{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/648c4ab95eac422c85455e778f57434d\" frameborder=\"0\" width=\"1364\" height=\"1023\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1023,"width":1364,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1023,"thumbnail_width":1364,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/648c4ab95eac422c85455e778f57434d-26bfb122e8ae5ac2.gif","duration":156.352,"title":"Databricks: New enterprise app in Make","description":"This Loom shows how to automate extracting and acting on churn risk outputs from Databricks using Make. It describes Databricks as the data platform behind company AI and machine learning, used by over 20,000 organizations including Adidas, Adobe, and 7-Eleven. Using a Gymshark example, the creator builds a three-module Make scenario that runs an SQL query in Databricks Warehouse to pull high-risk customers, iterates through each customer, and sends personalized win-back emails via Gmail. The demo emphasizes reducing the time from days of manual export to seconds of automated execution."}