Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Data Wrangling on AWS

You're reading from   Data Wrangling on AWS Clean and organize complex data for analysis

Arrow left icon
Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781801810906
Length 420 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
Sankar M Sankar M
Author Profile Icon Sankar M
Sankar M
Navnit Shukla Navnit Shukla
Author Profile Icon Navnit Shukla
Navnit Shukla
Sam Palani Sam Palani
Author Profile Icon Sam Palani
Sam Palani
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Unleashing Data Wrangling with AWS
2. Chapter 1: Getting Started with Data Wrangling FREE CHAPTER 3. Part 2:Data Wrangling with AWS Tools
4. Chapter 2: Introduction to AWS Glue DataBrew 5. Chapter 3: Introducing AWS SDK for pandas 6. Chapter 4: Introduction to SageMaker Data Wrangler 7. Part 3:AWS Data Management and Analysis
8. Chapter 5: Working with Amazon S3 9. Chapter 6: Working with AWS Glue 10. Chapter 7: Working with Athena 11. Chapter 8: Working with QuickSight 12. Part 4:Advanced Data Manipulation and ML Data Optimization
13. Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas 14. Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler 15. Part 5:Ensuring Data Lake Security and Monitoring
16. Chapter 11: Data Lake Security and Monitoring 17. Index 18. Other Books You May Enjoy

Data lake security

In this section, we will discuss bout various options and methodologies to enhance the security aspects of your data lake. We will talk about security across multiple layers; however, this should only be considered a starting point. You will have to work more and add additional security layers as applicable to your project.

Data lake access control

To secure a data lake, we can use a combination of user-based access policies and resource-based access policies. User policies are attached to users/roles and control the actions that a user/role can perform within an account. In addition to user-based control policies, we can attach access policies to resources such as Amazon S3, Amazon SQS, and more, to control access specifically for that resource. This provides another layer of security for protecting AWS resources. As a general best practice, the access policies should be kept least privilege to allow only required actions with specific conditions.

User...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image