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.
このコースはFunctional Programming in Scala の専門講座に属します。
Big Data Analysis with Scala and Spark
提供：
このコースについて
受講生の就業成果
11%
15%
12%
習得するスキル
受講生の就業成果
11%
15%
12%
100%オンライン
次における5の4コース
柔軟性のある期限
約15時間で修了
英語
提供：
スイス連邦工科大学ローザンヌ校（École Polytechnique Fédérale de Lausanne）
シラバス - 本コースの学習内容
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.
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.
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.
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.
レビュー
4.7
BIG DATA ANALYSIS WITH SCALA AND SPARK からの人気レビュー
Excellent overview of Spark, including exercises that solidify what you learn during the lectures. The development environment setup tutorials were also very helpful, as I had not yet worked with sbt.
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!
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.
Very Nice and effective course. One of the best course i have done on Spark online. Many Thanks to the course instructor Heather Miller for creating a very detail and updated course on Spark.
goot as introduction about spark and big data.\n\nSmall notice: it is incorrect to compare performance hadoop and spark. As I understand, spark was expected to be compacred with MapReduce.
Good overview of the subject, covering all important aspects. Assignments were well prepared, with a couple of unclear points that were quickly discovered and explained on the forums.
The course starts from the basic concepts and moves towards the complex concepts. The most important thing is that minute details are taken into consideration and explained properly.
Excellent course! It's clear the instructor put a ton of thought and hard work into this. I learned a lot that I wouldn't have learned without taking this class. Thank you, Heather!
Awesome course and awesome teacher! Nevertheless, to grasp the most of this course, you should do the previous 3 courses of the "Functional Programming in Scala" specialization.
Excellent material. Very good flow. Heather has an amazing way of walking through the flow and simplifying the concepts. Great assignments -- takes a bit longer than 3 hours.
The exercises were below the standard of previous courses. Also the instructions on exercises could have been better. Lost a lot of time figuring out as a new bee in Spark.
the theory is very clear and well explained.\n\nthe practical assignments are a little bit ambiguous but they are overall very good and challenging.\n\nhighly recommended!
Great course about Big Data analysis\n\nIt was my first exposure to Big Data frameworks and I learned a lot about the problems trying to be solved and the power of Spark.
Great course with nice explanations of some Spark concepts. The third week was particularly useful for my understanding of Spark shuffling and partitioning. Thanks a lot!
Great introduction to Spark and it's data structures. The course is easy to follow, and lecturer is entertaining and really engaged.\n\nThanks, I really had fun !
although spark part is taught nicely, it also takes a lot of time to understand the sql part and remember a lot of sql operations as a zero background man in sql
It was really useful material. It would be really nice if there are more assignments to polish the materials we learn, but I am really satisfied with the course.
Great course to get going with Apache Spark. Would recommend to someone who has java or scala experience already and wants to learn about distributed processing.
Great course, I learned a lot through the course. However, some of the lectures are quite long and could do with being broken down in to more smaller segments.
I worked with PySpark professionally, and this helped add some depth to my knowledge of Spark as well as give me a chance to translate those skills to Scala.
Functional Programming in Scala専門講座について
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