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Big Data Analysis with Scala and Spark
  • Level Professional
  • Duration 28 hours
  • Course by École Polytechnique Fédérale de Lausanne
  • Offered by

About

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. Learning Outcomes. By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Parallel Programming

Modules

Getting Started

    • 7 Readings
    • 1 Programming
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1 Programming

  • Example

7 Readings

  • Working on Assignments
  • Tools Setup (Please read)
  • Scala 3 REPL and Worksheets
  • Cheat Sheet
  • SBT tutorial and Submission of Assignments (Please read)
  • Learning Resources
  • Scala Style Guide

From Parallel to Distributed

    • 3 Videos
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3 Videos

  • Introduction, Logistics, What You'll Learn
  • Data-Parallel to Distributed Data-Parallel
  • Latency

Basics of Spark's RDDs

    • 4 Videos
    • 2 Programming
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2 Programming

  • Wikipedia (audit)
  • Wikipedia

4 Videos

  • RDDs, Spark's Distributed Collection
  • RDDs: Transformation and Actions
  • Evaluation in Spark: Unlike Scala Collections!
  • Cluster Topology Matters!

Reduction Operations & Distributed Key-Value Pairs

    • 4 Videos
    • 2 Programming
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2 Programming

  • StackOverflow (2 week long assignment) (audit)
  • StackOverflow (2 week long assignment)

4 Videos

  • Reduction Operations
  • Pair RDDs
  • Transformations and Actions on Pair RDDs
  • Joins

Partitioning and Shuffling

    • 4 Videos
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4 Videos

  • Shuffling: What it is and why it's important
  • Partitioning
  • Optimizing with Partitioners
  • Wide vs Narrow Dependencies

SQL, Dataframes, and Datasets

    • 5 Videos
    • 2 Programming
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2 Programming

  • Time Usage (audit)
  • Time Usage

5 Videos

  • Structured vs Unstructured Data
  • Spark SQL
  • DataFrames (1)
  • DataFrames (2)
  • Datasets

Auto Summary

Discover the power of big data analysis with "Big Data Analysis with Scala and Spark." This professional-level course, taught by Coursera, dives into manipulating large datasets using functional programming concepts. Focused on Apache Spark and Scala, you'll learn to read, manipulate, and analyze data efficiently. Ideal for learners with programming experience, it covers critical aspects like avoiding shuffles and recomputation. The course spans 1680 minutes and offers a starter subscription. Perfect for IT and computer science professionals looking to enhance their skills in big data.

Prof. Heather Miller
Instructor

Prof. Heather Miller