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Modern Data Architecture on AWS

Modern Data Architecture on AWS

By : Behram Irani
4.9 (15)
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Modern Data Architecture on AWS

Modern Data Architecture on AWS

4.9 (15)
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)
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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 concluded the book by providing you with options for automating your data platform. We looked at DevOps, DataOps, and MLOps as the three ways to completely automate and operationalize your data platform.

In the DevOps process, we looked at how CI/CD and Iac help organizations with an automated, repeatable, and organized way to operationalize their AWS infrastructure, services, and the features inside those services. DataOps focuses on simplifying the data pipelines by leveraging orchestration services such as Amazon MWAA and AWS Step functions. MLOps on the other hand helps to manage the entire life cycle of the ML process and Amazon SageMaker provides capabilities to make MLOps a seamless process.

Finally, we looked at how organizations can monetize their data by either using DaaS, insights-as-a-service, or API-as-a-service. All organizations have the common goal of deriving value from their data platform, either directly by monetizing the data or...

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