Keep every AI decision supplied with fresh, trusted data using Dagster

Dagster should own the definitions and dependency graph for durable data assets, coordinate their materialization, and expose lineage, run events, check results, and partition state. It should not become the source of truth for customer, policy, financial, or case records, nor should a successful Dagster run authorize a consequential business action. MetaCTO uses Dagster to produce an accepted, versioned context asset; the downstream workflow still applies scoped permissions, policy rules, human approvals, and an auditable write-back to the accountable system.

Not by default. Dagster's documentation states that a failed parent asset check normally allows the run and downstream materializations to continue; a check must be configured as blocking when failure should prevent downstream materialization. MetaCTO therefore classifies checks by consequence, makes release-critical checks blocking, and keeps the accepted-context pointer unchanged until those checks pass. We also avoid depending on preview-stage behavior for a production control without a tested fallback and an explicit owner.

Use a schedule for a known business cadence, a sensor when Dagster must poll internal or external events, and Declarative Automation when asset and dependency state should determine what runs. Dagster notes that sensor intervals are minimums rather than exact execution times, while run keys prevent duplicate runs for the same sensor request; its built-in automation conditions include cron, eager, and missing-asset patterns. MetaCTO chooses the simplest trigger that meets the freshness window, adds stable deduplication and concurrency bounds, and uses a streaming or event platform when durable high-volume transport is the real requirement.

Partition the data product around a business-safe recovery boundary, select only the affected partitions, and rematerialize with reviewed code and pinned inputs. A Dagster backfill coordinates work; it does not by itself make external writes idempotent or prove that corrected context is fit for production. MetaCTO writes repaired output to an isolated version or staging destination, reruns reconciliation and AI evaluations, requires approval for broad historical changes, and promotes the corrected asset only after preserving the evidence and rollback path.

Choose based on the operating model, not on asset syntax. Dagster describes Dagster+ as a managed control plane built on the open source engine, with Serverless and Hybrid deployment options plus managed services and capabilities such as alerts, authentication, role-based access control, audit logs, an asset catalog, and branch deployments. MetaCTO evaluates data residency, execution isolation, identity integration, audit retention, on-call ownership, support, and total operational burden; an open source deployment is appropriate only when the team is prepared to operate and secure those control-plane responsibilities itself.