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

Tool-specific performance tuning

We covered a lot about optimizing the service infrastructure in this chapter. However, often, optimizations happen at the service or tool level. This is caused by changing the configurations of the service or fine-tuning the logic that runs on these services. Typically, tuning is done to improve performance, which also helps save costs. It will not be possible to cover every aspect of performance tuning in this section, but we will try to cover some of the obvious ones from some of the key services that help build a data platform on AWS.

Performance tuning measures on Amazon Redshift

Many aspects of performance tuning depend on root cause analysis; hence, we may not be able to cover every tunable in Redshift. Also, recent Redshift autonomics advancements have made a lot of tunable settings automatic now; things such as data distribution, sorting, and analyze and vacuum operations can all be made automatic by the service. However, some common tunable...