{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/4ffc8f621f4646f9864ed703cad64500\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/4ffc8f621f4646f9864ed703cad64500-2a913bbfae113596.gif","duration":2311.328,"title":"Skipper IDE for Ingest, Model, Lineage","description":"This Loom demonstrates a VS Code based IDE that wraps the Skipper CLI to ingest, model, and manage data lake workflows with observability. The presenter shows running a sample pipeline that syncs fictitious MSN bike hire IoT data from S3 into Snowflake, including schema discovery, run status with rows and bytes ingested, and a run log for events and dead letters. They then model the synced data into cleanse and gold tier dbt artifacts, with steps for cleanse planning, validation, and an internal review that generates staging models and SQL dimensions and facts. The IDE also visualizes observability, lineage, and tests, and ends by showing a simpler business user view for asking questions, charting, and adding metadata comments and annotations."}