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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

Introduction to streaming execution model


Flink is an open source framework for distributed stream processing that:

  • Provides results that are accurate, even in the case of out-of-order or late-arriving data
  • Is stateful and fault tolerant, and can seamlessly recover from failures while maintaining an exactly-once application state
  • Performs on a large scale, running on thousands of nodes with very good throughput and latency characteristics

The following diagram is a generalized view of stream processing:

Many of Flink's features - state management, handling out-of-order data, flexible windowing – are essential for computing accurate results on unbounded datasets and are enabled by Flink's streaming execution model:

  • Flink guarantees exactly-once semantics for stateful computations. Stateful means that applications can maintain an aggregation or summary of data that has been processed over time, and Flink's checkpointing mechanism ensures exactly-once semantics for an application's state in the event...
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