Study Notes 4.3.1 - Build the First dbt Models

1. Introduction to dbt Models

• dbt (Data Build Tool) sits on top of data platforms like BigQuery or Postgres.
• Objective: Transform raw data (e.g., green and yellow taxi trip data) into structured, usable formats for BI tools.
• Approach: Modular data modeling using fact tables and dimensional tables.

• Process:
  • Start with raw data (sources).
  • Build SQL scripts (models) to clean, deduplicate, recast, and rename data.
  • Apply business logic to create fact and dimensional tables.
  • Aggregate data for stakeholders.

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2. Anatomy of a dbt Model

• Models are SQL files that contain SELECT statements.
• Materialization: Determines how the model is stored in the database.
  • Types of Materialization:
    1. Ephemeral: Not physically stored; exists only within other models (like a CTE).
    2. View: Materialized as a view in the database.
    3. Table: Materialized as a physical table.
    4. Incremental: Updates only new data in the table (avoids recreating the entire table).

• File Structure:
  • Each model is a .sql file.
  • Example: model_name.sql contains a SELECT statement.
  • dbt compiles the SQL into DDL/DML (e.g., CREATE TABLE AS SELECT).

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3. Defining Sources

• Sources: Raw data tables loaded into the database (e.g., green and yellow taxi trip data).

• YAML Configuration:

  • Define sources in a schema.yml file.
  • Specify database, schema, and table names.

  • Example:
    

    version: 2
    sources:
      - name: taxi_trips
        database: my_database
        schema: trip_data
        tables:
          - name: green_trip_data
          - name: yellow_trip_data
    
    

• Benefits:

  • Abstracts the complexity of source locations.
  • Allows for freshness testing to ensure data is up-to-date.

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4. Using Seeds

• Seeds: CSV files stored in the dbt repository (e.g., taxi_zone_lookup.csv).
• Purpose: Used for small, static datasets (e.g., master data tables).
• Benefits:
  • Version-controlled.
  • Can be documented and tested like other dbt models.
• Example:
  • Load taxi_zone_lookup.csv as a seed.
  • Use it in transformations to enrich trip data with zone information.

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5. Modular Data Modeling

• Layers:
  1. Staging: Clean and prepare raw data.
     • Example: stg_green_trip_data.sql and stg_yellow_trip_data.sql.
  2. Core: Create fact and dimensional tables.
     • Example: fact_trips.sql and dim_zones.sql.

• Ref Function:

  • Use ref('model_name') to reference other dbt models.
  • Automatically builds dependencies between models.

  • Example:
    

    SELECT * FROM {{ ref('stg_green_trip_data') }}
    
    

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6. Macros in dbt

• Macros: Reusable SQL code snippets (similar to functions in Python).
• Purpose: Simplify repetitive tasks and dynamically generate SQL.

• Example:

  • Macro to generate payment type descriptions:
    

    {% macro get_payment_type_description(payment_type) %}
        CASE CAST({{ payment_type }} AS INTEGER)
            WHEN 1 THEN 'Credit Card'
            WHEN 2 THEN 'Cash'
            ELSE 'Unknown'
        END
    {% endmacro %}
    
    

• Usage:

  • Call the macro in a model:
    

    SELECT {{ get_payment_type_description('payment_type') }} AS payment_description
    
    

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7. Packages in dbt

• Packages: Libraries of reusable macros and models.
• Example: dbt_utils package provides common utilities (e.g., surrogate_key).

• Installation:

  • Add to packages.yml:
    

    packages:
      - package: dbt-labs/dbt_utils
        version: 0.8.0
    
    

  • Run dbt deps to install.

• Usage:

  • Example: Generate a surrogate key:
    

    SELECT {{ dbt_utils.surrogate_key(['vendor_id', 'pickup_datetime']) }} AS trip_key
    
    

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8. Variables in dbt

• Variables: Dynamic values that can be defined at the project level or passed during execution.

• Usage:

  • Define in dbt_project.yml:
    

    vars:
      payment_type_values: [1, 2, 3, 4, 5, 6]
    
    

  • Use in a model:
    

    {% if var('is_test_run', true) %}
        LIMIT 100
    {% endif %}
    
    

• Execution:

  • Pass variables via CLI:
    

    dbt run --vars '{"is_test_run": false}'
    
    

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9. Building Fact and Dimensional Tables

• Fact Table: Aggregates data from multiple sources.
  • Example: fact_trips.sql combines green and yellow trip data.
  • Union both datasets and join with dim_zones for zone information.
• Dimensional Table: Provides context for fact tables.
  • Example: dim_zones.sql contains zone details (e.g., borough, service zone).

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10. Testing and Deployment

• Testing:

  • Use dbt's built-in tests (e.g., uniqueness, not null).

  • Example:
    

    tests:
      - unique
      - not_null
    
    

• Deployment:

  • Run dbt run to compile and execute models.
  • Use dbt build to run models and tests together.

  • Example:
    

    dbt build --vars '{"is_test_run": false}'
    
    

  • Best Practice to implement dev limit which can be toggle off when in production by simply setting default=false:
    

    -- dbt build --select <model_name> --vars '{'is_test_run': 'false'}'
    {% if var('is_test_run', default=true) %}
    
      limit 100
    
    {% endif %}
    

    → faster, cheaper and can be made as macro to follow DRY ( Don’t Repeat Yourself) methodology.

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11. Lineage and Dependency Management

• Lineage: Visual representation of dependencies between models.
• Benefits:
  • Understand how data flows through the pipeline.
  • Identify upstream and downstream impacts of changes.
• Example:
  • fact_trips depends on stg_green_trip_data, stg_yellow_trip_data, and dim_zones.

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12. Best Practices

• Modularity: Break down transformations into reusable models.
• Documentation: Use YAML files to document sources, models, and tests.
• Version Control: Store dbt projects in Git for collaboration and history.
• Performance: Materialize large tables as tables (not views) for better performance.

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13. Key Commands

• dbt run: Compiles and runs models.
• dbt test: Runs tests on models.
• dbt build: Runs models and tests together.
• dbt deps: Installs packages.
• dbt compile: Compiles SQL without executing it.

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14. Example Project Structure

dbt_project/
├── models/
│   ├── staging/
│   │   ├── stg_green_trip_data.sql
│   │   ├── stg_yellow_trip_data.sql
│   ├── core/
│   │   ├── fact_trips.sql
│   │   ├── dim_zones.sql
├── seeds/
│   ├── taxi_zone_lookup.csv
├── macros/
│   ├── get_payment_type_description.sql
├── schema.yml
├── dbt_project.yml
├── packages.yml

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