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Scalable Data Streaming with Amazon Kinesis

Scalable Data Streaming with Amazon Kinesis

By : Makota, Brian Maguire, Gagne, Chakrabarti
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Scalable Data Streaming with Amazon Kinesis

Scalable Data Streaming with Amazon Kinesis

5 (4)
By: Makota, Brian Maguire, Gagne, Chakrabarti

Overview of this book

Amazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services. This data streaming service provides APIs and client SDKs that enable you to produce and consume data at scale. Scalable Data Streaming with Amazon Kinesis begins with a quick overview of the core concepts of data streams, along with the essentials of the AWS Kinesis landscape. You'll then explore the requirements of the use case shown through the book to help you get started and cover the key pain points encountered in the data stream life cycle. As you advance, you'll get to grips with the architectural components of Kinesis, understand how they are configured to build data pipelines, and delve into the applications that connect to them for consumption and processing. You'll also build a Kinesis data pipeline from scratch and learn how to implement and apply practical solutions. Moving on, you'll learn how to configure Kinesis on a cloud platform. Finally, you’ll learn how other AWS services can be integrated into Kinesis. These services include Redshift, Dynamo Database, AWS S3, Elastic Search, and third-party applications such as Splunk. By the end of this AWS book, you’ll be able to build and deploy your own Kinesis data pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose (KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics (KDA).
Table of Contents (13 chapters)
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1
Section 1: Introduction to Data Streaming and Amazon Kinesis
5
Section 2: Deep Dive into Kinesis
10
Section 3: Integrations

Chapter 5: Kinesis Firehose

In this chapter, we take an in-depth look at Amazon Kinesis Data Firehose (KDF). This is a fully managed serverless service for the ingestion of large volumes of data from a number of sources and delivery to an ever-increasing number of integrated destinations. It provides an easy way to enable a number of big-data use cases by delivering data (without any coding requirements) to a data lake; a high-capacity and high-performance parallel data warehouse service; a search-and-analytics service; HyperText Transfer Protocol (HTTP) endpoints; and a number of third-party providers. It also allows a number of inline Extract-Transform-Load (ETL) transformations that enable high-velocity and high-throughput transformations, enabling near-real-time use cases. We also take a look at how KDF can be used in the data pipeline described in the chapter on Kinesis Data Streams (KDS), with the SmartCity use case.

The following topics will be covered in this chapter:

...
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