Why this course?

Description

Data Engineering is evolving at lightning speed—and Generative AI is reshaping the way engineers build, optimize, and manage data systems. ChatGPT 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, ChatGPT for Data Engineers, is designed to give you hands-on skills in applying ChatGPT 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, ChatGPT 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 ChatGPT 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 & ChatGPT

• Understand what ChatGPT is, how it works, and why data engineers should care about LLMs.
• Learn ChatGPT’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 ChatGPT

• 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 ChatGPT for code reviews, refactoring, and performance improvements.

Integration with Data Engineering Tools

• Connect ChatGPT 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 ChatGPT

• 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 ChatGPT 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.

What will students learn in your course?

• Understand what ChatGPT and Generative AI are, and why they matter for data engineers.
• Master prompt engineering techniques to craft effective prompts, debug outputs, and build reusable templates.
• Use ChatGPT for data exploration, SQL optimization, and summarization of large datasets.
• Auto-generate and refactor Python scripts, ETL pipelines, and pseudo-code conversions.
• Integrate ChatGPT into your data engineering tools and workflows such as Apache Spark, Apache Airflow, Kafka, Docker, and Kubernetes.
• Automate project documentation, README files, code comments, and even architecture diagrams.
• Leverage ChatGPT for DevOps tasks, including writing Bash scripts, analyzing log files, and tuning performance.
• Recognize the ethical risks, limitations, and data security challenges when using AI in production systems.
• Work on real-world projects like automating data quality checks, generating reports, building ETL workflows, and integrating ChatGPT with APIs.
• Complete a capstone project where you design, document, and implement a data pipeline in Apache Spark and Zeppelin with ChatGPT assistance.

What are the requirements or prerequisites for taking your course?

• Basic knowledge of Data Engineering concepts – familiarity with data pipelines, ETL workflows, or big data tools will be helpful.
• Working knowledge of SQL – you should know how to write basic queries (SELECT, JOIN, GROUP BY).
• Fundamentals of Python programming – ability to read and write simple scripts; advanced knowledge is not required.
• Familiarity with Big Data tools like Apache Spark, Airflow, Kafka, Docker, or Kubernetes is a plus, but not mandatory (the course will guide you on how ChatGPT integrates with them).
• Curiosity to learn Generative AI – no prior AI/ML experience is needed; everything about ChatGPT and prompt engineering is explained from scratch.
• Access to ChatGPT (Free or Plus version) – recommended for hands-on practice during the course.
   

Who is this course 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.