data-quality

Automate data quality validation in Spark and Databricks by applying Great Expectations, DLT checks, and custom validators to enforce completeness, accuracy, consistency, and referential integrity across your data pipelines.

Implement data contracts and enforce data quality rules by defining completeness, uniqueness, format, and range thresholds, then integrating these checks into your pipelines to generate alerts and quality metrics for cross-team governance.

Yes, you can use DLT quality checks alongside Great Expectations for data profiling. This combination enables structured quality checks, automated profiling, and reporting of quality metrics within your Databricks environment.

Set up data quality alerts and monitor metrics in Delta Live Tables by configuring DLT checks and custom validators, allowing you to iterate on validation thresholds against sample datasets and report results.

Custom validators for data quality checks require configuring Great Expectations and DLT environments in your Databricks workspace, meaning you must define a minimal set of quality rules and iterate on thresholds manually against sample datasets.

Data pipelines suffer from downstream failures and eroded trust due to unmanaged data quality issues. Applying automated validation checks for completeness, accuracy, consistency, and referential integrity prevents these downstream failures.