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

Summary

In this chapter, we looked at what data mesh is and how the four principles of data mesh help create a highly distributed, scalable, and governed data platform. AWS analytics services such as Amazon Redshift, S3 data lakes, AWS Lake Formation, and Amazon Athena contribute toward building a data mesh architecture; many features of these services assist in enabling a data mesh pattern.

We then looked at how, using AWS Lake Formation, organizations can create a cross-account permissions model that helps create a data mesh on an S3 data lake. Using Amazon DataZone, the process of publishing and subscribing to data assets become even easier to manage.

Finally, we looked at how you can use the Amazon Redshift datashare feature to create a data mesh pattern by allowing Redshift clusters in different AWS accounts and regions to share data assets. DataZone helps here too by simplifying the process of federated governance and fostering a self-service analytics culture.

In the...