Automations

Identify orphaned data in dbt

⚡️ Automation

Identify orphaned data in dbt 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 orphaned data in dbt

⚡️ Automation

Identify orphaned data in dbt 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

Integrating dbt into data cleanup practices is valuable because it enables the identification of orphaned data. Orphaned data refers to data that no longer has an assigned team or user. Identifying such data is important to maintain data integrity, optimize storage usage, enhance system performance, and ensure accurate data analysis and reporting processes. By tagging orphaned data for review, organizations can efficiently manage their data ecosystem and resolve any potential issues. With the help of dbt integration, this task becomes more streamlined and effective.

How it works

Integration with dbt allows for automated workflows that consist of triggers and actions. Triggers can be scheduled to activate the workflow at specific intervals, such as hourly, daily, or custom schedules. Actions provide various operations, such as filtering and updating metadata, allowing you to create customized workflows for your team. In the case of Secoda, you can utilize this integration to perform bulk updates to metadata in dbt. For instance, you can easily identify data that lacks an assigned team or user and tag it for review, ensuring comprehensive organization and management of your data.

About Secoda

Secoda, the data management platform, brings together various components of your company's data knowledge such as data catalog, lineage, documentation, and monitoring. By integrating Secoda with dbt, data teams can efficiently scale their data enablement practices. This integration simplifies the process of maintaining trust scorecards, enabling teams to easily track and manage data quality and reliability.

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