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自分のスケジュールですぐに学習を始めてください。

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スケジュールに従って期限をリセットします。

約15時間で修了

推奨:6 hours/week...

英語

字幕:英語

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Scala ProgrammingBig DataApache SparkSQL

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約15時間で修了

推奨:6 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
12時間で修了

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件のビデオ (合計105分), 5 readings, 3 quizzes
7件のビデオ
Latency24 分
RDDs, Spark's Distributed Collection9 分
RDDs: Transformation and Actions16 分
Evaluation in Spark: Unlike Scala Collections!20 分
Cluster Topology Matters!8 分
5件の学習用教材
Tools setup10 分
Eclipse tutorial10 分
Intellij IDEA Tutorial10 分
Sbt tutorial10 分
Submitting solutions10 分
2
7時間で修了

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件のビデオ (合計59分), 2 quizzes
4件のビデオ
Joins17 分
3
1時間で修了

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件のビデオ (合計57分)
4件のビデオ
Wide vs Narrow Dependencies16 分
4
8時間で修了

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.

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5件のビデオ (合計133分), 2 quizzes
5件のビデオ
Spark SQL17 分
DataFrames (2)30 分
Datasets43 分
4.7
388件のレビューChevron Right

14%

コース終了後に新しいキャリアをスタートした

18%

コースが具体的なキャリアアップにつながった

13%

昇給や昇進につながった

Big Data Analysis with Scala and Spark からの人気レビュー

by CCJun 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!

by CRApr 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.

講師

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Prof. Heather Miller

Assistant Professor
Carnegie Mellon University

スイス連邦工科大学ローザンヌ校(École Polytechnique Fédérale de Lausanne)について

Functional Programming in Scalaの専門講座について

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....
Functional Programming in Scala

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