{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/182d0889db6a4e75a9acb7e62e6d0b62\" frameborder=\"0\" width=\"1274\" height=\"955\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":955,"width":1274,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":955,"thumbnail_width":1274,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/182d0889db6a4e75a9acb7e62e6d0b62-1683138152831.gif","duration":930.23,"title":"Datafold Deployment Testing","description":"0:05: Example overview\n\n0:21: Datafold Github integration\n0:30: Datafold pull request comment\n\n1:19: Datafold data-diff UI\n1:33: Datafold data-diff UI: schema tab\n1:40: Datafold data-diff UI: primary keys tab\n1:56: Datafold data-diff UI: values tab\n1:56: Datafold data-diff UI: affected dependencies tab\n2:22: Datafold UI: lineage\n\n2:37 Summary\n\nHey, I'm Kyle with Data Fold, and I'm here to show you how Data Fold does deployment testing. I have a pull request proposing some changes to a single model. It's a simple change, but from the code alone, I have no idea how this change will impact my model or anything downstream. This is where Data Fold fits in. Once you open your pull request, the Data Fold integration will automatically run data diffs between prod and staging, and will flag differences between impacted models. Data Fold will write a comment on the pull request. In just a few minutes, Data Fold told us that we're losing 11 rows from dim orgs and served up the exact records we're losing. With Data Fold, you get the full context of your changes, including downstream effects, directly within the PR. Data Fold automates and standardizes testing that either doesn't happen or is inconsistent in ad hoc. By doing everything automatically, Data Fold's development testing helps you move fast on low impact changes."}