Create a Basic Data Flow System using Python and Docker
In the world of data-driven businesses, reliable and efficient data pipelines are essential for any professional working with data. This article explores how to build a straightforward data pipeline using Python and Docker, with a heart attack dataset from Kaggle as an example.
Data pipelines are systems designed to move and transform data from one source to another. They typically follow a standard pattern known as ETL (Extract, Transform, Load). This process involves extracting data from a source, performing transformations, and loading the cleaned data into a new location.
To build a simple ETL data pipeline, you can follow these steps:
- Extract: Load the heart attack dataset CSV into a pandas DataFrame using Python.
- Transform: Clean the dataset by handling missing values and normalizing column names.
- Load: Save the transformed data back as a cleaned CSV file.
- Dockerize: Package the Python ETL script inside a Docker container to ensure environment consistency.
An example Python script () for the ETL process could look like this:
```python
```
Next, create a to containerize this pipeline:
```
```
To run the pipeline using Docker, place your inside a local folder, and run the container with:
By following these steps, you can build a simple yet effective data pipeline using Python and Docker. For more complex workflows, you might consider integrating Docker Compose to manage multiple services or automate the pipeline execution.
Cornellius Yudha Wijaya, a data science assistant manager and data writer, shares Python and data tips via social media and writing media. If you're interested in learning more about data pipelines and Python, be sure to follow Cornellius on Instagram and DataQuest.io.
References: - [1] https://www.kdnuggets.com/build-your-own-simple-data-pipeline-with-python-and-docker - [2] https://www.instagram.com/p/DL3MHaYzl5L/ - [3] https://www.dataquest.io/blog/intro-to-docker-compose/
- The heart attack dataset CSV, utilized in this example, can be loaded into a pandas DataFrame using Python for the extraction phase of the ETL process.
- To clean the loaded dataset, Python scripts will handle missing values and normalize column names before continued processing.
- After transformations are performed, the cleaned data will be saved back as a new CSV file.
- To ensure consistency in the data pipeline environment, the Python ETL script can be packaged inside a Docker container with Dockerization.
