课程信息
4.7
1,683 个评分
355 个审阅
专项课程

第 4 门课程(共 5 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为15 小时

建议:6 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

Scala ProgrammingBig DataApache SparkSQL
专项课程

第 4 门课程(共 5 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为15 小时

建议:6 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 12 小时

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....
Reading
7 个视频 (总计 105 分钟), 5 个阅读材料, 3 个测验
Video7 个视频
Data-Parallel to Distributed Data-Parallel10分钟
Latency24分钟
RDDs, Spark's Distributed Collection9分钟
RDDs: Transformation and Actions16分钟
Evaluation in Spark: Unlike Scala Collections!20分钟
Cluster Topology Matters!8分钟
Reading5 个阅读材料
Tools setup10分钟
Eclipse tutorial10分钟
Intellij IDEA Tutorial10分钟
Sbt tutorial10分钟
Submitting solutions10分钟
2
完成时间(小时)
完成时间为 7 小时

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....
Reading
4 个视频 (总计 59 分钟), 2 个测验
Video4 个视频
Pair RDDs6分钟
Transformations and Actions on Pair RDDs20分钟
Joins17分钟
3
完成时间(小时)
完成时间为 1 小时

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....
Reading
4 个视频 (总计 57 分钟)
Video4 个视频
Partitioning14分钟
Optimizing with Partitioners11分钟
Wide vs Narrow Dependencies16分钟
4
完成时间(小时)
完成时间为 8 小时

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....
Reading
5 个视频 (总计 133 分钟), 2 个测验
Video5 个视频
Spark SQL17分钟
DataFrames (1)26分钟
DataFrames (2)30分钟
Datasets43分钟
4.7
355 个审阅Chevron Right
职业方向

10%

完成这些课程后已开始新的职业生涯
工作福利

83%

通过此课程获得实实在在的工作福利
职业晋升

12%

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热门审阅

创建者 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!

创建者 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.

讲师

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Dr. Heather Miller

Research Scientist
EPFL

关于 École Polytechnique Fédérale de Lausanne

关于 Functional Programming in Scala 专项课程

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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