How do you categorize and validate data quality within your Spark ETL workflows on Databricks?

Data quality in a Databricks Spark ETL pipeline is typically assessed across several dimensions: completeness, consistency, accuracy, timeliness, and uniqueness. I start by defining a data quality framework that maps each dimension to specific validation rules. For completeness, I use schema enforcement and null‑count checks; for consistency, I validate referential integrity across tables; for accuracy, I compare source values against reference datasets or business rules; for timeliness, I monitor ingestion lag and timestamp drift; and for uniqueness, I enforce primary key constraints or deduplication logic.

I implement these checks using Spark DataFrames and Delta Lake’s built‑in schema enforcement. Validation logic is encapsulated in reusable UDFs or Spark SQL expressions, and results are written to a dedicated quality‑metrics table. I also leverage Databricks notebooks to visualize metrics with widgets and dashboards, and I trigger alerts via Azure Monitor or PagerDuty when thresholds are breached. Finally, I integrate these checks into the CI/CD pipeline so that any regression in data quality is caught before production deployment.