Book Image

AWS FinOps Simplified

By : Peter Chung
Book Image

AWS FinOps Simplified

By: Peter Chung

Overview of this book

Much like how DevOps is a combination of cultural philosophies, practices, and tools that advocate a collaborative working relationship between development and IT operations, FinOps encourages the same collaboration between technology and finance team, making it key relationship to establish and maintain for any thriving business. This book will help you understand how organizations with a mature FinOps practice can decentralize cost ownership to developer teams and encourage cross-functional collaboration between business, finance, and technology, enabling speed, innovation, and business growth. You’ll focus on structuring your organization to form the right FinOps team, including a Cloud Center of Excellence, and learn how to implement practical cost savings measures with AWS tools to optimize costs in both the short as well as long term. By the end of this cloud FinOps book, you’ll be ready to implement a successful Cloud FinOps practice for your organization to get the best value from the AWS cloud for your workloads.
Table of Contents (18 chapters)
Free Chapter
2
Part 1: Managing Your AWS Inventory
7
Part 2: Optimizing Your AWS Resources
12
Part 3: Operationalizing FinOps

Optimizing analytics

If data is the new gold, we want to ensure we’re mining it without incurring waste. Data analytics and ML are discussion topics that deserve their own books but, in this section, we’ll summarize cost-optimization considerations when running these types of workloads. Broadly, we can categorize the steps involved as data ingestion, data exploration, model training, and model deployment.

We already know about Amazon S3 as an object store that functions nicely as a data lake. With data in S3, we can use a managed service such as Amazon Athena to run Structured Query Language (SQL) queries directly on our data in Amazon S3. Athena is serverless, meaning you don’t have to manage any infrastructure to run SQL queries on your data. Additionally, it scales automatically and parallelizes queries on large datasets without you having to specify configurations. It also requires no maintenance because the underlying servers powering Athena are managed...