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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Comparing and choosing what works best for your use case

The following table shows comparisons between Logstash, Fluentd, Apache Flume, Apache NiFi, and Apache Kafka:

Logstash

Fluentd

Apache Flume

Apache NiFi

Apache Kafka

Concerns

No UI and hard to write filters

Windows installation is still in the experiment stage.

Hard to manage multiple connections.

Not mature enough when compared to other tools available on the market.

No UI and hard to maintain offsets.

Main Features

Flexibility and Interoperability

Simplicity and robustness

Provides best integration with HDFS, reliable and scalable.

Flow management, ease of use, security, flexible scaling model.

Fast, provides pub/sub based data streams, easy to integrate and use, partitioned.

Plugins

90+ plugins

125+ plugins

50+ plugins and custom components

An ample amount of processors...

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