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

The DataOps process

DataOps in AWS refers to the application of DevOps principles and practices to data-related workflows and processes. It focuses on optimizing the development, deployment, and management of data pipelines, data integration, and data analytics solutions.

DataOps aims to improve the speed, quality, and reliability of data operations by fostering collaboration, automation, and repeatability across the data life cycle. It combines data engineering, data integration, data governance, and data analytics with the principles of CI/CD, version control, and IaC.

On AWS, several services and tools can be leveraged to implement DataOps practices:

  • AWS Glue: The AWS Glue ETL service simplifies data preparation and integration. It allows you to create and manage data pipelines using workflows, perform data transformations, and automate ETL jobs.
  • AWS Lake Formation: AWS Lake Formation is a service that simplifies the process of building, securing, and managing...