Automations

Usage monitoring for Sigma

⚡️ Automation

Usage monitoring for 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

Usage monitoring for Sigma

⚡️ Automation

Usage monitoring for 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

In order to ensure a smooth and secure migration process, it is important to monitor the usage levels of data resources from Sigma, a cloud-native analytics platform. Sigma Computing offers a user-friendly spreadsheet interface that allows business users to effortlessly access, explore, and gain insights from their data. This platform requires no coding or specialized training, enabling users to analyze billions of rows, incorporate new data, and conduct real-time "what if" analysis. By integrating Sigma with Secoda, organizations can effectively track the usage levels of metadata and ensure an efficient and safe migration.

How it works

Integration with Sigma allows you to monitor data resource usage levels through the Secoda Automations feature. Automations consist of two components: Triggers and Actions. Triggers initiate the workflow and can be scheduled on an hourly, daily, or custom basis. Actions encompass various operations such as filtering and updating metadata. By combining multiple actions, you can create customized workflows that cater to your team’s requirements. Additionally, Secoda enables you to make bulk updates to metadata in Sigma.

About Secoda

Integration with Secoda allows for the utilization of Sigma and Secoda for data migration purposes, enabling the monitoring of data resource usage levels. Secoda acts as a comprehensive data knowledge index, consolidating data catalog, lineage, documentation, and monitoring functionalities into a unified data management platform. This integration facilitates efficient data migration and provides insights into data resource utilization.

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