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Data Wrangling on AWS

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

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781801810906
Length 420 pages
Edition 1st Edition
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Authors (3):
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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
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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

What is a data lake?

A data lake is a centralized repository that allows organizations to store all of their structured and unstructured data at any scale. This approach to data storage and management provides organizations with a single, unified platform for storing and managing data from a variety of different sources, including social media, sensors, and transactional systems.

Data lakes are designed to support the storage of large amounts of data in its raw format, allowing it to be processed and analyzed at a later stage by various teams within the organization. This approach to data storage and management provides organizations with the flexibility to collect and store data from a wide range of sources, without the need to preprocess or structure the data in any specific way.

One of the key benefits of using a data lake is that it allows organizations to store and manage data from a variety of different sources, including both structured and unstructured data. This means...

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