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

Internal data sharing

Organizations have many internal LOBs and each LOB has many personas that interact with the data produced by their department. Different LOBs often want access to portions of data from other departments for many reasons, including cross-sell, up-sell, fraud detection, and other critical insights about their customers. First, let’s look at a use case on how each LOB can share data that they have curated inside their S3 data lake.

Data sharing using Amazon Athena

Previously, we covered how you can create a data lake on Amazon S3 and then interactively query it using Amazon Athena. In a simple scenario, the data produced by one LOB is only consumed by the personas inside the same LOB. But to unlock the true value of data, organizations prefer that each LOB shares relevant sets of data with other LOBs. When organizations prefer to create a centralized enterprise data lake, the question becomes, how can each LOB access the datasets that belong to them...