Dbt Expert

dbt Expert

You are working with dbt (data build tool) targeting Snowflake. Apply production-grade patterns at every level — clean SQL, thorough YAML metadata, sensible tests, and a project structure that scales.

Model Materializations

Configure at the model level (override project defaults):

{{ config(
    materialized='incremental',
    unique_key='event_id',
    incremental_strategy='merge',
    cluster_by=['date_day', 'account_id']
) }}

Incremental Strategies on Snowflake

merge (default) — upsert by unique_key

{{ config(materialized='incremental', unique_key='id', incremental_strategy='merge') }}

select ...
from {{ source('raw', 'events') }}
{% if is_incremental() %}
  where loaded_at > (select max(loaded_at) from {{ this }})
{% endif %}

delete+insert — for partitioned tables without a reliable unique key

{{ config(
    materialized='incremental',
    unique_key=['date_day', 'account_id'],
    incremental_strategy='delete+insert',
    partition_by={'field': 'date_day', 'data_type': 'date'}
) }}

append — event logs, audit tables where duplicates are acceptable

{{ config(materialized='incremental', incremental_strategy='append') }}

insert_overwrite — replace entire partitions (Snowflake dynamic partition pruning)

On schema changes: always add on_schema_change='sync_all_columns' for production incremental models — silently ignoring schema drift causes silent data loss.

Project Structure

dbt_project/
├── dbt_project.yml          # Project config
├── profiles.yml             # Connection config (NOT in git)
├── packages.yml             # dbt-utils, dbt-expectations, etc.
├── models/
│   ├── staging/             # 1:1 with source tables, light casting only
│   │   ├── _sources.yml     # source() definitions + freshness
│   │   ├── _staging.yml     # staging model docs + tests
│   │   └── stg_*.sql
│   ├── intermediate/        # Business logic joins, not exposed
│   │   └── int_*.sql
│   ├── marts/               # Final models consumed by BI/apps
│   │   ├── core/
│   │   └── finance/
│   └── utils/               # Generic macros used across layers
├── macros/
├── seeds/                   # Small reference CSVs
├── snapshots/               # SCD Type 2 models
├── tests/                   # Singular (custom SQL) tests
└── analyses/                # Ad hoc SQL, not materialized

Naming rule: stg_<source>__<entity>, int_<entity>__<verb>, fct_<event>, dim_<entity>

Schema YAML — Complete Structure

# models/staging/_staging.yml
version: 2

models:
  - name: stg_salesforce__accounts
    description: "One row per Salesforce account, cast and renamed."
    config:
      tags: ["salesforce", "staging"]
    columns:
      - name: account_id
        description: "Natural key from Salesforce."
        data_tests:
          - not_null
          - unique
      - name: created_at
        description: "UTC timestamp of account creation."
        data_tests:
          - not_null
      - name: account_type
        data_tests:
          - accepted_values:
              values: ["Customer", "Partner", "Prospect"]

# models/staging/_sources.yml
version: 2

sources:
  - name: salesforce
    database: RAW_DB
    schema: SALESFORCE
    freshness:
      warn_after: {count: 12, period: hour}
      error_after: {count: 24, period: hour}
    loaded_at_field: _loaded_at
    tables:
      - name: account
        identifier: ACCOUNT  # actual table name if different
        description: "Raw Salesforce accounts."
        columns:
          - name: Id
            data_tests:
              - not_null
              - unique

Tests — Layered Strategy

Generic tests (in schema.yml): not_null, unique, accepted_values, relationships

dbt-utils tests (install dbt-utils):

- dbt_utils.expression_is_true:
    expression: "amount >= 0"
- dbt_utils.unique_combination_of_columns:
    combination_of_columns: ["date_day", "account_id", "metric"]
- dbt_utils.not_empty_string:
    column_name: account_name

dbt-expectations for statistical tests:

- dbt_expectations.expect_column_values_to_be_between:
    min_value: 0
    max_value: 1

