As a solo BI analyst, I recently built my first data pipeline using Python. It was a daunting task, especially since I didn’t have a data engineer to guide me. But I learned a lot from the experience, and I’m excited to share my process with you.
## The Pipeline Logic
I used a combination of Python libraries, Selenium automation, and Power BI to extract, transform, and load data from various sources. Here’s a high-level overview of my pipeline:
* Imported necessary libraries and configured environment variables
* Used Selenium to automate interactions with a dashboard and download Excel files
* Processed Excel files, applied data transformations, and saved them to an output directory
* Converted Excel files to Parquet for efficient storage and querying
* Loaded Parquet files into a MotherDuck database
* Aggregated and transformed data into Power BI-ready tables using SQL queries
* Built a data dashboard and automated data refresh using Power Automate
* Integrated with Slack to send daily summaries of data refresh status and key outputs
## Lessons Learned
Looking back, I’m proud of what I accomplished, but I know my code isn’t perfect. Here are some lessons I learned along the way:
* Quality auditing is crucial: Without a tutor, it’s essential to have a process in place to review and test your pipeline regularly.
* Best practices matter: I learned the importance of following best practices, such as defining helper functions, using environment variables, and documenting my code.
* Automation is key: Automating repetitive tasks, like data refresh and report sending, saved me a lot of time and reduced the risk of human error.
## Your Turn
If you’re a seasoned data engineer or analyst, I’d love to hear your thoughts on my pipeline. What would you do differently? What best practices can you share with me?
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*Further reading: [Data Pipeline Best Practices](https://towardsdatascience.com/data-pipeline-best-practices-4c935a6f6f6b)*