Introduction

As organizations scale their data operations, implementing DBT (Data Build Tool) becomes increasingly crucial for maintaining clean, tested, and documented data transformations. However, this journey often comes with its share of learning experiences. In this post, I'll share five critical lessons learned from implementing DBT with BigQuery, along with practical solutions and code examples.

1. Overlooking the Importance of Data Testing

Initially, I underestimated the significance of data tests in DBT, leading to data quality issues propagating through our pipelines. Here's how we transformed our testing approach:

Before: Minimal Testing

-- models/orders.sql
SELECT
    order_id,
    user_id,
    amount,
    created_at
FROM {{ ref('raw_orders') }}

After: Comprehensive Testing Strategy

# models/schema.yml
version: 2

models:
  - name: orders
    description: "Cleaned orders table"
    columns:
      - name: order_id
        description: "Primary key"
        tests:
          - unique
          - not_null
      - name: amount
        description: "Order amount in USD"
        tests:
          - not_null
          - positive_values
          - expression: "amount < 10000"
            severity: warn
            config:
              where: "created_at >= current_date - 7"

    tests:
      - dbt_utils.equal_rowcount:
          compare_model: ref('raw_orders')

We also implemented custom tests for business-specific rules:

-- tests/assert_total_amount_positive.sql
SELECT
    date_trunc(created_at, month) as month,
    sum(amount) as total_amount
FROM {{ ref('orders') }}
GROUP BY 1
HAVING total_amount <= 0

2. Mismanagement of Incremental Models

Inefficient incremental model configuration led to performance issues and increased costs. Here's how we optimized our approach:

Before: Inefficient Incremental Logic

-- models/daily_metrics.sql
{{ config(
    materialized='incremental'
) }}

SELECT
    date,
    user_id,
    SUM(amount) as daily_spend
FROM {{ ref('orders') }}
GROUP BY 1, 2

After: Optimized Incremental Strategy

-- models/daily_metrics.sql
{{ config(
    materialized='incremental',
    unique_key=['date', 'user_id'],
    incremental_strategy='merge',
    partition_by={
        'field': 'date',
        'data_type': 'date',
        'granularity': 'day'
    }
) }}

SELECT
    date,
    user_id,
    SUM(amount) as daily_spend
FROM {{ ref('orders') }}
{% if is_incremental() %}
    WHERE date >= (SELECT MAX(date) FROM {{ this }})
{% endif %}
GROUP BY 1, 2

3. Underestimating the Complexity of Macros

Complex macros became maintenance nightmares. Here's how we simplified them:

Before: Overcomplicated Macro

{% macro complex_date_logic(column_name) %}
    CASE
        WHEN DATE_TRUNC({{ column_name }}, MONTH) = DATE_TRUNC(CURRENT_DATE(), MONTH)
        THEN 'current_month'
        WHEN DATE_TRUNC({{ column_name }}, MONTH) = DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH), MONTH)
        THEN 'previous_month'
        WHEN DATE_TRUNC({{ column_name }}, MONTH) BETWEEN DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 12 MONTH), MONTH)
            AND DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 2 MONTH), MONTH)
        THEN 'last_12_months'
        ELSE 'older'
    END
{% endmacro %}

After: Simplified, Reusable Macro

{% macro get_date_period(date_column) %}
    DATE_TRUNC({{ date_column }}, MONTH)
{% endmacro %}

-- Usage in models
SELECT
    user_id,
    {{ get_date_period('created_at') }} as period,
    SUM(amount) as total_amount
FROM {{ ref('orders') }}
GROUP BY 1, 2

4. Neglecting Documentation

Poor documentation hindered team collaboration. Here's our improved documentation approach:

# models/marts/core/schema.yml
version: 2

models:
  - name: customer_orders
    description: >
      Core customer orders model combining order history with customer details.
      Used by the marketing team for customer segmentation and analysis.
    columns:
      - name: customer_id
        description: Unique identifier for each customer
        tests:
          - unique
          - not_null
      - name: lifetime_value
        description: >
          Total monetary value of all customer orders.
          Calculated as sum of order amounts minus returns.
        tests:
          - not_null
          - positive_values

    meta:
      owner: 'data_team'
      updated_at: '2024-01-10'
      depends_on:
        - ref('stg_orders')
        - ref('stg_customers')

5. Inadequate Version Control Practices

We improved our version control workflow with:

Structured Branch Strategy

# Feature development
git checkout -b feature/add-customer-metrics

# Make changes
dbt run --models customer_metrics
dbt test --models customer_metrics

# Commit changes
git add models/customer_metrics.sql
git commit -m "feat: add customer lifetime value calculations

- Added new customer metrics model
- Implemented rolling 12-month calculations
- Added relevant tests and documentation"

# Create pull request for review

CI/CD Integration

# .github/workflows/dbt-ci.yml
name: DBT CI

on:
  pull_request:
    branches: [ main ]

jobs:
  dbt-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
      - name: Install dependencies
        run: |
          pip install dbt-bigquery
      - name: Run DBT tests
        run: |
          dbt deps
          dbt compile
          dbt test

Best Practices Moving Forward

1. Testing Strategy
   • Implement both generic and custom tests
   • Use severity levels appropriately
   • Regular test coverage reviews
2. Performance Optimization
   • Utilize BigQuery-specific optimizations
   • Monitor query costs
   • Regular performance testing
3. Code Organization
   • Clear folder structure
   • Consistent naming conventions
   • Regular refactoring sessions

Conclusion

Implementing DBT with BigQuery has transformed our data operations, but success required learning from these mistakes. By following these practices, you can avoid common pitfalls and build more robust data transformations.

Remember:

• Start with comprehensive testing
• Plan incremental models carefully
• Keep macros simple and documented
• Prioritize documentation
• Maintain strict version control

These lessons have helped us build more reliable, maintainable, and efficient data pipelines. What challenges have you faced with DBT implementation? Share your experiences in the comments below or via email.

This blog post is based on real-world experience implementing DBT with BigQuery. For more information, visit dbt documentation and BigQuery documentation.