dbt-ci 1.3.3

CI/CD tool for dbt projects with intelligent change detection and selective execution

dbt-ci

A CI tool for dbt (data build tool) projects that intelligently runs only modified models based on state comparison, supporting multiple execution environments including local, Docker, and dbt runners.

How It Works

dbt-ci uses a cache-based workflow:

1. init - Downloads reference state from cloud storage (or uses local), compares with current code, and creates a cache of changes
2. run/delete/ephemeral - Use the cached state automatically (no need to re-specify state paths)

This design ensures:

• ✅ Consistent state across all commands in a CI run
• ✅ Better performance (no redundant state downloads)
• ✅ Simpler CLI (specify state once in init, reuse everywhere)

Installation

From PyPI (Recommended)

pip install dbt-ci

From GitHub

# Install from main branch
pip install git+https://github.com/datablock-dev/dbt-ci.git@main

# Install a specific version
pip install git+https://github.com/datablock-dev/dbt-ci.git@v1.0.0

Local Development

git clone https://github.com/datablock-dev/dbt-ci.git
cd dbt-ci
pip install -e ".[dev]"

After installation, the tool is available as dbt-ci.

Quick Start

The Workflow: Initialize once with init, then run commands that use the cached state.

1. Initialize State

First, initialize the dbt-ci state. This downloads/reads reference state and creates a cache:

dbt-ci init \
  --dbt-project-dir dbt \
  --profiles-dir dbt \
  --reference-target production \
  --state dbt/.dbtstate

With Cloud Storage (GCS/S3):

dbt-ci init \
  --dbt-project-dir dbt \
  --state-uri gs://my-bucket/dbt-state/manifest.json \
  --reference-target production \
  --state dbt/.dbtstate

2. Run Modified Models

After initialization, run commands use the cached state automatically:

# No need to specify --state again!
dbt-ci run \
  --dbt-project-dir dbt \
  --profiles-dir dbt

With Docker:

dbt-ci run \
  --runner docker \
  --docker-image ghcr.io/dbt-labs/dbt-bigquery:latest

Commands

All commands share a set of common options (listed in the Common Options section below). Command-specific flags are listed under each command.

---

init - Initialize State

Creates initial state from your dbt project. Always run this first. Downloads reference manifest from cloud storage (if specified) and creates a local cache for subsequent commands.

dbt-ci init \
  --dbt-project-dir dbt \
  --profiles-dir dbt \
  --state-uri gs://my-bucket/manifest.json \
  --reference-target production \
  --state dbt/.dbtstate

Flags:

Flag	Aliases	Env Var(s)	Default	Description
--reference-target	--ref-target	DBT_REFERENCE_TARGET	None	dbt target for the production/reference manifest
--reference-vars	--ref-vars	DBT_REFERENCE_VARS	None	Variables to pass to dbt when compiling the reference manifest (YAML string or file path)
--state-uri		DBT_STATE_URI, STATE_URI	None	Remote URI for the state manifest (e.g. gs://bucket/manifest.json, s3://bucket/manifest.json)
--target-compile		DBT_TARGET_COMPILE	false	Run the second compile pass against the actual target
--skip-reference-compile		DBT_SKIP_REFERENCE_COMPILE	false	Skip the compile pass against the reference/production state
--no-git		DBT_NO_GIT	false	Skip git-based file change comparison
--comparison-strategy	--comparison	DBT_COMPARISON_STRATEGY	hybrid	Strategy for detecting changed nodes: dbt, git, or hybrid

All common options also apply.

---

run - Run Modified Models

Detects and runs models that have changed. Uses cached state from init.

dbt-ci run --dbt-project-dir dbt --mode models

Flags:

Flag	Aliases	Env Var(s)	Default	Description
--mode	-m, --nodes, -n	DBT_NODES	all	What to run: all, models, seeds, snapshots, tests
--filters	-f		None	Extra resource-type filter (repeatable, choices: models, seeds, snapshots, tests). E.g. --mode tests -f snapshots to run only tests that have a snapshot dependency

All common options also apply.

Examples:

# Run only modified models
dbt-ci run --mode models

# Run modified models with defer to production
dbt-ci run --mode models --defer

# Run all modified resources (models, tests, seeds, etc.)
dbt-ci run --mode all

# With Docker
dbt-ci run --runner docker --mode models

---

ephemeral - Ephemeral Environment

Clones changed models and their downstream dependencies into an isolated target schema using dbt clone, allowing integration testing without affecting production. Uses cached state from init.

