Build an Airflow Data Pipeline to Download Podcasts
- Guided Project
- 2 hours
- Intermediate
- Python
Practice building a flexible data pipeline to download and store podcast episodes using Python and Airflow.
Overview
In this project, you'll assume the role of a data engineer tasked with building a data pipeline to download and store podcast episodes. Using Python and Airflow, a popular data engineering tool, you'll create a four-step pipeline that retrieves podcast metadata, stores it in a SQLite database, and downloads the actual audio files.
Throughout the project, you'll gain hands-on experience with key data engineering concepts and tools. You'll learn how to use Airflow to define and run flexible data pipelines, interact with databases using SQL, and automate the retrieval and storage of data. By the end, you'll have a practical, real-world project to showcase your data engineering skills.
Objective: Build a data pipeline using Airflow to automatically download and store podcast episodes and metadata, demonstrating proficiency in data engineering fundamentals.
What You'll Learn
- ✓ Create a four-step data pipeline using Airflow
- ✓ Create a SQLite database to store podcast metadata into
- ✓ Load podcast metadata into the SQLite database
- ✓ Download podcast episodes
Before You Start
- ✓ Working with Python data types, variables, and functions
- ✓ Implementing control flow statements like loops and conditionals in Python
- ✓ Manipulating and analyzing data using the pandas library
- ✓ Exploring and preparing datasets using Python
Project Steps
6 steps
- 1 Project Overview and Setting up
- 2 Creating the First Task in the Data Pipeline
- 3 Using a SQL Database
- 4 Storing Data in the SQL Database
- 5 Downloading Podcast Episodes
- 6 Conclusions and Next Steps
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