Big Data Processing with Apache Spark

Big Data Processing with Apache Spark

Manuel Ignacio Franco Galeano, Nimish Narang
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Efficiently tackle large data sets and big data analysis challenges using Spark and Python
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Video Description

Processing big data in real time is challenging due to scalability, information consistency, and fault-tolerance. Big Data Processing with Apache Spark teaches you how to use Spark to make your overall analytical workflow faster and more efficient. You'll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming.

You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption.

By the end of this course, you’ll not only have understood how to use machine learning extensions and structured streams but you’ll also be able to apply Spark in your own upcoming big data projects.

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What You Will Learn

  • Write your own Python programs that can interact with Spark
  • Implement data stream consumption using Apache Spark
  • Recognize common operations in Spark to process known data streams
  • Integrate Spark streaming with Amazon Web Services
  • Create a collaborative filtering model with Python and the movielens dataset
  • Apply processed data streams to Spark machine learning APIs

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Video Details

ISBN 139781789953688
Course Length3 hours 30 minutes
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