What you’ll learn
- 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.
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.
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