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Modern Data Architecture on AWS

Modern Data Architecture on AWS

By : Behram Irani
4.9 (15)
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Modern Data Architecture on AWS

Modern Data Architecture on AWS

4.9 (15)
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)
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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 Sharing

In the previous chapter, we looked at how the data stored in Amazon Redshift can be consumed. But imagine that, in a large company such as our GreatFin example, every line of business (LOB) produces and consumes its own data gathered from multiple channels. For a company to be truly data-driven, the data silos need to be broken and there needs to be an easy way to share data across all LOBs, without the need to physically move the data around as duplicate copies.

First, we will look at how you can share data inside your organization, from a data lake on S3 as well as from the data warehouse we built on Redshift.

In this chapter, we will look at the following key topics:

  • Internal data sharing
  • External data sharing
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