How to validate data transformation logic before running processes on large datasets

How to validate data transformation logic before running processes on large datasets

This task can be performed using Yorph

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Yorph

Yorph

analytics

An agentic data platform that democratizes data preparation by allowing business domain experts to directly work with their data. It let's business users (PMs, analysts, operations teams) sync, clean, transform, analyze, visualize data through workflows, by simply asking. We help users validate their logic through dry runs and clarifying questions, ensuring correctness before running on big datasets. And the platform includes built-in semantic awareness that continuously evolves as users interact with the agent.

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What to expect from an ideal product

  1. Run small sample tests on your data before processing the full dataset to catch errors early and save processing time
  2. Ask the system clarifying questions about your transformation steps to make sure the logic matches what you actually want to accomplish
  3. Use dry run features that show you exactly what will happen to your data without actually changing anything or using compute resources
  4. Get instant feedback on whether your transformation rules make sense for your specific data structure and business context
  5. Preview the expected output format and results on a subset of rows so you can spot issues before running expensive operations on millions of records

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