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 Amazon Redshift helps modernize data warehouses. We covered the basics of what Amazon Redshift looks like and how some of its features help meet next-gen business use cases. We went through each type of use case, starting from an overarching use case around modernizing legacy on-premises data warehouses by migrating the data to Amazon Redshift. We then looked at some of the data ingestion use cases that most organizations use to get the data inside Redshift. Once the data was ingested, we looked at how to leverage the compute power of Redshift to transform data using the ELT pattern. Stored procs, MVs, and Apache Spark connectors are all supported by Redshift to help process the data so that it can be ready for consumption.

Before the data can be consumed, we had to learn how to control and set security measures for the data that resides in Redshift. We applied some fine-grained access control patterns such as RBAC, row-level and column...