Об этом курсе
4.7
1,587 ratings
340 reviews
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....
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 5 hours/week

Прибл. 14 ч. на завершение
Comment Dots

English

Субтитры: English

Приобретаемые навыки

Scala ProgrammingBig DataApache SparkSQL
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 5 hours/week

Прибл. 14 ч. на завершение
Comment Dots

English

Субтитры: English

Программа курса: что вы изучите

1

Раздел
Clock
12 ч. на завершение

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 видео (всего 105 мин.), 5 материалов для самостоятельного изучения, 3 тестов
Video7 видео
Data-Parallel to Distributed Data-Parallel10мин
Latency24мин
RDDs, Spark's Distributed Collection9мин
RDDs: Transformation and Actions16мин
Evaluation in Spark: Unlike Scala Collections!20мин
Cluster Topology Matters!8мин
Reading5 материала для самостоятельного изучения
Tools setup10мин
Eclipse tutorial10мин
Intellij IDEA Tutorial10мин
Sbt tutorial10мин
Submitting solutions10мин

2

Раздел
Clock
7 ч. на завершение

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 видео (всего 59 мин.), 2 тестов
Video4 видео
Pair RDDs6мин
Transformations and Actions on Pair RDDs20мин
Joins17мин

3

Раздел
Clock
1 ч. на завершение

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 видео (всего 57 мин.)
Video4 видео
Partitioning14мин
Optimizing with Partitioners11мин
Wide vs Narrow Dependencies16мин

4

Раздел
Clock
8 ч. на завершение

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 видео (всего 133 мин.), 2 тестов
Video5 видео
Spark SQL17мин
DataFrames (1)26мин
DataFrames (2)30мин
Datasets43мин
4.7
Direction Signs

10%

начал новую карьеру, пройдя эти курсы
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу
Money

12%

стал больше зарабатывать или получил повышение

Лучшие рецензии

автор: 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!

автор: 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.

Преподаватель

Dr. Heather Miller

Research Scientist
EPFL

О École Polytechnique Fédérale de Lausanne

О специализации ''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....
Functional Programming in Scala

Часто задаваемые вопросы

  • 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.

  • 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.

Остались вопросы? Посетите Центр поддержки учащихся.