Chatgpt For Data Engineers

Practical Applications of ChatGPT for Modern Data Engineers

What you'll learn

Learn how to use ChatGPT to write, debug, and optimize code for data pipelines, SQL queries, and automation scripts across tools like Spark, Airflow, and Bash.

Master Prompt Engineering for Data Use Cases

Discover how to apply ChatGPT in real-life use cases including pipeline creation, performance tuning, schema design, and cloud-based deployment.

Create custom workflows and tools using ChatGPT and APIs to automate repetitive tasks, enhance productivity, and boost team collaboration.

Requirements

Basic Understanding of Data Engineering Concepts

Working Knowledge of SQL and Python

Comfort with the Command Line & Scripting

Curiosity About AI and Automation

Optional: Familiarity with Tools Like Spark, Airflow, or Docker

Description

In today’s fast-paced data-driven world, efficiency, automation, and innovation are key to staying ahead. This course, "ChatGPT for Data Engineers," is designed to empower data professionals with the practical skills and strategies to leverage Generative AI tools like ChatGPT in real-world data engineering workflows.Whether you're writing complex SQL queries, designing scalable ETL pipelines, automating documentation, or debugging code, ChatGPT can act as your virtual co-pilot. This course blends hands-on demos, best practices, and real-world use cases to help you integrate ChatGPT into every stage of your data engineering lifecycle — from ideation to implementation.You'll learn how to use ChatGPT to:Generate, optimize, and explain SQL queries for different databasesAutomate data pipeline tasks and script generation in Python, Bash, and SparkTranslate requirements into technical specifications using prompt engineeringBuild AI-powered tools to streamline documentation, data profiling, and quality checksBy the end of this course, you’ll not only understand the capabilities and limitations of ChatGPT, but also have the skills to apply it confidently in your daily workflow to save time, improve accuracy, and drive innovation.Perfect for aspiring and experienced data engineers, data analysts, and technical leads looking to future-proof their skill set with cutting-edge AI tools.

Overview

Section 1: Introduction to ChatGPT for Data Engineers

Lecture 1 What is ChatGPT? Overview of Generative AI

Lecture 2 Why Data Engineers Should Care About LLMs

Lecture 3 Capabilities & Limitations of ChatGPT

Lecture 4 ChatGPT Sneak Peek

Lecture 5 Hands On Practice Part 1

Lecture 6 Use Cases of ChatGPT in Data Engineering

Lecture 7 Hands On Practice Part 2

Section 2: Mastering Prompt Engineering

Lecture 8 What is Prompt Engineering?

Lecture 9 Crafting Effective Prompts for Data Tasks

Lecture 10 Hands On Practice Part 3

Lecture 11 Prompt Patterns: Templates, Chains, and Variables

Lecture 12 Debugging and Refining Prompts for Better Results

Section 3: ChatGPT for Data Exploration and SQL

Lecture 13 Writing and Optimizing SQL Queries with ChatGPT

Lecture 14 Data Profiling and Summarization

Lecture 15 Explaining Complex Queries and Database Schemas

Lecture 16 Data Cleaning Suggestions Using AI

Section 4: ChatGPT for Python & Data Pipelines

Lecture 17 Auto-generating Python Scripts and Functions

Lecture 18 Converting Pseudo-code to Production-ready Code

Lecture 19 Writing ETL Scripts with ChatGPT

Lecture 20 Using ChatGPT for Code Reviews and Refactoring

Section 5: Integrating ChatGPT with Data Engineering Tools

Lecture 21 Connecting ChatGPT to Apache Spark Jobs

Lecture 22 Automating Airflow DAG Generation

Lecture 23 Assisting with Kafka Topic Management

Lecture 24 ChatGPT for Dockerfile and Kubernetes YAML Creation

Section 6: Automation & Documentation with ChatGPT

Lecture 25 Auto-generating Project Documentation

Lecture 26 Writing README Files and Code Comments

Lecture 27 Explaining Data Workflows to Non-Technical Stakeholders

Lecture 28 Creating Architecture Diagrams from Text Prompts

Section 7: ChatGPT for DevOps & Monitoring

Lecture 29 Writing Bash Scripts and Monitoring Scripts

Lecture 30 Assisting with CI/CD YAML Configuration

Lecture 31 Analyzing Log Files with ChatGPT

Lecture 32 Suggestions for Performance Tuning

Section 8: Ethical Use, Risks, and Limitations

Lecture 33 Avoiding Over-Reliance on AI

Lecture 34 Validating AI-Generated Code and Outputs

Lecture 35 Data Privacy & Security Considerations

Lecture 36 Responsible Use of Generative AI in Data Teams

Aspiring & Junior Data Engineers,Mid-Level & Senior Data Engineers,Data Analysts & Scientists,ETL Developers & Pipeline Architects,DevOps & MLOps Practitioners,Tech-Curious Professionals