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

Amazon Kinesis Data Firehose (KDF)

Amazon KDF was launched in October 2015. It is a fully managed, serverless service for ingesting streaming data and delivering to destinations in AWS, third-party services such as Splunk, or even generic HTTP endpoints. In terms of the five core stages of enabling real-time analytics, Amazon KDF straddles stream storage and real-time stream processing. Some of the core capabilities of Amazon KDF are as follows:

  • Ingesting data at high volumes
  • Ingesting high-throughput streaming data from myriad data sources
  • Buffering and aggregating data
  • Transforming and processing data inline
  • Sending data to one of a number of destinations
  • Handling errors and retries while sending
  • Storing ingested data in the service for 24 hours, to enable retries and handle situations when destinations are unavailable

When Amazon KDS first launched, the majority of organizations used the service to ingest streaming data and store it in Amazon...