dbt Cloud is the managed platform for running dbt jobs in production — it handles scheduling, execution infrastructure, run history, and the hosted Docs site without requiring the data team to provision servers or manage dbt runtime environments. For most data engineering teams, dbt Cloud eliminates the operational complexity of running dbt in production: no cron jobs to maintain, no dbt version management on shared servers, and no separate documentation hosting infrastructure. The free tier supports one developer seat and one scheduled job, which is sufficient for many small teams.
Integrating dbt into a CI/CD pipeline ensures that every pull request that modifies a dbt model is automatically tested before merging — preventing broken SQL, failing tests, and missing documentation from reaching production. The standard CI pattern runs `dbt build --select state:modified+` (which builds modified models and all their downstream dependents) against a dedicated CI schema, then deletes the schema after the checks pass. This targeted build avoids rebuilding the entire project on every PR while still testing all affected models and their downstream dependencies.
Analogy🏏Cricket
🏏 Think of it like cricket: Migrating from Airflow to Prefect is like the same bowling coach shifting from traditional Test cricket notation to a modern T20 analytics dashboard — the underlying ball-by-ball data (the business logic) is exactly the same. What changes is how the data is recorded, displayed, and acted upon. The yorker that Bumrah bowls in over 20 is identical whether it is recorded in the old scorebook (Airflow DAG file) or the new analytics platform (Prefect flow). The migration is a transcription exercise, not a strategy change — and a wise coach verifies that the runs, wickets, and economies match exactly between the old and new system before decommissioning the scorebook. That verification step is the whole heart of the migration: because the yorker is unchanged, the only honest test is to run the same over through both systems and confirm the recorded runs, wickets and economies match to the last digit before the old scorebook is thrown away. Rushing to burn the scorebook the moment the shiny dashboard lights up is how teams lose a season of records to a silent transcription slip. The coach keeps both systems running in parallel for a while, reconciles their outputs ball by ball, and only when every figure agrees does he trust the new dashboard alone — a transcription is only complete when you have proven nothing was lost in the copying.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.