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Learning Apache Spark 2

You're reading from   Learning Apache Spark 2 A beginner's guide to real-time Big Data processing using the Apache Spark framework

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785885136
Length 356 pages
Edition 1st Edition
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Author (1):
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Muhammad Asif Abbasi Muhammad Asif Abbasi
Author Profile Icon Muhammad Asif Abbasi
Muhammad Asif Abbasi
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Table of Contents (12) Chapters Close

Preface 1. Architecture and Installation 2. Transformations and Actions with Spark RDDs FREE CHAPTER 3. ETL with Spark 4. Spark SQL 5. Spark Streaming 6. Machine Learning with Spark 7. GraphX 8. Operating in Clustered Mode 9. Building a Recommendation System 10. Customer Churn Prediction Theres More with Spark

What is Structured Streaming?


We've covered discretized streams in quite a lot of detail. However, if you have been following the Spark news recently, you may have heard of the new DataFrame/DataSet-based streaming framework named Structured Streaming. Why is there a need for a new streaming framework? We've talked about how revolutionary the concept of Spark Streaming using DStreams was, and how you can actually combine multiple engines such as SQL, Streaming, Graph, and ML to build a data pipeline, so why the need for a new engine altogether?

Based on the experience with Spark Streaming, the team at Apache Spark released that there were a few issues with DStreams. The top three issues were as follows:

  • As we have seen in the preceding examples, DStreams can work with the batch time, but not the event time inside the data.
  • While every effort was made to keep the API similar, the Streaming API was still different to RDD API in the sense that you cannot take a Batch job and start running it as...
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