Jamie has worked in consulting since 1994, with top firms including Price Waterhouse and Chariot Solutions. He has a long track record of working closely with clients to build high quality, mission critical systems that scale to meet the needs of their businesses, and has worked in myriad industries including automotive, retail, pharmaceuticals, telecommunications and more. Jamie has been coding in Scala and actor based systems since 2009, and is the author of "Effective Akka" book from O'Reilly.
Spark Overview for Scala Analytics
On-line reviews show the preference to use Scala with Spark vs Python for enterprise scale big data analytical platforms. Why is that? This question will be addressed in this course as well as many others that will guide your use of this program to address your need for speed.Start the Free Course
About This Course
Learn the history of Apache Spark™, how it came to be, how to build applications with Spark, how establish an understanding of RDDs and DataFrames, and other advanced Spark topics.
- Be prepared to leverage the core RDD and DataFrame APIs to perform analytics on datasets with Scala.
- Get an overview of Spark and its associated ecosystem.
- Gain enough skills to leverage the Map-Reduce framework with the Scala language.
- Module 1 - What is Spark?
- Module 2 - Introduction to RDDs
- Module 3 - Introduction to DataFrames
- Module 4 - Advanced Spark Topics
- Module 5 - Introduction to Spark MLlib
- This course is free.
- It is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
Recommended skills prior to taking this course
- Experience with Java (preferred), Python, or another object-oriented language.
- No previous Spark knowledge is required.
- No previous experience with Data Science concepts is required. These concepts will be explained as needed.
- Taken the Scala 101 course.