Important: --target and --vars must match the environment you want to clone into. The clone operation reads your profiles.yml to determine the target database/schema — if these are wrong, models will be cloned to the wrong location or the command will fail.

dbt-ci ephemeral \
  --target my-pr-env \
  --vars '{"use_production_data":"false"}'

How it works:

1. Reads the cached change set from init
2. Builds a selection of all affected models and their downstream dependencies
3. Runs dbt clone --select <nodes> targeting the specified environment
4. The cloned tables/views can then be used as the base for subsequent dbt run commands in the PR environment

Flags:

Flag	Aliases	Env Var(s)	Default	Description
--keep-env		DBT_KEEP_ENV	false	Don't destroy the ephemeral environment after the run (if supported by the runner)

All common options also apply.

---

delete - Delete Removed Models

Detects and deletes models that have been removed from the project. Uses cached state from init.

dbt-ci delete --dry-run  # preview what will be deleted
dbt-ci delete            # execute deletions

Flags:

Only common options apply — no command-specific flags.

---

finalize - Finalize State

Run after run, delete, or ephemeral to upload artifacts and clean up the local cache for the next CI run.

dbt-ci finalize
dbt-ci finalize --artifacts-uri s3://my-bucket/dbt-artifacts/

Flags:

Flag	Aliases	Env Var(s)	Default	Description
--artifacts-uri		DBT_ARTIFACTS_URI, ARTIFACTS_URI	None	Object storage URI for uploading run artifacts such as the updated manifest.json (e.g. s3://bucket/dbt-artifacts/)
--clean-ephemeral	--destroy-ephemeral	DBT_CLEAN_EPHEMERAL, DBT_DESTROY_EPHEMERAL	false	Clean up the ephemeral environment as part of finalization

All common options also apply.

Runners

dbt-ci supports multiple execution environments:

Local Runner

Execute dbt commands directly on your machine:

# After init
dbt-ci run \
  --runner local \
  --dbt-project-dir dbt

dbt Runner (Python API)

Uses dbt's Python API (fastest, default):

# After init - uses dbt Python API
dbt-ci run \
  --runner dbt \
  --dbt-project-dir dbt

Docker Runner

Run dbt commands inside a Docker container:

dbt-ci run \
  --runner docker \
  --docker-image ghcr.io/dbt-labs/dbt-duckdb:latest \
  --docker-volumes $(pwd):/workspace \
  --dbt-project-dir /workspace/dbt \
  --state /workspace/dbt/.dbtstate

For Apple Silicon Macs:

dbt-ci run \
  --runner docker \
  --docker-platform linux/amd64 \
  --docker-image ghcr.io/dbt-labs/dbt-postgres:latest \
  --docker-volumes $(pwd):/workspace \
  --dbt-project-dir /workspace/dbt

Docker Advanced Options

Platform (for Apple Silicon compatibility):

--docker-platform linux/amd64  # or linux/arm64

Custom Volumes:

--docker-volumes "/host/path:/container/path" --docker-volumes "/another:/path:ro"

Environment Variables:

--docker-env "DBT_ENV=prod" --docker-env "MY_API_KEY=secret"

Network Mode:

--docker-network bridge  # or host, none, container:name

User:

--docker-user "1000:1000"  # or leave empty for auto-detect

Additional Docker Args:

--docker-args "--memory=2g --cpus=2"

Complete Docker Example:

dbt-ci run \
  --runner docker \
  --docker-image ghcr.io/dbt-labs/dbt-postgres:1.7.0 \
  --docker-platform linux/amd64 \
  --docker-env "POSTGRES_HOST=host.docker.internal" \
  --docker-network host \
  --docker-volumes "$(pwd):/workspace" \
  --docker-volumes "$HOME/.aws:/root/.aws:ro" \
  --dbt-project-dir /workspace/dbt \
  --profiles-dir /workspace/dbt \
  --target prod

Common Options

These flags are available on every command.

Configuration File

dbt-ci supports a dbt-ci.config.yaml file as an alternative to passing every flag on the command line. It is loaded before any other options so that CLI flags and shell environment variables always take precedence.

Default location: dbt-ci.config.yaml in the current working directory (override with --config / DBT_CONFIG).

Keys in the file correspond to the environment variable names of each flag:

# dbt-ci.config.yaml
DBT_RUNNER: docker
DBT_DOCKER_IMAGE: docker.pkg.dev/my-project/dbt:latest
DBT_PROJECT_DIR: dbt
DBT_STATE: dbt/state
DBT_REFERENCE_TARGET: prod
DBT_DOCKER_VOLUMES: "$(pwd)/dbt:/dbt"
DBT_DOCKER_ENV: "DBT_PROFILES_DIR=/dbt,GOOGLE_APPLICATION_CREDENTIALS=${GOOGLE_APPLICATION_CREDENTIALS}"

Precedence (highest → lowest):

1. Shell environment variables
2. CLI flags
3. dbt-ci.config.yaml
4. Built-in defaults

${VAR_NAME} references inside the config file are resolved from the shell environment at load time.

