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

Summary

In this chapter, we reviewed the features of Amazon KDF and how it can be used in data pipelines in common multi-account enterprise architectures. We saw how to do encryption, networking, authentication, and authorization with multiple Amazon and third-party services and software. We saw how Amazon KDF can be an integral part of any data analytics pipeline or data-lake architecture with its ability to easily ingest data from other AWS services including AWS IoT, Amazon CloudWatch Logs, Amazon CloudWatch Events, and KDS, do inline transformations using Lambda functions, and deliver to Amazon S3, Amazon Redshift, Amazon Elasticsearch, HTTP endpoints, and other third-party destinations. We also looked at the SmartCity bikes example and saw how to deliver records to Amazon S3 in a columnar Parquet format. It should now be clear how you can configure Amazon KDF for your use cases.

In the next chapter, we will look at other services under the Kinesis umbrella of services.

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