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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Characteristics of a data lake

Another important thing to analyze when setting up data lakes is the characteristics of the data lake. As we will see in a later section, these characteristics can be measured and help us gauge the success or failure of a data lake:

Size: This is the "volume" in the often-mentioned three Vs of big data (volume, variety, velocity) – how big is the lake?
Governability: How easy is it to verify and certify the data in your lake?
Quality: What is the quality of the data contained in the lake? Are some records and files invalid? Are there duplicates? Can you determine the source and lineage of the data in the lake?
Usage: How many visitors, sources, and downstream systems does the lake have? How easy is it to populate and access the data in the lake?
Variety: Does the data that the lake holds have many types? Are there many types of data sources that feed the lake? Can the data in the lake be extracted in different...