CSV Data Pipeline Tips: Database vs. Pandas
When to use a database for CSV data pipelines versus sticking with pandas for visualisation and analysis.
CSV Data Pipeline Tips: Database vs. Pandas
When it comes to handling CSV data pipelines, a common question arises: should you load your CSV files into a database or stick with pandas for visualisation and analysis?
I’ve seen a lot of folks wondering whether to load CSV data into a database or stick with something simpler like pandas for visualisation pipelines. Honestly, it really depends on your specific situation.
If your goal is to build a self-serve analytics tool for your team, where they can filter, slice, and dive into data independently, then having a structured database (even a lightweight option like DuckDB or cloud-based PostgreSQL) is definitely worth considering, especially if you plan to add a BI tool or dashboard later. Just keep in mind, databases do introduce some setup and ongoing maintenance.
But if you’re mostly generating charts or summaries from selected data batches, pandas might actually be enough. You can simply scan your directory, load only the necessary files, and cache results to improve performance. It’s straightforward, flexible, and usually sufficient if your CSV files aren’t massive.
Before choosing, here’s what I recommend asking yourself:
How big are your CSV files—are we talking megabytes or gigabytes?
Are the column structures consistent across files?
Does your team need direct access to explore raw data?
How often does new data land in your data lake?
Do you perform heavy computations or aggregations frequently that SQL would simplify?
Answering these questions first makes it much easier to determine if a full database solution is needed or if a tidy pandas workflow could simplify your life.