1 min lesson
Scaling Dagster for many technical users
Work through the cases in "Scaling Dagster for many technical users", pairing each signal with the move that fits.
Step 1 of 3
The JD's example project is scaling Dagster for a growing population of technical users. That is a multi-tenant platform problem and it is the design they will hand you.
As the data team grows, one shared bag of definitions stops working. Teams step on each other's deploys, one team's backfill starves another's daily run and onboarding a new engineer means pulling you into the loop. The fix is structure on four axes.
A code location per team or domain
Each deploys independently, owns its assets
A bad deploy is blast-radius-limited to one location
Run concurrency limits + a run queue
Concurrency pools per shared resource (cluster, API)
One team's backfill cannot starve another's SLA run
Scaffolding + conventions for new assets
Guardrails: required checks, naming, partition defaults
The team adds assets without you in the loop
Run health, asset freshness, lineage in one UI
Alerting on failed/overdue assets
A growing audience can self-diagnose