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Scalable Data Streaming with Amazon Kinesis

You're reading from   Scalable Data Streaming with Amazon Kinesis Design and secure highly available, cost-effective data streaming applications with Amazon Kinesis

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800565401
Length 314 pages
Edition 1st Edition
Languages
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Authors (4):
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Rajeev Chakrabarti Rajeev Chakrabarti
Author Profile Icon Rajeev Chakrabarti
Rajeev Chakrabarti
Tarik Makota Tarik Makota
Author Profile Icon Tarik Makota
Tarik Makota
Brian Maguire Brian Maguire
Author Profile Icon Brian Maguire
Brian Maguire
Danny Gagne Danny Gagne
Author Profile Icon Danny Gagne
Danny Gagne
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Toc

Table of Contents (13) Chapters Close

Preface 1. Section 1: Introduction to Data Streaming and Amazon Kinesis
2. Chapter 1: What Are Data Streams? FREE CHAPTER 3. Chapter 2: Messaging and Data Streaming in AWS 4. Chapter 3: The SmartCity Bike-Sharing Service 5. Section 2: Deep Dive into Kinesis
6. Chapter 4: Kinesis Data Streams 7. Chapter 5: Kinesis Firehose 8. Chapter 6: Kinesis Data Analytics 9. Chapter 7: Amazon Kinesis Video Streams 10. Section 3: Integrations
11. Chapter 8: Kinesis Integrations 12. Other Books You May Enjoy

Summary

In this chapter, we learned about the core concepts and capabilities, as well as approaches, regarding common deployment patterns, monitoring and scaling, and the KDA application's security. We built real-time streaming applications using both SQL and Apache Flink.

KDA has many applications, but it's most commonly used for clickstream and big data analytics. When used with other AWS services, it enables us to build sophisticated and scalable solutions. It is easy for those of you with a SQL skillset to translate those and create your KDA SQL applications. On the other hand, if you are coming from a Java or Scala background, using KDA for Flink should be a breeze.

In the next chapter, you will learn to consume and process stream video with Kinesis Video Streams (KVS). KVS allows us to build applications that stream video from connected devices to use it for other processing, such as machine learning (ML) or simply for the purposes of playback. As is the case...

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