Book Image

Scalable Data Streaming with Amazon Kinesis

By : Tarik Makota, Brian Maguire, Danny Gagne, Rajeev Chakrabarti
Book Image

Scalable Data Streaming with Amazon Kinesis

By: Tarik Makota, Brian Maguire, Danny Gagne, Rajeev 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)
1
Section 1: Introduction to Data Streaming and Amazon Kinesis
5
Section 2: Deep Dive into Kinesis
10
Section 3: Integrations

What this book covers

Chapter 1, What Are Data Streams?, covers core streaming concepts so that you will have a detailed understanding of their application in distributed systems.

Chapter 2, Messaging and Data Streaming in AWS, takes a brief look at the ecosystem of AWS services in the messaging space. After reading this chapter, you will have a good understanding of the various services, be able to differentiate them, and understand the strengths of each service.

Chapter 3, The SmartCity Bike-Sharing Service, reviews the existing bike-sharing application and how the city plans to modernize it. This chapter will provide the background information for the examples used throughout the book.

Chapter 4, Kinesis Data Streams, teaches concepts and capabilities, common deployment patterns, monitoring and scaling, and how to secure KDS. We will step through a data streaming solution that will ingest, process, and feed data from multiple SmartCity data systems.

Chapter 5, Kinesis Firehose, teaches the concepts, common deployment patterns, monitoring and scaling, and security in KFH.

Chapter 6, Kinesis Data Analytics, covers the concepts and capabilities, approaches for common deployment patterns, monitoring and scaling, and security in KDA. You will learn how real-time streaming data can be queried like a database with SQL or code.

Chapter 7, Amazon Kinesis Video Streams, explores the concepts, monitoring and scaling, security, and deployment patterns for real-time communication and data ingestion. We will step through a solution that will provide real-time access to a video stream and ingest video data for the SmartCity data system.

Chapter 8, Kinesis Integrations, reviews how to integrate Kinesis with several Amazon services, such as Amazon Redshift, Amazon DynamoDB, AWS Glue, Amazon Aurora, Amazon Athena, and other third-party services such as Splunk. We will integrate a wide variety of services to create a serverless data lake.