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Features Overview

dagctl is an AI-powered pipeline operations platform for data engineering teams running dbt-core and SQLMesh. It provides autonomous remediation, pipeline triage, and operational intelligence alongside intelligent execution, comprehensive observability, and seamless collaboration.

Core Features

Job Execution

dagctl executes jobs on Kubernetes with framework-specific optimizations:

  • SQLMesh — Smart model claiming prevents duplicate work across overlapping jobs, with parallel model execution respecting dependencies.
  • dbt — Pre-built container images with runtime secret injection. Jobs run from the image built at deploy time.
  • Both — Kubernetes-native scheduling, automatic retries, and real-time log streaming.

Observability

Monitor and track all aspects of your data pipelines:

  • Job execution history — View detailed run logs, status, and performance metrics
  • Model-level tracking — See which models succeeded, failed, or were blocked
  • Real-time monitoring — Track job progress as it happens
  • Alerting — Get notified via Slack when jobs fail

Model Catalog

Browse all models and sources across every project in your organization:

  • Cross-project search — Find models by name across all projects
  • Column metadata — View column definitions and types
  • Execution history — Track model performance over time
  • Favorites — Pin important models for quick access

Lineage

Visualize data flow through your pipelines:

  • Interactive DAG — Explore model dependencies with zoom, pan, and click-to-highlight
  • Upstream/downstream tracing — Understand the impact of changes
  • Both frameworks — Works with SQLMesh and dbt projects

Environment Variables & Secrets

Manage credentials without storing them in your repository:

  • Organization and project-level variables — Shared or scoped to individual projects
  • File-mounted secrets — Mount credentials as files for tools requiring file-based auth (Snowflake private keys, GCP service accounts)
  • Runtime injection — Secrets are injected at pod runtime via Kyverno, never baked into images

Environment Protection

Control who can deploy to production:

  • Protected environments — Require explicit approval before plans execute
  • Role-based access — Owner and Developer roles can approve; Reader cannot

Organizations & RBAC

Manage your team and access controls:

  • SSO — SAML and Okta Workforce support
  • Role-based access — Owner, Developer, and Reader roles
  • Team management — Invite members, assign roles, manage access