Big Data Analysis with Scala and Spark

About this course: 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: https://www.coursera.org/learn/parprog1.

Created by:  École Polytechnique Fédérale de Lausanne

• Taught by:  Dr. Heather Miller, Research Scientist

  EPFL

Basic Info Course 4 of 5 in the Functional Programming in Scala Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said 课程作业

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École Polytechnique Fédérale de Lausanne

Syllabus


WEEK 1


Getting Started + Spark Basics



Get up and running with Scala on your computer. Complete an example assignment to familiarize yourself with our unique way of submitting assignments. In this week, we'll bridge the gap between data parallelism in the shared memory scenario (learned in the Parallel Programming course, prerequisite) and the distributed scenario. We'll look at important concerns that arise in distributed systems, like latency and failure. We'll go on to cover the basics of Spark, a functionally-oriented framework for big data processing in Scala. We'll end the first week by exercising what we learned about Spark by immediately getting our hands dirty analyzing a real-world data set.


7 videos, 5 readings expand

1. 阅读: Tools setup
2. 阅读: Eclipse tutorial
3. 阅读: Intellij IDEA Tutorial
4. 阅读: Sbt tutorial
5. 阅读: Submitting solutions
6. 未分级程序开发: Example
7. Video: Introduction, Logistics, What You'll Learn
8. Video: Data-Parallel to Distributed Data-Parallel
9. Video: Latency
10. Video: RDDs, Spark's Distributed Collection
11. Video: RDDs: Transformation and Actions
12. Video: Evaluation in Spark: Unlike Scala Collections!
13. Video: Cluster Topology Matters!
14. 未分级程序开发: Wikipedia

Graded: Wikipedia

WEEK 2


Reduction Operations & Distributed Key-Value Pairs
This week, we'll look at a special kind of RDD called pair RDDs. With this specialized kind of RDD in hand, we'll cover essential operations on large data sets, such as reductions and joins.


4 videos expand

1. Video: Reduction Operations
2. Video: Pair RDDs
3. Video: Transformations and Actions on Pair RDDs
4. Video: Joins
5. 未分级程序开发: StackOverflow (2 week long assignment)

Graded: StackOverflow (2 week long assignment)

WEEK 3


Partitioning and Shuffling



This week we'll look at some of the performance implications of using operations like joins. Is it possible to get the same result without having to pay for the overhead of moving data over the network? We'll answer this question by delving into how we can partition our data to achieve better data locality, in turn optimizing some of our Spark jobs.


4 videos expand

1. Video: Shuffling: What it is and why it's important
2. Video: Partitioning
3. Video: Optimizing with Partitioners
4. Video: Wide vs Narrow Dependencies

WEEK 4


Structured data: SQL, Dataframes, and Datasets



With our newfound understanding of the cost of data movement in a Spark job, and some experience optimizing jobs for data locality last week, this week we'll focus on how we can more easily achieve similar optimizations. Can structured data help us? We'll look at Spark SQL and its powerful optimizer which uses structure to apply impressive optimizations. We'll move on to cover DataFrames and Datasets, which give us a way to mix RDDs with the powerful automatic optimizations behind Spark SQL.


5 videos expand

1. Video: Structured vs Unstructured Data
2. Video: Spark SQL
3. Video: DataFrames (1)
4. Video: DataFrames (2)
5. Video: Datasets
6. 未分级程序开发: Time Usage

Graded: Time Usage