dbt + BI-as-Code Integration Guide for Scalable BI

The combination of dbt™ for data transformation and BI-as-Code for visualization creates a powerful, scalable analytics stack. According to the dbt Labs State of Analytics Engineering 2023, 67% of organizations now use dbt™ for data transformation. When these tools work together seamlessly, organizations achieve true analytics engineering—where data pipelines and dashboards evolve together as one cohesive system.

How dbt and BI-as-Code Complement Each Other

dbt™ handles data transformation within your warehouse, creating clean, tested, documented data models. Learn more at docs.getdbt.com. BI-as-Code takes these models and creates visualizations through code-based configurations. Together, they form a complete analytics pipeline from raw data to polished dashboards.

The synergy is powerful:

• dbt™ ensures data quality and consistency
• BI-as-Code ensures visualization consistency and version control
• Both use Git for collaboration (see git-scm.com)
• Both support CI/CD workflows (see our CI/CD analytics implementation guide)
• Both enable testing and documentation

A McKinsey study reveals that data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain them, and 19 times as likely to be profitable - this integrated approach enables that comprehensive data-driven success.

Integration Strategies

Shared Repository Structure:

analytics-platform/
├── dbt/
│   ├── models/
│   │   ├── staging/
│   │   └── marts/
│   ├── tests/
│   └── macros/
├── dashboards/
│   ├── executive/
│   ├── operational/
│   └── components/
├── .github/
│   └── workflows/
│       └── integrated-pipeline.yml
└── README.md

Synchronized Deployment:

# integrated-pipeline.yml
name: Analytics Platform Deploy

on:
  push:
    branches: [main]

jobs:
  transform:
    name: Run dbt transformations
    steps:
      - name: dbt run
        run: |
          dbt deps
          dbt run
          dbt test

      - name: Generate manifest
        run: |
          dbt docs generate
          cp target/manifest.json ../dashboards/

  visualize:
    name: Update dashboards
    needs: transform
    steps:
      - name: Sync dbt metadata
        run: |
          visivo sync --dbt-manifest manifest.json

      - name: Deploy dashboards
        run: |
          visivo deploy --production

      - name: Validate integration
        run: |
          visivo test --check-dbt-refs

Automated Dependency Management:

# sync-dependencies.py
import json
import yaml

def sync_dbt_to_bi():
    """Ensure BI dashboards reference valid dbt models"""

    # Load dbt manifest
    with open('dbt/target/manifest.json') as f:
        dbt_models = json.load(f)['nodes']

    # Load dashboard configs
    dashboards = yaml.load_all('dashboards/*.yml')

    for dashboard in dashboards:
        for chart in dashboard['charts']:
            source = chart['model']

            # Validate source exists in dbt
            if source not in dbt_models:
                raise ValueError(f"Dashboard references non-existent model: {source}")

            # Update schema from dbt
            chart['columns'] = dbt_models[source]['columns']

Example Workflow

Complete Integration Example:

name: example_project

# BI dashboard referencing dbt model
# dashboards/executive.yml
name: Executive Dashboard

sources:
  - name: warehouse
    type: snowflake
    account: "{{ env_var('SNOWFLAKE_ACCOUNT') }}"
    database: ANALYTICS
    warehouse: COMPUTE_WH
    db_schema: PUBLIC

charts:
  - name: revenue_trend
    source: revenue_data
    type: line
    x: month
    y: total_revenue

    # Metadata from dbt
    description: "{{ dbt.get_model_description('revenue_summary') }}"
    freshness: "{{ dbt.get_model_freshness('revenue_summary') }}"

Testing Integration:

# Integrated tests
tests:
  data_quality:
    - dbt_test: unique_customer_id
    - dbt_test: revenue_positive

  dashboard_accuracy:
    - name: totals_match
      assertion: |
        dashboard.total_revenue ==
        dbt.run_query("SELECT SUM(total_revenue) FROM revenue_summary")

  integration:
    - name: all_dashboard_sources_exist
      check: every_dashboard_source_in_dbt_catalog

    - name: column_references_valid
      check: all_column_refs_exist_in_dbt_schema

Achieving Scalability

The integrated approach scales elegantly:

Modular Growth: Add new data sources to dbt, automatically available in BI.

Team Scalability: Data engineers work on dbt, analysts on dashboards, with clear interfaces.

Performance Scalability: dbt handles heavy transformations, BI tools just visualize.

Maintenance Scalability: Changes in one place propagate automatically.

Organizations with integrated dbt + BI-as-Code report:

• Eliminated metric mismatches
• Faster end-to-end development
• Consistent definitions
• Improved scalability

The integration of dbt and BI-as-Code isn't just a technical improvement—it's a fundamental shift toward treating analytics as a first-class engineering discipline. This combination provides the foundation for analytics that scales with your organization's ambitions.