Scalable Data Streaming with Amazon Kinesis

Scalable Data Streaming with Amazon Kinesis –

Explore Kinesis managed services such as Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video Streams with the help of practical use cases

Key Features:

• Get well versed with the capabilities of Amazon Kinesis
• Explore the monitoring, scaling, security, and deployment patterns of various Amazon Kinesis services
• Learn how other Amazon Web Services and third-party applications such as Splunk can be used as destinations for Kinesis data

Book Description:

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).

What You Will Learn:

• Get to grips with data streams, decoupled design, and real-time stream processing
• Understand the properties of KFH that differentiate it from other Kinesis services
• Monitor and scale KDS using CloudWatch metrics
• Secure KDA with identity and access management (IAM)
• Deploy KVS as infrastructure as code (IaC)
• Integrate services such as Redshift, Dynamo Database, and Splunk into Kinesis

Who this book is for:

This book is for solutions architects, developers, system administrators, data engineers, and data scientists looking to evaluate and choose the most performant, secure, scalable, and cost-effective data streaming technology to overcome their data ingestion and processing challenges on AWS. Prior knowledge of cloud architectures on AWS, data streaming technologies, and architectures is expected.