databricks-dq-professionally

To build a scalable data quality framework on Databricks, register generic DQ rules and map them to specific table fields to deterministically evaluate active mappings and produce a consolidated DQ_RESULT string per row for audits and dashboards.

Centralize data quality rules by maintaining a generic rules registry that binds reusable checks like NOT NULL or ISO country codes to specific catalog.schema.table.field combinations with optional category overrides for contextual accuracy across multiple tables.

A per-row DQ_RESULT string is a consolidated output like "RULE_101: 1 | RULE_102: 0" generated by dynamic rule application. It supports data governance by providing auditable traceability for dashboards and regulatory quality standards.

Yes, you can use Databricks for dynamic rule evaluation across schemas by copying source tables into a dedicated DQ schema, applying active field mappings, and enriching outputs into tables like bikes_dq and dq_results for traceability.

The best way to set up a Databricks schema for data quality checks is to automate workspace schema creation, copy source tables into the dq_professional workspace, and run an enrichment notebook to generate tables with a DQ_RESULT column for dashboards.

Yes, this data quality approach supports remediation workflows on Databricks by outputting enriched tables and auditable dq_results that standardize checks and centralize rule definitions for tracking and resolving data quality issues.