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

Generative AI on AWS

Ever since the art of the possibility, using GenAI has become obvious, and almost all cloud service providers and software vendors have shown a sense of urgency in providing new services/tools to help organizations build GenAI-based applications for their use cases. AWS also provides a few services that directly assist with this. Keep in mind that new services, along with new features in existing services, will continue to roll out going forward, so keep an eye on new ways of solving business use cases in the future.

To unlock the potential of GenAI, AWS focuses on a few considerations that organizations care for. Let’s look at them and introduce AWS services that support these considerations.

Firstly, building ML models is never trivial; in our predictive analytics chapter, we discussed the many stages that need to be addressed before and after training an ML model. Building and using FMs at scale needs a lot of work. AWS recently announced a new...