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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

Summary

In this chapter, we have discussed what big data is, the characteristics of big data, what a data lake is, why we need data lakes, and how a data lake can be built on Amazon S3 by providing an overview of the benefits of data lakes, the different layers of a data lake, and the best practices for building a data lake on Amazon S3. We also provided details on organizing and managing the data within a data lake on S3, including using features such as file formats, partitions, S3 lifecycle management, Amazon S3 Intelligent-Tiering, and so on. The chapter also discussed some challenges and considerations when building a data lake on Amazon S3, such as cost and performance.

In the next chapter, we are going to learn about AWS Glue. AWS Glue is a data integration service that lets you bring data from different data sources and allows you to perform ETL on top of it using frameworks such as Apache Spark and Python.

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