Note: dbt-ci.config.yaml is ignored by git by default (it is listed in .gitignore). Use it for local developer overrides and commit a .example variant for your team.

Core

Flag	Aliases	Env Var(s)	Default	Description
--dbt-project-dir		DBT_PROJECT_DIR	.	Path to the dbt project directory
--profiles-dir		DBT_PROFILES_DIR	Auto-detect	Path to the directory containing profiles.yml
--reference-state	--state	DBT_STATE	None	Local path to the reference state directory (where manifest.json is stored)
--target	-t	DBT_TARGET	From profiles.yml	dbt target to use
--vars	-v	DBT_VARS	""	YAML string or path to a YAML file with dbt variables
--defer		DBT_DEFER	false	Pass dbt's --defer flag (defers unmodified nodes to the production state)
--runner	-r	DBT_RUNNER	dbt	Runner to use: dbt, local, docker, bash
--entrypoint		DBT_ENTRYPOINT	dbt	Command entrypoint for dbt
--dbt-version		DBT_VERSION	Current	Pin a specific dbt version (e.g. 1.10.13)
--adapter	-a	DBT_ADAPTER	None	dbt adapter to install (e.g. dbt-bigquery, dbt-duckdb=1.10.0)
--config	-c	DBT_CONFIG	dbt-ci.config.yaml	Path to a dbt-ci YAML configuration file
--dry-run		DBT_DRY_RUN	false	Print commands without executing them
--quiet	-q	DBT_QUIET	false	Run in quiet mode with minimal output
--log-level		DBT_LOG_LEVEL	INFO	Logging verbosity: DEBUG, INFO, WARNING, ERROR, CRITICAL
--slack-webhook	--slack-webhook-url	SLACK_WEBHOOK, SLACK_WEBHOOK_URL	None	Slack webhook URL for CI notifications

Docker Runner

Only used when --runner docker is set.

Flag	Env Var(s)	Default	Description
--docker-image	DBT_DOCKER_IMAGE	ghcr.io/dbt-labs/dbt-core:latest	Docker image to use
--docker-platform	DBT_DOCKER_PLATFORM	Auto-detect	Platform override, e.g. linux/amd64 or linux/arm64
--docker-volumes	DBT_DOCKER_VOLUMES	[]	Volume mounts (repeatable): host:container[:mode]
--docker-env	DBT_DOCKER_ENV	[]	Environment variables (repeatable): KEY=VALUE
--docker-network	DBT_DOCKER_NETWORK	host	Docker network mode
--docker-user	DBT_DOCKER_USER	Auto-detect	User to run as inside the container (UID:GID)
--docker-args	DBT_DOCKER_ARGS	""	Extra arguments appended to docker run

Bash Runner

Only used when --runner bash is set.

Flag	Aliases	Env Var(s)	Default	Description
--shell-path	--bash-path	DBT_SHELL_PATH	/bin/bash	Path to the shell executable

Cloud Storage Support

dbt-ci supports storing and retrieving state files from cloud storage (GCS, S3), making it ideal for distributed CI/CD workflows.

GCS/S3 State Storage

Store your dbt reference state in cloud storage for shared access across CI runs:

# Initialize and download state from GCS
dbt-ci init \
  --dbt-project-dir dbt \
  --state-uri gs://my-bucket/dbt-state/manifest.json \
  --reference-target production \
  --state dbt/.dbtstate

# Run using cached state (no need to specify URI again)
dbt-ci run --dbt-project-dir dbt --mode models

Benefits:

• 🔄 Shared State: Download the same reference state across different CI jobs
• 💾 Cache-Based: After init, commands use local cache (no repeated downloads)
• 📦 No Git Commits: State files don't need to be committed to version control
• 🚀 Scalable: Works seamlessly in containerized and distributed environments
• 🔐 Secure: Leverage cloud IAM and bucket policies for access control

Configuration:

The tool uses cloud credentials from your environment. Ensure your bucket is accessible:

# For GCS
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

# For AWS S3
export AWS_ACCESS_KEY_ID=your_key
export AWS_SECRET_ACCESS_KEY=your_secret
export AWS_DEFAULT_REGION=us-east-1

