Streaming analytics can be a difficult to set up, especially when working with late data arrivals and other variables. In this course, Modeling Streaming Data for Processing with Apache Spark Structured Streaming, you’ll learn to model your data for real-time analysis. First, you’ll explore applying batch processing to streaming data. Next, you’ll discover aggregating and outputting data. Finally, you’ll learn how to late arrivals and job failures. When you’re finished with this course, you’ll have the skills and knowledge of Spark Structured Streaming needed to combine your batch and streaming analytics jobs.
Author Name: Eugene Meidinger
Author Description:
Eugene Meidinger works as an independent BI consultant and Pluralsight author, specializing in Power BI and the Azure Data Platform. He has been working with data for over 8 years and speaks regularly at user groups and conferences. He also helps run the GroupBy online conference.

Table of Contents

• Course Overview
  1min
• Comparing Batch and Stream Processing
  24mins
• Understanding Structured Streaming
  17mins
• Grouping and Aggregating Data
  17mins
• Handling Late Arrivals and Failures
  18mins