Analytics Models Studio (AMS)

Open-source, self-hosted GUI for scheduling and monitoring dbt Core pipelines.

AMS provides a complete web platform to schedule, monitor, and manage your dbt Core data transformation pipelines — no Airflow, Prefect, or cloud dependency needed.

Quick Start

pip install analytics-models-studio
ams init --project-dir /path/to/your/dbt/project
ams serve

Open http://localhost:8585 in your browser.

Features

• 📅 Job Scheduling — Cron-based scheduling with model selection, branch targeting, and timeout protection
• 🔄 Git Integration — Automatic fetch/checkout/pull before each execution with commit tracking
• 📊 Log Streaming — Real-time WebSocket log viewer with per-model result breakdown
• 📈 Dashboard — 14-day execution trends, success rates, and pipeline health metrics
• 🕸️ Lineage Explorer — Interactive DAG visualization from manifest.json with saved views
• 📖 Data Catalog — Column-level metadata, test coverage, and model performance analytics
• 🌡️ Source Freshness — Monitor data freshness with status indicators and thresholds
• 🔧 Onboarding Wizard — Guided setup with environment detection and adapter installation
• 🔐 Credential Encryption — Fernet-based at-rest encryption for service accounts and tokens
• 🖥️ Cross-Platform — Windows Server, Linux, macOS with conda/venv environment support
• 🧩 API-First — Full REST API with OpenAPI docs at /docs

CLI

ams serve     Start the web server
ams init      Initialize AMS configuration
ams version   Show installed version
ams info      Show environment information

Requirements

• Python ≥ 3.10
• dbt-core ≥ 1.7 (with your database adapter)
• Git ≥ 2.30

License

MIT