# Or use IAM roles (recommended in CI/CD)
dbt-ci init --state-uri gs://my-bucket/manifest.json

Supported URI Formats:

• gs://bucket-name/path/to/manifest.json (Google Cloud Storage)
• s3://bucket-name/path/to/manifest.json (AWS S3)

Environment Variables

All CLI options can also be set via environment variables:

export DBT_PROJECT_DIR=./dbt
export DBT_PROFILES_DIR=./dbt
export DBT_TARGET=production
export DBT_RUNNER=local

# After running init, just use:
dbt-ci run

Common Environment Variables:

• DBT_PROJECT_DIR - Path to dbt project
• DBT_PROFILES_DIR - Path to profiles.yml location
• DBT_TARGET - Target environment to use
• DBT_RUNNER - Runner type (local, docker, bash, dbt)

Note: State management is cache-based. Run init once, then subsequent commands automatically use the cached state.

CI/CD Integration

GitHub Actions Example

name: dbt CI

on: [pull_request]

jobs:
  dbt-ci:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      
      - name: Configure AWS Credentials
        uses: aws-actions/configure-aws-credentials@v2
        with:
          role-to-assume: arn:aws:iam::123456789012:role/GitHubActionsRole
          aws-region: us-east-1
      
      - name: Install dbt-ci
        run: pip install git+https://github.com/datablock-dev/dbt-ci.git@main
      
      - name: Initialize dbt-ci with cloud state
        run: |
          dbt-ci init \
            --dbt-project-dir dbt \
            --state-uri gs://my-dbt-state/prod/manifest.json \
            --reference-target production \
            --state dbt/.dbtstate
      
      - name: Run modified models
        run: |
          dbt-ci run --mode models

GitLab CI Example

dbt-ci:
  image: python:3.11
  script:
    - pip install git+https://github.com/datablock-dev/dbt-ci.git@main
    - dbt-ci init --dbt-project-dir dbt --state-uri gs://my-dbt-state/prod/manifest.json --reference-target production --state dbt/.dbtstate
    - dbt-ci run --mode models
  only:
    - merge_requests

Features

• 🎯 Smart Detection: Automatically identifies modified, new, and deleted models
• 📊 Dependency Tracking: Generates and traverses dependency graphs for lineage analysis
• 🔄 State Comparison: Compares current state against production for precise CI
• ☁️ Cloud Storage: S3 integration for shared state across distributed CI/CD workflows
• 🚀 Multiple Runners: Supports local, Docker, bash, and dbt Python API execution
• 🐳 Docker-First: Extensive Docker configuration for containerized workflows
• ⚡ Selective Execution: Run only what changed, saving time and resources
• 🔌 Adapter Support: Install specific dbt versions and adapters on-demand
• 💬 Notifications: Slack webhook integration for CI/CD alerts
• ♻️ Ephemeral Environments: Test changes in isolated environments
• 🧹 Cleanup: Automatically remove deleted models from target warehouse

Use Cases

Pull Request CI

Only build and test models affected by PR changes:

# Initialize with reference state
dbt-ci init --state-uri gs://bucket/manifest.json --reference-target production --state dbt/.dbtstate

# Run modified models with defer
dbt-ci run --mode models --defer

Distributed CI with Cloud Storage

Share state across multiple CI jobs:

# Job 1: Initialize state (downloads from cloud)
dbt-ci init --state-uri gs://my-bucket/manifest.json --reference-target production --state dbt/.dbtstate

# Job 2: Run models (uses cached state)
dbt-ci run --mode models

# Job 3: Run tests (uses cached state)
dbt-ci run --mode tests

Selective Testing

Run tests only for modified models:

# After init
dbt-ci run --mode tests

Schema Migrations

Clean up deleted models from production:

# After init
dbt-ci delete --target production

Multi-Environment Testing

Create ephemeral test environments:

dbt-ci ephemeral --keep-env

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

1. Clone the repository
2. Install dependencies: pip install -e ".[dev]"
3. Run tests: pytest tests/
4. Run linting: black src/ tests/

Commit Message Format

This project uses Conventional Commits for automated releases:

• feat: New feature (minor version bump)
• fix: Bug fix (patch version bump)
• docs: Documentation changes
• refactor: Code refactoring
• test: Adding tests
• chore: Maintenance tasks

Example:

git commit -m "feat: add Docker runner support"
git commit -m "fix: resolve path resolution on Windows"

See RELEASING.md for details on the automated release process.

License

See LICENSE file for details.

Links

• PyPI: https://pypi.org/project/dbt-ci/
• Documentation: https://datablock.dev
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Changelog: CHANGELOG.md