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 how organizations can share data that’s internal to the organization as well as externally for monetization. Internal data sharing can be as easy as sharing the data in the S3 data lake by providing cross-account access to Amazon Athena. Athena can read data from a shared Glue Data Catalog, making it easy to share different objects from the catalog. We also looked at how Redshift’s data sharing feature helps in sharing data that’s stored in one Redshift cluster with many other clusters in the organization. By creating a producer cluster and providing grants, the consumer cluster can easily access the objects shared with it.

Finally, we looked at patterns for sharing data external to the organization by leveraging AWS Data Exchange. Data Exchange helps us share datasets via various modes, such as files, S3, Redshift, Lake Formation, and APIs. Without data sharing features, complex ETL pipelines would have to be built to move...