Project 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.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- 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
Projects steps
Step 1: Project Overview and Setting up
Step 2: Creating the First Task in the Data Pipeline
Step 3: Using a SQL Database
Step 4: Storing Data in the SQL Database
Step 5: Downloading Podcast Episodes
Step 6: Conclusions and Next Steps
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