fast-bi-dbt-runner 2026.1.0.5

A comprehensive Python library for managing DBT (Data Build Tool) DAGs within the Fast.BI data development platform

Fast.BI DBT Runner

A comprehensive Python library for managing DBT (Data Build Tool) DAGs within the Fast.BI data development platform. This package provides multiple execution operators optimized for different cost-performance trade-offs, from low-cost slow execution to high-cost fast execution.

🚀 Overview

Fast.BI DBT Runner is part of the Fast.BI Data Development Platform, designed to provide flexible and scalable DBT workload execution across various infrastructure options. The package offers four distinct operator types, each optimized for specific use cases and requirements.

🎯 Key Features

• Multiple Execution Operators: Choose from K8S, Bash, API, or GKE operators
• Cost-Performance Optimization: Scale from low-cost to high-performance execution
• Airflow Integration: Seamless integration with Apache Airflow workflows
• Manifest Parsing: Intelligent DBT manifest parsing for dynamic DAG generation
• Airbyte Integration: Built-in support for Airbyte task group building
• Flexible Configuration: Extensive configuration options for various deployment scenarios

📦 Installation

Basic Installation (Core Package)

pip install fast-bi-dbt-runner

With Airflow Integration

pip install fast-bi-dbt-runner[airflow]

With Development Tools

pip install fast-bi-dbt-runner[dev]

With Documentation Tools

pip install fast-bi-dbt-runner[docs]

Complete Installation

pip install fast-bi-dbt-runner[airflow,dev,docs]

🏗️ Architecture

Operator Types

The package provides four different operators for running DBT transformation pipelines:

1. K8S (Kubernetes) Operator - Default Choice

• Best for: Cost optimization, daily/nightly jobs, high concurrency
• Characteristics: Creates dedicated Kubernetes pods per task
• Trade-offs: Most cost-effective but slower execution speed
• Use cases: Daily ETL pipelines, projects with less frequent runs

2. Bash Operator

• Best for: Balanced cost-speed ratio, medium-sized projects
• Characteristics: Runs within Airflow worker resources
• Trade-offs: Faster than K8S but limited by worker capacity
• Use cases: Medium-sized projects, workflows requiring faster execution

3. API Operator

• Best for: High performance, time-sensitive workflows
• Characteristics: Dedicated machine per project, always-on resources
• Trade-offs: Fastest execution but highest cost
• Use cases: Large-scale projects, real-time analytics, high-frequency execution

4. GKE Operator

• Best for: Complete isolation, external client workloads
• Characteristics: Creates dedicated GKE clusters
• Trade-offs: Full isolation but higher operational complexity
• Use cases: External client workloads, isolated environment requirements

🚀 Quick Start

Basic Usage

from fast_bi_dbt_runner import DbtManifestParserK8sOperator

# Create a K8S operator instance
operator = DbtManifestParserK8SOperator(
    task_id='run_dbt_models',
    project_id='my-gcp-project',
    dbt_project_name='my_analytics',
    operator='k8s'
)

# Execute DBT models
operator.execute(context)

Configuration Example

# K8S Operator Configuration
k8s_config = {
    'PLATFORM': 'Airflow',
    'OPERATOR': 'k8s',
    'PROJECT_ID': 'my-gcp-project',
    'DBT_PROJECT_NAME': 'my_analytics',
    'DAG_SCHEDULE_INTERVAL': '@daily',
    'DATA_QUALITY': 'True',
    'DBT_SOURCE': 'True'
}

# API Operator Configuration
api_config = {
    'PLATFORM': 'Airflow',
    'OPERATOR': 'api',
    'PROJECT_ID': 'my-gcp-project',
    'DBT_PROJECT_NAME': 'realtime_analytics',
    'DAG_SCHEDULE_INTERVAL': '*/15 * * * *',
    'MODEL_DEBUG_LOG': 'True'
}

📚 Documentation

For detailed documentation, visit our Fast.BI Platform Documentation.

Key Documentation Sections

• Operator Selection Guide
• Configuration Variables
• Advanced Configuration Examples
• Best Practices

🔧 Configuration

Core Variables

Variable	Description	Default Value
PLATFORM	Data orchestration platform	Airflow
OPERATOR	Execution operator type	k8s
PROJECT_ID	Google Cloud project identifier	Required
DBT_PROJECT_NAME	DBT project identifier	Required
DAG_SCHEDULE_INTERVAL	Pipeline execution schedule	@once

Feature Flags

Variable	Description	Default
DBT_SEED	Enable seed data loading	False
DBT_SOURCE	Enable source loading	False
DBT_SNAPSHOT	Enable snapshot creation	False
DATA_QUALITY	Enable quality service	False
DEBUG	Enable connection verification	False

🎯 Use Cases

Daily ETL Pipeline

# Low-cost, reliable daily processing
config = {
    'OPERATOR': 'k8s',
    'DAG_SCHEDULE_INTERVAL': '@daily',
    'DBT_SOURCE': 'True',
    'DATA_QUALITY': 'True'
}

Real-time Analytics

# High-performance, frequent execution
config = {
    'OPERATOR': 'api',
    'DAG_SCHEDULE_INTERVAL': '*/15 * * * *',
    'MODEL_DEBUG_LOG': 'True'
}

External Client Workload

# Isolated, dedicated resources
config = {
    'OPERATOR': 'gke',
    'CLUSTER_NAME': 'client-isolated-cluster',
    'DATA_QUALITY': 'True'
}

🔍 Monitoring and Debugging

Enable Debug Logging

config = {
    'DEBUG': 'True',
    'MODEL_DEBUG_LOG': 'True'
}

Data Quality Integration

config = {
    'DATA_QUALITY': 'True',
    'DATAHUB_ENABLED': 'True'
}

🚀 CI/CD and Automation

This package uses GitHub Actions for continuous integration and deployment:

• Automated Testing: Tests across Python 3.9-3.12
• Code Quality: Linting, formatting, and type checking
• Automated Publishing: Automatic PyPI releases on version tags
• Documentation: Automated documentation building and deployment

Release Process

1. Create a version tag: git tag v1.0.0
2. Push the tag: git push origin v1.0.0
3. GitHub Actions automatically:
   • Tests the package
   • Builds and validates
   • Publishes to PyPI
   • Creates a GitHub release

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

# Clone the repository
git clone https://github.com/fast-bi/dbt-workflow-core-runner.git
cd dbt-workflow-core-runner

# Install in development mode with all tools
pip install -e .[dev,airflow]

# Run tests
pytest

# Check code quality
flake8 fast_bi_dbt_runner/
black --check fast_bi_dbt_runner/
mypy fast_bi_dbt_runner/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• Documentation: Fast.BI Platform Wiki
• Email: [e-mail]
• Issues: GitHub Issues
• Source: GitHub Repository

🔗 Related Projects

• Fast.BI Platform - Complete data development platform
• Fast.BI Replication Control - Data replication management
• Apache Airflow - Workflow orchestration platform

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Fast.BI DBT Runner - Empowering data teams with flexible, scalable DBT execution across the Fast.BI platform.