validate-data

You can validate data types and schema after a dlt pipeline load by inspecting table structures and applying processing_steps to correct fields. This catches errors early by verifying that loaded data conforms to expected quality standards.

The best way to inspect nested data structures is by visualizing the loaded table schema with Mermaid diagrams and flattening the nested elements. This iterative refinement process ensures accurate data representation in your warehouse.

Yes, you can preview sample data and row counts during schema inspection by accessing the Workspace Dashboard. This allows for a quick review of data integrity immediately after the pipeline load completes.

You can correct incorrect data types in loaded tables by defining processing_steps to refine the columns. This handles nested structures appropriately and ensures accurate data representation based on your specific concerns and pipeline context.

Yes, data validation supports iterative refinement of data shape based on user-defined concerns and pipeline context. It allows you to progressively adjust schemas, correct data types, and flatten nested structures until quality standards are met.