You're reading from Generative AI-Powered Assistant for Developers Accelerate software development with Amazon Q Developer

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835089149

Length 416 pages

Edition 1st Edition

Tools

AWS

Concepts

GPT/LLMs

Authors (2):

Behram Irani

Rahul Sonawane

View More author details

Table of Contents (23) Chapters

Preface

1. Part 1:Generative AI-Powered Assistant

2. Chapter 1: Introduction to Generative AI-Powered Assistants FREE CHAPTER

3. Chapter 2: Introducing and Setting Up Amazon Q Developer

4. Part 2:Generate Code Recommendations

5. Chapter 3: Understanding Auto-Code Generation Techniques

6. Chapter 4: Boost Coding Efficiency for Python and Java with Auto-Code Generation

7. Chapter 5: Boost Coding Efficiency for C and C++ with Auto-Code Generation

8. Chapter 6: Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation

9. Chapter 7: Boost Coding Efficiency for SQL with Auto-Code Generation

10. Chapter 8: Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation

11. Chapter 9: Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation

12. Part 3:Advanced Assistant Features

13. Chapter 10: Customizing Code Recommendations

14. Chapter 11: Understanding Code References

15. Chapter 12: Simplifying Code Explanation, Optimization, Transformation, and Feature Development

16. Chapter 13: Simplifying Scanning and Fixing Security Vulnerabilities in Code

17. Part 4:Accelerate Build on AWS

18. Chapter 14: Accelerate Data Engineering on AWS

19. Chapter 15: Accelerate Building Solutions on AWS

20. Chapter 16: Accelerate the DevOps Process on AWS

21. Index

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Summary

In this chapter, we initially covered the integration of different AWS services with code companions to assist users in auto-code generation. Then, we explored the integration of Amazon Q Developer with some of the core services, such as AWS Glue, Amazon EMR, AWS Lambda, Amazon Redshift, and Amazon SageMaker, commonly used by application developers, data engineers, and data scientists.

We then discussed, in the prerequisites, the in-depth integration with sample common use cases and corresponding solution walk-throughs for various integrations.

AWS Glue integration with Amazon Q Developer, aiding data engineers in generating and executing ETL scripts using the AWS Glue Studio notebook environment. This includes a skeletal outline of a full end-to-end Glue ETL job using AWS Glue Studio.

AWS EMR integration with Amazon Q Developer to assist data engineers in generating and executing ETL scripts using the AWS EMR Studio notebook environment.

AWS Lambda console IDE...