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 Athena and Presto, Trino, and Hive on EMR help organizations perform ad hoc interactive data analytics on the data stored in the S3 data lake. Athena is a serverless platform that integrates with the Glue Data Catalog and provides data analysts with the ability to write and execute SQL queries without having to manage the platform itself. Using Athena, organizations can focus on the business logic needed for reports versus spending time on creating and managing the infrastructure that’s required by the platform.

We also looked at cases when creating a Presto/Trino cluster on Amazon EMR may be more beneficial for interactive analytics. This is particularly helpful when there are very large volumes of datasets that need to be scanned by thousands of queries on a daily basis and where performance SLAs are strict. Using Presto/Trino on EMR, customers can control cost and at the same time improve query performance by custom tuning...