The virtual course "Big Data Analysis with Scala and Spark - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 28 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Manipulation of distributed big data in a cluster using functional concepts is widespread 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 more recently Apache Spark, a fast in-memory distributed collection framework written in Scala.
In this course, we'll see how the parallel data paradigm can be extended to the distributed case, using Spark throughout.
We will cover Spark's programming model in detail, taking care to understand how and when it differs from familiar programming models such as shared-memory parallel collections or Scala's sequential collections.
Through practical examples in Spark and Scala, we can learn when to consider important distribution-related issues, such as latency and network communication, and how they can be effectively addressed to improve performance.
The learning outcomes.
At 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 merges and recomputation in Spark , Recommended Background: Must have at least one year of programming experience.
Mastery of Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript, or Ruby is also sufficient.
You should be familiar with using the command line.
This course is designed to be taken after parallel programming: https://www.
coursera.
org/learn/parprog1.
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Federal Polytechnic School of Lausanne
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