Automations

Usage monitoring for Datafold

⚡️ Automation

Usage monitoring for Datafold with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.

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Automations

Usage monitoring for Datafold

⚡️ Automation

Usage monitoring for Datafold with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.

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Overview

By leveraging Secoda, you can effectively monitor the usage levels of data resources and metadata from Datafold. This integration ensures a smooth and secure migration process. Datafold, a specialized platform, primarily addresses analytical data quality concerns. It facilitates automated testing for data engineers, enabling them to validate dbt model changes throughout development, deployment, and migrations. The platform empowers users to compare data differences between different stages, enhancing overall efficiency and safety.

How it works

The integration with Datafold in Secoda allows you to closely monitor the usage levels of your data resources through the Automations feature. Automations consist of Triggers and Actions. Triggers determine when the workflow is activated, and you can set them to occur at specific intervals such as hourly, daily, or on a custom basis. Actions encompass various operations like filtering and updating metadata, allowing you to build detailed workflows that suit your team's requirements. This integration also enables you to perform bulk updates to metadata in Datafold, streamlining your data management processes.

About Secoda

Datafold and Secoda can be integrated to facilitate data migration and monitor the usage levels of data resources. Secoda acts as a comprehensive data knowledge index, bringing together your company's data catalog, lineage, documentation, and monitoring functionalities into one centralized data management platform. By leveraging this integration, organizations can streamline their data migration processes and gain valuable insights into the utilization of their data resources.

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