Ce cours fait partie de la Spécialisation Functional Programming in Scala
Big Data Analysis with Scala and Spark
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À propos de ce cours
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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.
Compétences que vous acquerrez
Scala ProgrammingBig DataApache SparkSQL
Programme du cours : ce que vous apprendrez dans ce cours
1
Section
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 vidéos (Total 105 min), 5 lectures, 3 quiz
7 vidéos
Data-Parallel to Distributed Data-Parallel10 min
Latency24 min
RDDs, Spark's Distributed Collection9 min
RDDs: Transformation and Actions16 min
Evaluation in Spark: Unlike Scala Collections!20 min
Cluster Topology Matters!8 min
5 lectures
Tools setup10 min
Eclipse tutorial10 min
Intellij IDEA Tutorial10 min
Sbt tutorial10 min
Submitting solutions10 min
2
Section
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 vidéos (Total 59 min), 2 quiz
4 vidéos
Reduction Operations14 min
Pair RDDs6 min
Transformations and Actions on Pair RDDs20 min
Joins17 min
3
Section
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 vidéos (Total 57 min)
4 vidéos
Partitioning14 min
Optimizing with Partitioners11 min
Wide vs Narrow Dependencies16 min
4
Section
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 vidéos (Total 133 min), 2 quiz
5 vidéos
Spark SQL17 min
DataFrames (1)26 min
DataFrames (2)30 min
Datasets43 min
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Meilleurs avis
par CC•Jun 8th 2017
The sessions where clearly explained and focused. Some of the exercises contained slightly confusing hints and information, but I'm sure those mistakes will be ironed out in future iterations. Thanks!
par CR•Apr 10th 2017
Great introduction to spark. Fun assignments. Since it was the first ever session, there were quite a few kinks with the assignments. But the discussion forums rescued me any time I was stuck.
Enseignant
Dr. Heather Miller
Research ScientistEPFL
À propos de École Polytechnique Fédérale de Lausanne
À propos de la Spécialisation Functional Programming in Scala
Discover how to write elegant code that works the first time it is run.
This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. It begins from the basic building blocks of the functional paradigm, first showing how to use these blocks to solve small problems, before building up to combining these concepts to architect larger functional programs. You'll see how the functional paradigm facilitates parallel and distributed programming, and through a series of hands on examples and programming assignments, you'll learn how to analyze data sets small to large; from parallel programming on multicore architectures, to distributed programming on a cluster using Apache Spark. A final capstone project will allow you to apply the skills you learned by building a large data-intensive application using real-world data.
Foire Aux Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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