What you’ll learn
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Understand what ChatGPT and Generative AI are, and why they matter for data engineers.
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Master prompt engineering techniques to craft effective prompts, debug outputs, and build reusable templates.
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Use ChatGPT for data exploration, SQL optimization, and summarization of large datasets.
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Auto-generate and refactor Python scripts, ETL pipelines, and pseudo-code conversions.
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Integrate ChatGPT into your data engineering tools and workflows such as Apache Spark, Apache Airflow, Kafka, Docker, and Kubernetes.
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Automate project documentation, README files, code comments, and even architecture diagrams.
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Leverage ChatGPT for DevOps tasks, including writing Bash scripts, analyzing log files, and tuning performance.
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Recognize the ethical risks, limitations, and data security challenges when using AI in production systems.
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Work on real-world projects like automating data quality checks, generating reports, building ETL workflows, and integrating ChatGPT with APIs.
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Complete a capstone project where you design, document, and implement a data pipeline in Apache Spark and Zeppelin with ChatGPT assistance.
Requirements
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Basic knowledge of Data Engineering concepts – familiarity with data pipelines, ETL workflows, or big data tools will be helpful.
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Working knowledge of SQL – you should know how to write basic queries (SELECT, JOIN, GROUP BY).
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Fundamentals of Python programming – ability to read and write simple scripts; advanced knowledge is not required.
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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).
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Curiosity to learn Generative AI – no prior AI/ML experience is needed; everything about ChatGPT and prompt engineering is explained from scratch.
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Access to ChatGPT (Free or Plus version) – recommended for hands-on practice during the 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.
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.
