🎓 Course [UDEMY] ᑕᕼᗩTGᑭT for Data Engineers

Description​

Data Engineering is evolving at lightning speed—and Generative AI is reshaping the way engineers build, optimize, and manage data systems. ᑕᕼᗩTGᑭT is not just a chatbot; it’s a productivity amplifier, a coding assistant, and a knowledge partner that can help you accelerate data engineering tasks, automate documentation, and simplify complex workflows.
This course, ᑕᕼᗩTGᑭT for Data Engineers , is designed to give you hands-on skills in applying ᑕᕼᗩTGᑭT and Large Language Models (LLMs) to real-world data engineering challenges. Whether you are writing SQL queries, debugging ETL pipelines, creating Airflow DAGs, or generating project documentation, ᑕᕼᗩTGᑭT can act as your co-pilot—saving time, improving quality, and enabling you to focus on solving higher-level engineering problems.
By the end of this course, you’ll not only understand how ᑕᕼᗩTGᑭT works, but also how to use it effectively in your day-to-day work as a data engineer. With practical examples, guided projects, and capstone assignments, you will gain confidence in leveraging AI responsibly in your professional workflows.
What You Will Learn
Foundations of Generative AI & ᑕᕼᗩTGᑭT
Understand what ᑕᕼᗩTGᑭT is, how it works, and why data engineers should care about LLMs.
Learn ᑕᕼᗩTGᑭT’s strengths, limitations, and responsible use cases.
Prompt Engineering for Data Engineers
Master the art of writing precise prompts for SQL, Python, ETL, and documentation tasks.
Explore prompt patterns, templates, and debugging techniques.
SQL & Data Exploration with ᑕᕼᗩTGᑭT
Auto-generate, optimize, and explain SQL queries.
Perform data profiling, summarization, and cleaning with AI assistance.
Python & ETL Pipelines
Generate Python scripts, convert pseudocode into production-ready code, and build ETL workflows.
Use ᑕᕼᗩTGᑭT for code reviews, refactoring, and performance improvements.
Integration with Data Engineering Tools
Connect ᑕᕼᗩTGᑭT with Apache Spark, Airflow, Kafka, Docker, and Kubernetes.
Automate repetitive engineering tasks with AI guidance.
Automation & Documentation
Create high-quality project documentation, README files, and code comments instantly.
Generate architecture diagrams and explain workflows to both technical and non-technical stakeholders.
DevOps & Monitoring with ᑕᕼᗩTGᑭT
Write Bash scripts, CI/CD configurations, and monitoring tools.
Analyze logs and troubleshoot performance issues with AI assistance.
Ethical & Responsible AI Use
Learn the risks of over-reliance on AI and how to validate outputs.
Understand data privacy, security considerations, and responsible AI practices.
Real-World Projects & Capstone
Build an end-to-end ETL workflow with ᑕᕼᗩTGᑭT as your assistant.
Automate data quality checks and reporting pipelines.
Design and document data pipelines using AI-powered workflows.
Complete a capstone project integrating Apache Spark and Apache Zeppelin.
Why Take This Course?
Hands-On Learning: Includes multiple practice sessions and guided exercises.
Real-World Focus: Covers practical data engineering workflows instead of abstract AI theory.
Capstone Projects: Apply your skills to build, automate, and document real data pipelines.
Future-Proof Your Skills: Learn how to collaborate with AI tools and stay competitive in the era of Generative AI.

• Understand what ᑕᕼᗩTGᑭT is, how it works, and why data engineers should care about LLMs.
• Learn ᑕᕼᗩTGᑭT’s strengths, limitations, and responsible use cases.
• Master the art of writing precise prompts for SQL, Python, ETL, and documentation tasks.
• Explore prompt patterns, templates, and debugging techniques.
• Auto-generate, optimize, and explain SQL queries.
• Perform data profiling, summarization, and cleaning with AI assistance.
• Generate Python scripts, convert pseudocode into production-ready code, and build ETL workflows.
• Use ᑕᕼᗩTGᑭT for code reviews, refactoring, and performance improvements.
• Connect ᑕᕼᗩTGᑭT with Apache Spark, Airflow, Kafka, Docker, and Kubernetes.
• Automate repetitive engineering tasks with AI guidance.
• Create high-quality project documentation, README files, and code comments instantly.
• Generate architecture diagrams and explain workflows to both technical and non-technical stakeholders.
• Write Bash scripts, CI/CD configurations, and monitoring tools.
• Analyze logs and troubleshoot performance issues with AI assistance.
• Learn the risks of over-reliance on AI and how to validate outputs.
• Understand data privacy, security considerations, and responsible AI practices.
• Build an end-to-end ETL workflow with ᑕᕼᗩTGᑭT as your assistant.
• Automate data quality checks and reporting pipelines.
• Design and document data pipelines using AI-powered workflows.
• Complete a capstone project integrating Apache Spark and Apache Zeppelin.
• Hands-On Learning: Includes multiple practice sessions and guided exercises.
• Real-World Focus: Covers practical data engineering workflows instead of abstract AI theory.
• Capstone Projects: Apply your skills to build, automate, and document real data pipelines.
• Future-Proof Your Skills: Learn how to collaborate with AI tools and stay competitive in the era of Generative AI.

Who this course is for:​

• Data Engineers looking to enhance productivity and automate repetitive tasks.
• Aspiring Data Professionals (SQL developers, Python programmers, BI engineers) who want to stay ahead in the AI-driven data world.
• Software Engineers & DevOps Engineers working with data workflows and automation.
• Technical Managers & Team Leads interested in exploring how AI can accelerate data projects.

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