You're reading from AWS for Solutions Architects Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781789539233

Length 454 pages

Edition 1st Edition

Tools

AWS

Concepts

Cloud Computing

Table of Contents (20) Chapters

Preface

1. Section 1: Exploring AWS

2. Chapter 1: Understanding AWS Cloud Principles and Key Characteristics FREE CHAPTER

3. Chapter 2: Leveraging the Cloud for Digital Transformation

4. Section 2: AWS Service Offerings and Use Cases

5. Chapter 3: Storage in AWS – Choosing the Right Tool for the Job

6. Chapter 4: Harnessing the Power of Cloud Computing

7. Chapter 5: Selecting the Right Database Service

8. Chapter 6: Amazon Athena – Combining the Simplicity of Files with the Power of SQL

9. Chapter 7: AWS Glue – Extracting, Transforming, and Loading Data the Simple Way

10. Chapter 8: Best Practices for Application Security, Identity, and Compliance

11. Section 3: Applying Architectural Patterns and Reference Architectures

12. Chapter 9: Serverless and Container Patterns

13. Chapter 10: Microservice and Event-Driven Architectures

14. Chapter 11: Domain-Driven Design

15. Chapter 12: Data Lake Patterns – Integrating Your Data across the Enterprise

16. Chapter 13: Availability, Reliability, and Scalability Patterns

17. Section 4: Hands-On Labs

18. Chapter 14: Hands-On Lab and Use Case

19. Other Books You May Enjoy

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Key metrics in a data lake

More so now than ever, digital transformation projects have tight deadlines and are forced to continue to do more with fewer resources. It is vital to quickly demonstrate added value and results. As we will see in later chapters, AWS offers many services to assist in the creation of an enterprise data lake. However, the tooling needed to measure the success of enterprise data lake implementations is still somewhat lacking.

A successful data lake implementation can serve a corporation well for years. A key to this success and longevity is being able to effectively communicate whether the implementation is adding value or not. However, most metrics for an enterprise data lake are not binary and are more granular than just saying that a project is green or red.

Metrics to gauge the success of your data lake

A list of metrics that can be used to gauge the success of your data lake follows. It is not meant to be a comprehensive list but rather a starting...