Usage monitoring for Databricks 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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Secoda can seamlessly integrate with Databricks, a cloud-based data engineering platform. Databricks offers a collaborative environment for big data and machine learning, making it effortless for data scientists and engineers to analyze and process large datasets. Built on Apache Spark, Databricks provides automated cluster management and IPython-style notebooks. By integrating with Secoda, you can efficiently monitor the usage levels of data resources and metadata from Databricks. This integration ensures a smooth and secure migration process, allowing for efficient management of your data.
Secoda, when integrated with Databricks, allows users to monitor the usage levels of data resources using its Automations feature. Automations consist of Triggers and Actions. Triggers initiate the workflow and can be set to activate at specific schedules or customized intervals. Actions encompass various operations, such as filtering and updating metadata, which can be stacked to create customized workflows. Secoda also enables bulk updates to metadata in Databricks.
Secoda and Databricks can be integrated to facilitate data migration and monitor data resource usage levels. Secoda acts as a comprehensive data management platform, consolidating your company's data catalog, lineage, documentation, and monitoring capabilities. By leveraging the integration with Databricks, you can effectively manage and track the movement of data while ensuring optimal utilization of resources.
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