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

Data pipelines with Amazon Kinesis Data Streams

As we have learned how to create streams, producers, and consumers, we will design a simple data pipeline for SmartCity. A data pipeline is a series of processing steps applied to data flowing from the source to the target destination. The processing steps could include automation for copying, transforming, routing, and loading source data to destinations such as business systems, data lakes, and data warehouses. A data pipeline should support the requirements for data throughput, reliability, and latency. A well-architected design will prevent many of the common problems that can occur when collecting and loading data, such as data corruption, bottlenecks, conflicts between sources, and the creation of duplicate entries.

Data pipeline design (simple)

With this first design, this demonstrates receiving data from a single source of data. The data source producer is using Amazon Kinesis Agent deployed in the SmartCity data center...