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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

This chapter was all about ensuring that the data platform that’s built is performant as well as cost-effective. We started by understanding the need for a data platform that operates optimally. If any part of the data platform is either not performing well or is very expensive, it often creates a snowball effect and affects the business negatively.

A lot of cost optimization can be achieved by optimizing the infrastructure used by the AWS services under the covers. By optimizing storage and compute resources, we can save significant costs. We also looked at some of the tools AWS provides that help in the cost optimization process.

Finally, we looked at some of the service-specific tuning settings that can help with performance improvements. The list of such improvements can be quite long for each service, but the key message was to leverage the best practices for each service and always perform a WAR before deploying workloads in production.

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