In today's fast-paced data environment, ensuring that changes to your dbt models don't inadvertently cause issues downstream is a crucial part of maintaining a healthy data ecosystem. With the introduction of Sifflet’s new feature—dbt Impact Analysis for GitHub and GitLab, data teams now have a powerful tool to assess and prevent potential disruptions before they reach production.
This new feature automatically adds a comment to pull requests or merges in GitHub or GitLab, displaying a list of impacted assets (tables, dashboards, etc.) when someone modifies a dbt model. For example, if a change to a dbt model impacts other tables or dashboards, Sifflet generates an Impact Report listing each of these assets, helping teams quickly assess the extent of the potential impact.
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The introduction of the Sifflet’s Dbt Impact Analysis for GitHub and GitLab represents a significant advancement in data observability. Here’s why:
As Sifflet continues to expand its platform, we are excited to announce that this feature will soon be available for Bitbucket as well. By building out our dbt Impact Analysis for Bitbucket, Sifflet will enable more data teams to benefit from this powerful tool, ensuring seamless impact tracking across multiple version control platforms. This is part of our ongoing commitment to providing comprehensive support for diverse ecosystems and ensuring every team has the tools to manage their dbt models effectively.
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