Hey everyone! I've been running into a recurring issue in my projects that I think many developers face — dealing with CSV or Excel uploads and webhook payloads that are often inconsistent and messy. I'm particularly interested in hearing about how you handle: 1) CSV/Excel uploads that have missing or inconsistent headers, 2) webhook payloads from different services which come with various field structures, and 3) JSON outputs from AI models that don't match the expected schema.
In the past, I've always written custom parsers and converters for each situation, but lately, I've been trying out an 'API plugin' approach that cleans, maps, and validates inputs before they reach the core of the application. This has been such a time-saver! What strategies or tools do you guys use to normalize these types of inputs?
If anyone's interested, I can show a demo of my API-based approach, but I'd love to hear about your current workflows first!
5 Answers
Providing sample CSVs for download with the right headers set up can make a big difference. It gives users a clear reference!
Adding a correct template download button near the uploader can make it super easy for users to get it right the first time. Just a small change that helps a lot!
Check out csvbox.io — it's a neat hosted solution for CSV imports that could save you some hassle!
I tend to take a more straightforward approach by rejecting formats that don't meet the criteria. If people want it done right, they usually adapt to the requirements. I might include a downloadable template for users to fill out correctly, but overall, I believe in minimal hand-holding when it comes to user uploads.
Exactly! Providing a clear template helps a lot. I just normalize headers a bit, but I'm not going overboard unless it's a paid service.
Totally agree! If it's editable by humans, some errors are just part of the game. Offer them an option for stricter handling if they want.
I had a similar issue at my last job with CSV uploads. I set up a process where users have to map the columns in their uploaded CSV to the expected column names before validation. If their columns matched, it skipped this step, but if not, it guided them through the mapping. After ensuring everything is mapped correctly, we validate each row and inform users about any issues directly, including the specific rows and error types. This approach really helped reduce complaints and made users more comfortable with the system!
That's exactly what I was going to suggest! Showing a mapping or preview stage is so helpful. Notifying them about missing data with a marker also keeps things clear.
This is similar to how ArcGIS handles CSV uploads, and honestly, it's a lifesaver for dealing with poorly named columns.
Sounds interesting! I've been looking into alternatives. I actually prefer an SDK/API that cleans, validates, and normalizes not just CSVs but also webhook and JSON payloads into specific schemas. It really streamlines the process with a nice audit trail.