Acerca de este Curso
4.7
1,623 ratings
•
346 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....
Stacks

Curso 4 de 5 en

Programa especializado Functional Programming in Scala
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Cursos 100 % en línea

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Fechas límite flexibles

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Clock

Sugerido: 6 hours/week

Aprox. 14 horas para completar
Comment Dots

English

Subtítulos: English

Habilidades que obtendrás

Scala ProgrammingBig DataApache SparkSQL
Stacks

Curso 4 de 5 en

Programa especializado Functional Programming in Scala
Globe

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Clock

Sugerido: 6 hours/week

Aprox. 14 horas para completar
Comment Dots

English

Subtítulos: English

Programa - Qué aprenderás en este curso

1

Sección
Clock
12 horas para completar

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 videos (Total: 105 min), 5 readings, 3 quizzes
Video7 videos
Introduction, Logistics, What You'll Learn15m
Data-Parallel to Distributed Data-Parallel10m
Latency24m
RDDs, Spark's Distributed Collection9m
RDDs: Transformation and Actions16m
Evaluation in Spark: Unlike Scala Collections!20m
Cluster Topology Matters!8m
Reading5 lecturas
Tools setup10m
Eclipse tutorial10m
Intellij IDEA Tutorial10m
Sbt tutorial10m
Submitting solutions10m

2

Sección
Clock
7 horas para completar

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 videos (Total: 59 min), 2 quizzes
Video4 videos
Reduction Operations14m
Pair RDDs6m
Transformations and Actions on Pair RDDs20m
Joins17m

3

Sección
Clock
1 hora para completar

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 videos (Total: 57 min)
Video4 videos
Shuffling: What it is and why it's important14m
Partitioning14m
Optimizing with Partitioners11m
Wide vs Narrow Dependencies16m

4

Sección
Clock
8 horas para completar

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 videos (Total: 133 min), 2 quizzes
Video5 videos
Structured vs Unstructured Data14m
Spark SQL17m
DataFrames (1)26m
DataFrames (2)30m
Datasets43m
4.7
Direction Signs

10%

comenzó una nueva carrera después de completar estos cursos
Briefcase

83%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
Money

12%

consiguió un aumento de sueldo o ascenso

Principales revisiones

por 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!

por 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.

Instructor

Dr. Heather Miller

Research Scientist
EPFL

Acerca de École Polytechnique Fédérale de Lausanne

Acerca del programa especializado 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

Preguntas Frecuentes

  • 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.

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