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Serverless ETL and Analytics with AWS Glue

You're reading from  Serverless ETL and Analytics with AWS Glue

Product type Book
Published in Aug 2022
Publisher Packt
ISBN-13 9781800564985
Pages 434 pages
Edition 1st Edition
Languages
Authors (6):
Vishal Pathak Vishal Pathak
Profile icon Vishal Pathak
Subramanya Vajiraya Subramanya Vajiraya
Profile icon Subramanya Vajiraya
Noritaka Sekiyama Noritaka Sekiyama
Profile icon Noritaka Sekiyama
Tomohiro Tanaka Tomohiro Tanaka
Profile icon Tomohiro Tanaka
Albert Quiroga Albert Quiroga
Profile icon Albert Quiroga
Ishan Gaur Ishan Gaur
Profile icon Ishan Gaur
View More author details

Table of Contents (20) Chapters

Preface 1. Section 1 – Introduction, Concepts, and the Basics of AWS Glue
2. Chapter 1: Data Management – Introduction and Concepts 3. Chapter 2: Introduction to Important AWS Glue Features 4. Chapter 3: Data Ingestion 5. Section 2 – Data Preparation, Management, and Security
6. Chapter 4: Data Preparation 7. Chapter 5: Data Layouts 8. Chapter 6: Data Management 9. Chapter 7: Metadata Management 10. Chapter 8: Data Security 11. Chapter 9: Data Sharing 12. Chapter 10: Data Pipeline Management 13. Section 3 – Tuning, Monitoring, Data Lake Common Scenarios, and Interesting Edge Cases
14. Chapter 11: Monitoring 15. Chapter 12: Tuning, Debugging, and Troubleshooting 16. Chapter 13: Data Analysis 17. Chapter 14: Machine Learning Integration 18. Chapter 15: Architecting Data Lakes for Real-World Scenarios and Edge Cases 19. Other Books You May Enjoy

Technical requirements

For this chapter, you will need the following resources:

  • An AWS account
  • An AWS IAM role
  • An Amazon S3 bucket

All the sample code needs to be executed in a Glue runtime (for example, the Glue job system, Glue Interactive Sessions, a Glue Studio notebook, a Glue Docker container, and so on). If you do not have any preferences, we recommend using a Glue Studio notebook so that you can easily start writing code. To use a Glue Studio notebook, follow these steps:

  1. Open the AWS Glue console.
  2. Click AWS Glue Studio.
  3. Click Jobs.
  4. Under Create job, click Jupyter Notebook, then Create.
  5. For Job name, enter your preferred job name.
  6. For IAM Role, choose an IAM role where you have enough permission.
  7. Click Start notebook job.
  8. Wait for the notebook to be started.
  9. Write the necessary code and run the cells on the notebook.

Let’s begin!

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