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

How does generative AI help different industries?

Since we will not be able to dive deep into every use case across many industries, the least we can do is highlight how GenAI can disrupt the conventional ways of solving use cases across many sectors. Every organization wants to ensure that it can transform its business outcomes by incorporating GenAI into its operations. Every industry has many low-hanging use cases where GenAI can accelerate its business outcomes.

Financial services

Since our book revolves around GreatFin, a financial conglomerate, let’s start with how GenAI helps solve several important but tedious use cases within the financial services industry. Here are some examples:

  • Fraud detection: GenAI can play a crucial role in identifying and preventing fraudulent activities in financial transactions. By analyzing extensive datasets, generative models can detect patterns and anomalies, enabling the identification of suspicious transactions and the...