Hi shubham,

How are you doing today?, It’s great to see your team focusing on data quality using the DQX framework—it’s a solid tool for keeping your data clean and reliable. To get started, I’d suggest beginning with simple checks like NOT NULL, IN RANGE, or UNIQUE validations, and apply them early in your pipeline (ideally right after raw ingestion). You can find setup guidance and rule examples in the official DQX GitHub repo, though it’s a bit light on detailed docs, so testing in a dev environment first really helps. When applying rules, make sure your table and column names match exactly, and double-check data types to avoid silent issues. If you run into problems, the cluster logs usually give clues—especially for schema mismatches. Also, consider structuring your checks in a reusable format (like YAML or functions) to make it easier to scale across pipelines. Let me know if you want help with a simple template or sample use case—happy to help!

Regards,

Brahma