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

Database migration using AWS DMS

In the prologue, we saw how in recent times, types and volumes of data have exponentially grown. However, a vast amount of data still resides in relational data stores, such as databases and data warehouses. So, let’s get going with relational data stores as the low-hanging fruit for data migration, and tie it back to our GreatFin corporation’s use cases.

Use case for database migration and replication

All lines of business (LOBs) at GreatFin have their transactional data sitting in on-prem databases such as Oracle and SQL Server, and they want the data all centralized in a data lake for them to have self-service analytics and derive insights from the data across all these systems.

Some reports need to get the latest data for analytics as soon as the source databases commit the transaction. This will allow the business to see near-real-time dashboards in order for them to make quick decisions.

At the same time, some LOBs want...