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

Data mesh on Amazon Redshift

A data mesh is an architecture pattern that’s not just limited to a single kind of analytics setup. A data lake is a prominent architecture that benefits from a data mesh in a large organization setup, with many independent analytics environments. However, data warehouses are also a foundational data store for analytics operations, and many times, data warehouses are the primary driving force of a data platform. Let’s look at how to establish a data mesh architecture using Amazon Redshift and Amazon DataZone.

The Redshift datashare feature plays a huge role in creating a data mesh using just Redshift. Any number of Redshift clusters, in any AWS account and region, can share datasets with other such clusters. This allows data producers to share data just by using SQL statements inside Redshift. Also, the consumers in other Redshift clusters use SQL statements to gain access to such shared assets.

However, distributed federated governance...