Singular tests (tests/ folder) — custom SQL that returns rows on failure:

-- tests/assert_fct_orders_amount_positive.sql
select order_id
from {{ ref('fct_orders') }}
where amount < 0

Macros

Date spine:

-- macros/generate_date_spine.sql
{% macro generate_date_spine(start_date, end_date) %}
  {{ dbt_utils.date_spine(
      datepart="day",
      start_date="cast('" ~ start_date ~ "' as date)",
      end_date="cast('" ~ end_date ~ "' as date)"
  ) }}
{% endmacro %}

Surrogate key:

select
  {{ dbt_utils.generate_surrogate_key(['account_id', 'date_day']) }} as sk_account_day,
  ...

Safe divide:

{% macro safe_divide(numerator, denominator) %}
  iff({{ denominator }} = 0, null, {{ numerator }} / {{ denominator }})
{% endmacro %}

Snapshots (SCD Type 2)

-- snapshots/snp_accounts.sql
{% snapshot snp_accounts %}
  {{
    config(
      target_schema='SNAPSHOTS',
      unique_key='account_id',
      strategy='timestamp',
      updated_at='updated_at',
      invalidate_hard_deletes=True
    )
  }}
  select * from {{ source('crm', 'accounts') }}
{% endsnapshot %}

Snowflake-specific dbt Patterns

Clustering keys (for large tables queried by date range):

{{ config(cluster_by=["date_day", "account_id"]) }}

Dynamic tables (Snowflake-native, set materialized='dynamic_table'):

{{ config(
    materialized='dynamic_table',
    snowflake_warehouse='TRANSFORM_WH',
    target_lag='5 minutes'
) }}

Warehouse override for expensive models:

{{ config(snowflake_warehouse='LARGE_WH') }}

Copy grants so downstream roles keep access after table rebuild:

{{ config(materialized='table', copy_grants=True) }}

Stage-based seed loading for large reference files (avoid in-YAML seed if >10k rows):

-- Use a model that reads from a named Snowflake stage instead of dbt seed
select $1::varchar as code, $2::varchar as label
from @RAW_DB.PUBLIC.REFERENCE_STAGE/lookup.csv
(file_format => 'CSV_FORMAT')

dbt_project.yml Key Config

name: 'my_project'
version: '1.0.0'
config-version: 2

profile: 'snowflake_prod'

model-paths: ["models"]
test-paths: ["tests"]
snapshot-paths: ["snapshots"]
seed-paths: ["seeds"]
macro-paths: ["macros"]

target-path: "target"
clean-targets: ["target", "dbt_packages"]

models:
  my_project:
    staging:
      +materialized: view
      +schema: STAGING
    intermediate:
      +materialized: ephemeral
    marts:
      +materialized: table
      +schema: MARTS
      core:
        +tags: ["core", "daily"]

vars:
  start_date: "2020-01-01"

Generating dbt Projects Programmatically

When generating dbt YAML files from code (e.g., from ETL metadata), follow these rules:

• sources.yml: one file per source system, in models/staging/
• schema.yml: one file per subfolder (staging, marts, etc.), named _<layer>.yml
• Always include version: 2 at the top of every YAML file
• Columns referenced in data_tests must match the SQL output column names exactly
• identifier field in sources is only needed when the dbt model name differs from the actual table name
• Freshness config only belongs on sources, not models
• ref() creates lineage — always use it instead of hardcoding table names
• source() for raw tables — never ref() a staging model that wraps a source

Quick Diagnostics

dbt debug                          # Test connection
dbt compile --select my_model      # See rendered SQL without running
dbt run --select +my_model         # Run model + all ancestors
dbt test --select my_model         # Run tests for one model
dbt docs generate && dbt docs serve  # Browse lineage DAG
dbt source freshness               # Check source staleness
dbt list --select tag:daily        # Find all models tagged daily