Book Image

Modern Data Architecture on AWS

By : Behram Irani
5 (1)
Book Image

Modern Data Architecture on AWS

5 (1)
By: Behram Irani

Overview of this book

Many IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.
Table of Contents (24 chapters)
1
Part 1: Foundational Data Lake
5
Part 2: Purpose-Built Services And Unified Data Access
17
Part 3: Govern, Scale, Optimize And Operationalize

Streaming Data Ingestion

In this chapter, we will look at the following key topics:

  • The need for streaming architectures and its challenges
  • Streaming data ingestion using Amazon Kinesis
  • Streaming data ingestion using Amazon MSK
  • Streaming services usage patterns

Chapter 3, Batch Data Ingestion, was all about batch data ingestion, where we saw multiple ways of ingesting data in batches. Batch data ingestion is still the bedrock of many data pipelines since it helps to serve so many business use cases. For many such use cases, data analytics can be performed with data that’s not fresh – that is, data is not available for consumption in the analytics environment as soon as it’s produced in the source system. For a very long time, deriving reactive insights from data was fine as OLAP systems were meant to perform analytics on data that was typically a day old.

However, data in these modern times gets generated in large volumes and moves...