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

Data processing using AWS Glue DataBrew

In the quest to build an end-to-end data platform, IT teams in organizations spend a significant amount of time creating data processing ETL pipelines. Typically, data processing is the responsibility of data engineers, who have to understand the rules of data transformations and then implement them. This means that other personas in the organization, such as data scientists or data analysts, have to rely on data engineers to help them with the structure of data they are looking for in their day-to-day tasks. The change cycles involve ETL, normalizing, cleaning the data, and finally, orchestrating and deploying in automated data pipelines. The whole process takes weeks and sometimes months. This creates a bottleneck and delays the final business outcomes.

AWS Glue DataBrew solves this exact problem by providing a serverless, no-code data preparation service, specifically targeted at data scientists and data analysts. With DataBrew, end users...