it's very hard to use (the learning step is very steep). This video course makes the learning smoother, and takes the users to a place where they can experiment by themselves."
~ Alberto Boschetti, Data Scientist
When you're doing analytics on big data systems, it can be a challenge to efficiently query, stream, filter, and consolidate data sharded across a cluster. Built especially for efficiently operating over large distributed datasets, the Spark data processing engine takes some of the weight off your shoulders. Spark features an easy-to-use interface, near-limitless upgrade potential, and performance that will knock your socks off. Spark simplifies your data infrastructure so you can focus on creating top-notch analytics.
Designed for a software engineer or architect, data scientist, or data analyst interested in getting started with Spark. No prior experience is needed.
Jason Kolter is an instructor for the University of Washington certificate program in Big Data Technologies. Additionally he has worked in a wide range of technology companies, gaining extensive experience leading teams building production large-scale distributed analytics systems.