Identify assets for cleanup in Snowflake with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.
Get startedIntegration with Snowflake allows for efficient data cleanup by tagging data that has not been accessed within a specific time frame as 'for review.' This process helps to maintain the accuracy, reliability, and consistency of datasets. It ensures that analyses and decisions made using this data are valid and informed, thus improving the overall quality of data. Cleaning up data is essential for effective data management.
In Snowflake, you can integrate automation into your workflow to efficiently handle data tasks. This involves two key components: Triggers and Actions. Triggers act as the catalyst for the workflow, allowing you to define schedules such as hourly, daily, or custom timeframes. By setting up triggers, you establish a structured framework for initiating subsequent actions. Actions, on the other hand, encompass a wide range of operations, including filtering and updating metadata. By stacking actions, you can create customized workflows that cater to your team's unique requirements. With Secoda, you also have the capability to perform bulk updates to metadata in Snowflake. In the given context, you can tag data from Snowflake that has not been accessed within a certain period as "for review" for cleanup purposes.
Secoda's integration with Snowflake offers data teams the capability to scale their data cleanup practices effectively. By leveraging Snowflake's powerful platform and combining it with Secoda's comprehensive data management solution, users can effortlessly prioritize their assets. Secoda acts as a centralized index of your company's data knowledge, consolidating data catalog, lineage, documentation, and monitoring. With this integration, data teams can streamline their data processes and enhance their overall data management capabilities.