Identify assets for cleanup in Databricks

By integrating Databricks with Secoda, you can automate the process of tagging data that has not been accessed within a specified period as "for review" for cleanup. This integration combines Triggers and Actions. Triggers are used to activate the workflow, and you can set schedules like hourly, daily, or custom to trigger subsequent actions. Actions in this integration involve various operations such as filtering and updating metadata. By stacking actions together, you can create detailed workflows tailored to your team's specific requirements. With Secoda, you can efficiently perform bulk updates to metadata in Databricks, streamlining the data cleanup process.

Integrating Databricks with Secoda allows data teams to enhance their data cleanup practices and efficiently prioritize assets. Secoda serves as a comprehensive index of your company's data knowledge by consolidating the data catalog, lineage, documentation, and monitoring into a single data management platform. This integration ensures scalability and streamlines the process of managing and organizing data within Databricks.