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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Spark Streaming


Spark Streaming wasn't the first streaming architecture. Over time, multiple technologies have been developed in order to address various real-time processing needs. One of the first popular stream processor technologies was Twitter Storm, and it was used in many businesses. Spark includes the streaming library, which has grown to become the most widely used technology today. This is mainly because Spark Streaming holds some significant advantages over all of the other technologies, the most important being its integration of Spark Streaming APIs within its core API. Not only that, but Spark Streaming is also integrated with Spark ML and Spark SQL, along with GraphX. Because of all of these integrations, Spark is a powerful and versatile streaming technology.

Note that https://spark.apache.org/docs/2.1.0/streaming-programming-guide.html has more information on Spark Streaming Flink, Heron (Twitter Storm's successor), and Samza and their various features; for example, their...

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