Automations

Identify assets for cleanup in Sigma

⚡️ Automation

Identify assets for cleanup in Sigma 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

Identify assets for cleanup in Sigma

⚡️ Automation

Identify assets for cleanup in Sigma 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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Sigma Computing
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Overview

Integration with Sigma allows for efficient data cleanup by automatically tagging data that has not been accessed within a specified time period as "for review." Data cleanup plays a crucial role in maintaining the accuracy, reliability, and consistency of datasets. This process ensures that analyses and decisions made using the data remain valid and informed, ultimately improving the overall quality of the data.

How it works

One way to achieve the desired task of tagging data from Sigma for review and cleanup is by using the integration feature, Sigma, available in Secoda. By leveraging the automation capabilities of Sigma, you can create workflows consisting of triggers and actions to streamline the process. Triggers in Sigma automation can be set up to activate the workflow based on specific schedules, such as hourly, daily, or custom schedules. The actions available in Sigma automation allow for various operations, such as filtering and updating metadata. By stacking these actions, you can create detailed workflows tailored to meet the specific needs of your team. This integration also enables you to perform bulk updates to metadata in Sigma, facilitating efficient and streamlined data cleanup processes.

About Secoda

The integration of Sigma with Secoda enhances data cleanup practices for data teams by providing a convenient way to prioritize assets. Secoda acts as a centralized platform that consolidates your company's data catalog, lineage, documentation, and monitoring. By incorporating Sigma, teams can scale their data operations more effectively.

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