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

Interactive Analytics

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

  • Analytics using Amazon Athena
  • Analytics using Presto, Trino, and Hive on Amazon EMR

One of the fundamental principles of building a modern data architecture on AWS is hinged around using purpose-built tools for solving specific use cases. An enterprise data platform once fully built has many components, each with a specific purpose for solving a particular business use case.

In Chapter 2, Scalable Data Lakes, we went through the fundamentals of building a data lake on AWS using Amazon S3 as the storage layer and the AWS Glue Data Catalog as the technical metadata layer. Each layer of the data lake has data that may be of use to different personas in an organization. The most basic ask from each of these personas will be to provide them the ability to query datasets in the data lake using the SQL syntax so that they can derive insights from the data. Interactive analytics, using specific...