Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance.
Offered By


Big Data Analysis with Scala and Spark (Scala 2 version)
École Polytechnique Fédérale de LausanneAbout this Course
3,022 recent views
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 27 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- Scala Programming
- Big Data
- Apache Spark
- SQL
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 27 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
12 hours to complete
Getting Started + Spark Basics
12 hours to complete
7 videos (Total 105 min), 6 readings, 3 quizzes
7 hours to complete
Reduction Operations & Distributed Key-Value Pairs
7 hours to complete
4 videos (Total 59 min)
1 hour to complete
Partitioning and Shuffling
1 hour to complete
4 videos (Total 57 min)
8 hours to complete
Structured data: SQL, Dataframes, and Datasets
8 hours to complete
5 videos (Total 133 min)
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I purchase the Certificate?
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.