10 Steps to Solving for Data Quality in Databricks

10 Steps to Solving for Data Quality in Databricks

You migrated to Databricks. Great! But can you trust your data?

We’ve outlined the most critical steps you need to take - and processes you need to invest in - to ensure your analytics are trustworthy and your organization is making the most out of your lakehouse. 

Access this eBook to learn how to:

  • Take your data quality game to the next level with native Snowflake features Unity Catalog, Delta Lake schema validation, and SQL queries for specific business logic.
  • Implement strong data quality processes such as quarterly data audits, data SLAs, and assigning domain owners to data assets.
  • Leverage industry leading solutions like end-to-end lineage, pipeline testing, data observability, data audit logging, and more.

Don’t let Databricks and other data investments go to waste! Your data quality journey begins with these 10 steps.

Access the Guide:

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