# Dbt Projects On Snowflake > Deploy, manage, and monitor dbt projects natively within Snowflake using web-based workspaces, Use when this capability is needed. - Skill: `tomevault-io/dbt-projects-on-snowflake` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/dbt-projects-on-snowflake` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/dbt-projects-on-snowflake/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/dbt-projects-on-snowflake --- # dbt Projects on Snowflake Deploy, manage, and monitor dbt projects natively within Snowflake using web-based workspaces, schema-level DBT PROJECT objects, and comprehensive event table telemetry. ## Quick Start **Three Ways to Use dbt Projects:** 1. **Snowsight Workspaces** - Web-based IDE for interactive development 2. **DBT PROJECT Objects** - Deployed projects for production execution 3. **Snowflake CLI** - Command-line deployment and execution ## Setup Complete setup instructions including prerequisites, external access integration, Git API integration, and event table configuration are in `references/SETUP.md`. ## Deployment Methods ### Method 1: Snowflake CLI (Recommended) ```bash # Deploy project snow dbt deploy my_project --source . # Execute commands snow dbt execute my_project run snow dbt execute my_project build ``` ### Method 2: Snowsight - Navigate to Projects → My Workspace - Create new project from Git repository - Configure profiles.yml - Deploy as DBT PROJECT object ### Method 3: SQL Execution Execute directly in SQL: ```sql EXECUTE DBT PROJECT .. args='build'; EXECUTE DBT PROJECT .. args='build --full-refresh'; EXECUTE DBT PROJECT .. args='build --select tag:gold'; ``` ## Scheduling & Automation For automated scheduling with Snowflake Tasks, see the "Optional: Schedule Automated Runs" section in `references/SETUP.md`. ## Event Table Monitoring ### Event Table Configuration Configure event tables following the Event Table Monitoring Configuration section in `references/SETUP.md`. This enables OpenTelemetry-based monitoring of dbt project executions. ### Monitoring Queries All monitoring scripts use parameterized event table references. Specify your event table location when running: ```bash # Example: Query recent executions snow sql -f scripts/recent_executions.sql --enable-templating JINJA \ -D event_table=MY_DATABASE.MY_SCHEMA.EVENT_LOG # Example: Check for errors snow sql -f scripts/execution_errors.sql --enable-templating JINJA \ -D event_table=LOGS_DB.PUBLIC.DBT_EVENTS # Example: Performance metrics snow sql -f scripts/performance_metrics.sql --enable-templating JINJA \ -D event_table=MY_DATABASE.MY_SCHEMA.EVENT_LOG ``` **Core Monitoring:** - **`recent_executions.sql`** - Lists recent dbt project executions with severity - **`execution_errors.sql`** - Query ERROR logs to identify failures - **`performance_metrics.sql`** - Query CPU and memory usage metrics - **`trace_spans.sql`** - Query execution spans for timing analysis - **`execution_summary.sql`** - Summarize executions by project with error counts **Advanced Use Cases:** - **`alert_failures.sql`** - Alert trigger for execution failures (returns error count) - **`performance_regression.sql`** - Week-over-week performance comparison - **`resource_usage.sql`** - CPU and memory consumption by project - **`audit_trail.sql`** - Complete execution audit trail for compliance ### Event Table Structure Event tables follow the [OpenTelemetry data model](https://opentelemetry.io/) with these key columns: | Column | Description | | ----------------------- | --------------------------------------------------------------------------- | | **TIMESTAMP** | UTC timestamp when event was created (end of time span for span events) | | **START_TIMESTAMP** | For span events, the start of the time span | | **TRACE** | Tracing context with `trace_id` and `span_id` | | **RESOURCE_ATTRIBUTES** | Source identification: database, schema, user, warehouse, etc. | | **SCOPE** | Event scopes (e.g., class names for logs) | | **RECORD_TYPE** | Event type: `LOG`, `SPAN`, `SPAN_EVENT`, `EVENT`, `METRIC` | | **RECORD** | JSON object with record-specific data (severity, metric type, span details) | | **RECORD_ATTRIBUTES** | Event metadata