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

AWS AI/ML services overview

AWS provides a very broad set of AI/ML services, right from specialized infrastructure and ML frameworks that allow ML engineers to custom train their ML models and deploy them on custom hardware. This includes ML frameworks such as PyTorch, TensorFlow, and Apache MXNet. ML infrastructure often requires plenty of CPU and GPU power. AWS provides many types of Amazon Elastic Compute Cloud (Amazon EC2) instances such as the P3 and Trn1 instances that are suitable for ML training. AWS also provides ML accelerators such as AWS Trainium for DL training and AWS Inferentia for high-performance ML inferences.

The next layer of services revolves around ML. AWS ML services are created specifically keeping in mind many of the barriers to ML adoption. In order to democratize ML, it is essential to have different services geared toward different personas in the organization. For the same reason, AWS has created ML services that can help multiple personas, even those...