Faust: Stream Processing Using Windowing Operations

Faust 1.10
  • 13 Videos | 1h 28m 51s
  • Includes Assessment
  • Earns a Badge
When working with data, windows are a handy tool to accumulate data subsets from input streams and perform aggregation operations on this specific data. In this course, you'll learn how to perform stream processing through windowing operations in Faust. You'll start by examining the different windowing operations possible on input streams, including tumbling, sliding, count, session, and global windows. Next, you'll distinguish the three notions of time associated with streaming events: event, ingestion, and processing time. You'll then use Faust window features to perform windowing operations on input streams and emit aggregation results for every window. Finally, you'll use the REST API server, which all Faust applications have, to make streaming code metrics and table data accessible to the user. Once you're done with this course, you'll be able to use windowing operations via Faust and expose metrics using web views.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    summarize how windowing operations work on input streams
    recall the different types of windows supported by Faust and their characteristics
    recall the differences between event time, ingestion time, and processing time
    implement processing time tumbling windows
    aggregate data on a per-key, per-window basis
    contrast tumbling windows and hopping windows
  • use the key index to iterate over keys, values, and items in windowed tables
    implement event time hopping windows
    use web views to monitor metrics associated with workers
    handle GET, PUT, POST, DELETE, and HTTP requests with web views
    access tables from web views
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 35s
    UP NEXT
  • Playable
    2. 
    How Windowing Operations on Input Streams Work
    4m 41s
  • Locked
    3. 
    The Characteristics of Faust Windows
    9m 25s
  • Locked
    4. 
    The Different Notions of Time in Streaming Events
    6m 48s
  • Locked
    5. 
    Implementing Faust Processing Time Tumbling Windows
    8m 33s
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    6. 
    Performing Per-key, Per-window Aggregations in Faust
    8m 34s
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    7. 
    Using Tumbling Windows and Hopping Windows in Faust
    10m 40s
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    8. 
    Using the Key Index for Granular Access in Faust
    6m 54s
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    9. 
    Implementing Event Time Hopping Windows in Faust
    6m 42s
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    10. 
    Using Web Views to Monitor Workers in Faust
    6m 53s
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    11. 
    Handling Various Requests Using Web Views and Faust
    8m 45s
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    12. 
    Accessing Faust Tables from Web Views
    6m 3s
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    13. 
    Course Summary
    2m 20s

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