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Introduction to ChatGPT for Data Engineers
What is ChatGPT? Overview of Generative AI
Why Data Engineers Should Care About LLMs
Capabilities & Limitations of ChatGPT
ChatGPT Sneak Peek
Hands On Practice Part 1
Download Hands On Activity 1
Use Cases of ChatGPT in Data Engineering
Hands On Practice Part 2
Download Hands On Activity 2
Mastering Prompt Engineering
What is Prompt Engineering?
Crafting Effective Prompts for Data Tasks
Hands On Practice Part 3
Download Hands On Activity 3
Prompt Patterns Templates Chains and Variables
Debugging and Refining Prompts for Better Results
ChatGPT for Data Exploration and SQL
Writing and Optimizing SQL Queries with ChatGPT
Data Profiling and Summarization
Explaining Complex Queries and Database Schemas
Data Cleaning Suggestions Using AI
ChatGPT for Python & Data Pipelines
Auto-generating Python Scripts and Functions
Converting Pseudo-code to Production-ready Code
Writing ETL Scripts with ChatGPT
Using ChatGPT for Code Reviews and Refactoring
Integrating ChatGPT with Data Engineering Tools
Connecting ChatGPT to Apache Spark Jobs
Automating Airflow DAG Generation
Assisting with Kafka Topic Management
ChatGPT for Dockerfile and Kubernetes YAML Creation
Automation & Documentation with ChatGPT
Auto-generating Project Documentation
Writing README Files and Code Comments
Explaining Data Workflows to Non-Technical Stakeholders
Creating Architecture Diagrams from Text Prompts
ChatGPT for DevOps & Monitoring
Writing Bash Scripts and Monitoring Scripts
Assisting with CI/CD YAML Configuration
Analyzing Log Files with ChatGPT
Suggestions for Performance Tuning
Ethical Use, Risks, and Limitations
Avoiding Over-Reliance on AI
Validating AI-Generated Code and Outputs
Data Privacy & Security Considerations
Responsible Use of Generative AI in Data Teams