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

Summary

In this chapter, we looked at how AI/ML technologies play a big role in predictive analytics so that organizations can stay ahead of the curve and proactively make decisions before things happen. But at the same time, we also looked at many of the barriers related to the adoption of AI/ML and how AWS is able to overcome all these barriers.

We introduced the different stacks of how AWS provides services specific to each of these layers. For the AI layer, AWS provides a long list of 20+ services that help with specific types of AI problems such as speech, image, text, and so forth. These services help fast-track solutions that can be solved by pre-trained ML models.

We then looked at Amazon SageMaker as an ML service that has many components to it. SageMaker Canvas helps business analysts with low-code/no-code types of tools so that they can quickly create ML models and predict business outcomes. We looked at how SageMaker Studio has various tools inside it to help with...