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 Federation

In the previous chapter, we explored different use cases for sharing data, both internally and externally with the organization. Data sharing is a very critical aspect of any data platform, where data stored in an Amazon S3-based data lake and in an Amazon Redshift data warehouse is seamlessly shared, without the need to create duplicate copies. Every data platform has distinct components for data storage, as well as for data computations. In the data sharing model, we focused on sharing data between similar systems – for example, using Amazon Athena to share data stored in an S3 data lake and using Amazon Redshift to share data with other Redshift clusters.

Data doesn’t always get stored, processed, and shared within homogeneous systems. A lot of times, data is captured in heterogeneous systems and those systems may not even reside inside the AWS ecosystem. This brings us to the question, how do we seamlessly and transparently query datasets from a...