Automations

Identify assets for cleanup in Airbyte

⚡️ Automation

Identify assets for cleanup in Airbyte 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 Airbyte

⚡️ Automation

Identify assets for cleanup in Airbyte 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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Integration
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Airbyte
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Overview

Integration with Airbyte can greatly facilitate the process of data cleanup. By tagging data that has not been accessed within a certain time period as 'for review,' organizations can identify and address any inconsistencies, inaccuracies, or outdated information. This ensures that the datasets remain reliable, consistent, and accurate, supporting valid analysis and informed decision-making. It ultimately enhances the overall quality of the data, benefiting the organization as a whole.

How it works

Integration with Airbyte allows users to automate workflows through triggers and actions. Triggers set the schedule for activating the workflow, whether it's on an hourly, daily, or custom basis. Actions provide various operations, such as filtering and updating metadata, allowing users to tailor detailed workflows according to their team's needs. Secoda enables bulk updates to metadata in Airbyte, facilitating efficient management and organization of data. As a result, users can effectively tag data from Airbyte that hasn't been accessed within a specific period, marking it as "for review" to initiate cleanup processes.

About Secoda

Airbyte is a data integration tool that, when used in conjunction with Secoda, assists data teams in expanding their data cleanup processes. By prioritizing assets, Secoda enables data teams to effectively manage and streamline their data catalog, lineage, documentation, and monitoring. With the integration of Airbyte, Secoda becomes a comprehensive data management platform, aiding in the organization and optimization of data practices.

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