set by Snowflake or code | | **VALUE** | Actual log message, metric value, or null for spans | ### Best Practices **Performance Optimization:** - **Always filter by TIMESTAMP** to limit scanned data (reduces cost) - **Use RESOURCE_ATTRIBUTES** for efficient filtering by project/database/schema - **Archive old event table data** (>90 days) to separate tables **Monitoring Strategy:** 1. Set event tables at **DATABASE level**, not account or schema 2. Configure appropriate log/trace/metric levels per schema 3. Always filter by `TIMESTAMP` to avoid scanning large event tables 4. Use `snow.executable.type = 'DBT_PROJECT'` to isolate dbt events 5. Leverage `RESOURCE_ATTRIBUTES` for filtering by project/database/schema 6. Monitor ERROR severity logs for immediate alerts 7. Use SPAN records to analyze execution timing and bottlenecks **Alerting Priorities:** - **High**: Any ERROR in execution, execution >2x historical avg, warehouse credit anomalies - **Medium**: WARNING logs, test/model failures on critical models, performance trending down - **Low**: INFO logs, scheduled job confirmations, performance metrics for analysis ### Event Table Troubleshooting | Issue | Solution | | ----------------------- | -------------------------------------------------------------- | | No events captured | Verify event table set at DATABASE level with `ALTER DATABASE` | | Too many events | Adjust `LOG_LEVEL`/`TRACE_LEVEL`/`METRIC_LEVEL` per schema | | Slow monitoring queries | Always filter by TIMESTAMP first; consider archiving old data | | Missing metrics | Set `METRIC_LEVEL = 'ALL'` for schema | | Missing traces | Set `TRACE_LEVEL = 'ALWAYS'` for schema | | Cannot see project name | Verify `snow.executable.type = 'DBT_PROJECT'` filter | ## Supported dbt Commands | Command | Workspaces | EXECUTE DBT PROJECT | snow dbt execute | | -------- | ------------------- | ------------------- | ---------------- | | build | ✅ | ✅ | ✅ | | run | ✅ | ✅ | ✅ | | test | ✅ | ✅ | ✅ | | compile | ✅ | ✅ | ✅ | | seed | ✅ | ✅ | ✅ | | snapshot | ✅ | ✅ | ✅ | | deps | ✅ (workspace only) | ❌ | ❌ | ## Team Collaboration **Flexibility:** Team members can use different development approaches simultaneously: - Developer A: dbt Projects on Snowflake workspaces - Developer B: dbt Cloud - Developer C: Local VS Code with dbt CLI - All check into the same Git repository ## Key Commands | Command | Purpose | | ------------------------------- | --------------------------- | | `snow dbt deploy ` | Deploy project to Snowflake | | `snow dbt execute run` | Run dbt models | | `snow dbt execute build` | Run and test models | | `snow dbt execute test` | Run tests only | | `snow dbt list` | List all dbt projects | ## Troubleshooting For setup and deployment issues, see `references/SETUP.md`. For monitoring issues, see the Troubleshooting table in the Event Table Monitoring section above. ## Related Skills **Complementary Observability:** - `dbt-artifacts` skill - For cross-platform execution logging and historical trend analysis **When to use both together:** - **dbt Projects on Snowflake** for real-time monitoring with OpenTelemetry event tables - **dbt Artifacts** for cross-platform historical analysis and long-term metrics **When to use one vs the other:** - Use **dbt Projects on Snowflake** alone if you exclusively run dbt within Snowflake - Use **dbt Artifacts** alone if you run dbt outside Snowflake (dbt Cloud, Airflow, local) - Use **both** for comprehensive enterprise monitoring (real-time + historical) --- ## Resources ### Local Files - **Monitoring Scripts**: `scripts/` - Ready-to-use parameterized SQL scripts for monitoring - Core Monitoring: `recent_executions.sql`, `execution_errors.sql`, `performance_metrics.sql`, `trace_spans.sql`, `execution_summary.sql` - Advanced Monitoring: `alert_failures.sql`, `performance_regression.sql`, `resource_usage.sql`, `audit_trail.sql` - **Setup Guide**: `references/SETUP.md` - Complete step-by-step setup including event table configuration and task scheduling ### Official Documentation - dbt Projects Documentation: - Monitoring Guide: --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/sfc-gh-dflippo) — claim your Tome and manage your conversions.