Last updated
Last updated
CsvPath is a framework for easy and robust data onboarding. It verifies that CSV, Excel, and other delimited and tabular data files meet expectations and enter the organization in a controlled way. CsvPath's approach to edge governance is opinionated, prescriptive, and super productive.
The CsvPath Validation Language is simple, easy to integrate, and flexible enough to handle the unexpected. Inspired by Schematron, XPath, and the Collect, Store, Validate design pattern, CsvPath Validation Language brings rules-based data validation to less structured data.
The CsvPath Library implements CsvPath Validation Language within a complete Collect, Store, Validate Pattern framework that makes data onboarding and publishing faster, cost-efficient, and more effective. CsvPath fills the blindspot between MFT (managed file transfer) and the data lake with a simple path to provably correct data.
This data onboarding blindspot is a big deal. Think about it. If even 1 in 30 companies depends heavily on CSV or Excel data, the lack of good delimited file validation is a trillion-dollar problem. In our experience, 1 in 30 would be a low estimate.
CsvPath isn't the silver bullet to reams of messy delimited data, but it can help build confidence that your data governance doesn't turn a blind eye to your most unruly data.
Take a look through these pages and cruise over to the detailed docs on the CsvPath Github to see if open source CSV and Excel data validation should be part of your DataOps toolkit.
5-minutes to get the idea
