Path overview
In this path, you’ll learn how to build data pipelines in Python and orchestrate them with Apache Airflow. You’ll go from foundational pipeline concepts to automating complex workflows through hands-on, real-world scenarios.
Key skills
- Building reliable data pipelines with Python
- Orchestrating and scheduling workflows with Apache Airflow
- Applying pipeline automation patterns to production data engineering
Path outline
Data Pipelines with Airflow [2 courses]
Course 1: Building a Data Pipeline 4h
Course Objectives- Define functional programming
- Define advanced Python concepts such as closures and decorators
- Write a robust data pipeline with a scheduler in Python
Course 2: Building Data Pipelines with Apache Airflow 8h
Course Objectives- Understand workflow orchestration concepts and how Airflow coordinates complex data pipelines
- Deploy Apache Airflow using Docker Compose with all core services including scheduler, web UI, and workers
- Build DAGs using the TaskFlow API for clean, Pythonic workflow definitions
- Implement Dynamic Task Mapping to process multiple datasets in parallel
- Connect Airflow to databases and manage credentials securely through Connections and environment variables
- Integrate Git and git-sync to enable version-controlled DAG deployment
- Implement CI/CD pipelines using GitHub Actions to validate and deploy DAGs automatically
- Build production-ready ETL pipelines that extract data from APIs, transform it with Python, and load it into databases
Projects in this path
Hacker News Pipeline
For this project, we’ll step into the role of data engineers to process Hacker News posts using Python. We’ll apply skills in JSON parsing, string cleaning, and building data pipelines.
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