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Building Structured Streaming Jobs in Spark

Course Overview
In this course, you will be introduced to Spark Structured Streaming. You will learn about data sources and data sinks and working with the Structured Streaming APIs. You will look at stream processing techniques such as windowing and aggregation functions, checkpointing and watermarking and their use in stream processing jobs. Finally, you will investigate fault tolerance in stream processing jobs.

Course Details
  • Author: Opsgility Trainer
  • Length: 0 hours, 0 minutes, 0 seconds
  • Lab: 0 hours, 0 minutes, 0 seconds
  • Published: 2018-07-30
  • Level: 300

Who this course is designed for
  • Data Engineers
  • Data Administrators
  • Data Scientists
  • Data Architects
  • Software Developers

Course Objectives

What You Will Learn
  • Understand how to use data sources and data sinks and working with the Structured Streaming APIs
  • Understand stream processing techniques such as windowing and aggregation functions, checkpointing and watermarking and their use in stream processing jobs
  • Understand fault tolerance in stream processing jobs.

Course Pre-Requisites

Prerequisites:
  • Familiarity with cloud computing concepts
  • Familiarity with Spark

Course Modules

Course Outline
In this course, you will be introduced to Spark Structured Streaming. You will learn about data sources and data sinks and working with the Structured Streaming APIs. You will look at stream processing techniques such as windowing and aggregation functions, checkpointing and watermarking and their use in stream processing jobs. Finally, you will investigate fault tolerance in stream processing jobs.

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