Acerca de este Curso
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Aprox. 15 horas para completar

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Habilidades que obtendrás

Scala ProgrammingBig DataApache SparkSQL

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Aprox. 15 horas para completar

Sugerido: 6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

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.

7 videos (Total 105 minutos), 5 readings, 3 quizzes
7 videos
Data-Parallel to Distributed Data-Parallel10m
RDDs, Spark's Distributed Collection9m
RDDs: Transformation and Actions16m
Evaluation in Spark: Unlike Scala Collections!20m
Cluster Topology Matters!8m
5 lecturas
Tools setup10m
Eclipse tutorial10m
Intellij IDEA Tutorial10m
Sbt tutorial10m
Submitting solutions10m
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.

4 videos (Total 59 minutos), 2 quizzes
4 videos
Pair RDDs6m
Transformations and Actions on Pair RDDs20m
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.

4 videos (Total 57 minutos)
4 videos
Optimizing with Partitioners11m
Wide vs Narrow Dependencies16m
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.

5 videos (Total 133 minutos), 2 quizzes
5 videos
Spark SQL17m
DataFrames (1)26m
DataFrames (2)30m
388 revisionesChevron Right


comenzó una nueva carrera después de completar estos cursos


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


consiguió un aumento de sueldo o ascenso

Principales revisiones sobre Big Data Analysis with Scala and Spark

por 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!

por 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.



Prof. Heather Miller

Assistant Professor
Carnegie Mellon University

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

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