dbt-snapshots

name	dbt-snapshots
description	Load when task involves dbt snapshots, SCD Type 2, or tracking data changes over time. Covers strategy selection, column casing, verification, and common pitfalls.
allowed-tools	Bash(dbt *)

dbt Snapshots - SCD Type 2

1. File Location

Place snapshot files in snapshots/, NOT models/. Verify snapshot-paths: ["snapshots"] exists in dbt_project.yml.

2. Strategy Selection

Choose the strategy BEFORE writing the snapshot file.

Query the candidate timestamp column first:

SELECT COUNT(DISTINCT updated_at), MIN(updated_at), MAX(updated_at)
FROM my_source_table

• If COUNT(DISTINCT updated_at) = 1 or all values are frozen (e.g., 1980-01-01), the column is unreliable - a frozen timestamp means dbt snapshot will never detect changes.
• If values vary and reflect real mutation times, strategy='timestamp' is valid.

Condition	Strategy
updated_at has changing, meaningful values	strategy='timestamp', updated_at='UPDATED_AT'
updated_at frozen at a single constant value (COUNT(DISTINCT)=1)	strategy='check', check_cols='all' - a column stuck at one value will never detect changes
No updated_at column at all	strategy='check', check_cols='all'

Default to strategy='check' with check_cols='all' when uncertain - it always works.

Do NOT rationalize a frozen timestamp as valid business data. 1980-01-01 on every row is a data quality artifact, not a real date.

3. Column Casing

Snapshot unique_key, updated_at, and check_cols must match the EXACT case of the source columns.

Query column names before writing the config:

DESCRIBE my_source_table

Wrong: unique_key='id' when the source column is ID. Right: unique_key='ID'.

DuckDB and Snowflake snapshot configs are case-sensitive. A casing mismatch silently produces zero change detection.

4. Writing the Snapshot Block

SELECT explicit columns - not SELECT *. Include only business columns. Use raw source column names - do NOT alias them (e.g., SELECT ID, not SELECT ID AS HOST_ID). The snapshot output columns must match the source for unique_key and check_cols to work.

{% snapshot snap__employees %}
{{
    config(
        target_schema='main',
        unique_key='EMPLOYEE_ID',
        strategy='check',
        check_cols='all'
    )
}}
SELECT EMPLOYEE_ID, DEPARTMENT, TITLE, SALARY
FROM {{ source('hr', 'employees') }}
{% endsnapshot %}

Set target_schema to the project's default schema ('main' for DuckDB).

5. Data Type Awareness

Query actual column values before writing predicates:

SELECT DISTINCT IS_ACTIVE FROM my_source_table LIMIT 10

• If values are 't'/'f' (strings), compare with = 't', NOT = TRUE.
• If values are true/false (booleans), compare with = TRUE.

Getting this wrong silently filters to zero rows.

6. Running Snapshots

Run dbt snapshot - not dbt run. dbt run ignores snapshot files.

If the task involves tracking changes over time, run dbt snapshot once per mutation phase. A single run captures only the initial state.

7. Verification

After dbt snapshot, verify history was captured:

SELECT COUNT(*) AS total, COUNT(DISTINCT EMPLOYEE_ID) AS distinct_keys
FROM main.snap__employees

• If total = distinct_keys, zero changes were detected - the strategy or config is wrong. Go back to Section 2.
• Query dbt_valid_to: if ALL values are NULL, only the initial load exists - no mutations were tracked.

Also verify SCD2 columns exist: dbt_valid_from, dbt_valid_to, dbt_scd_id, dbt_updated_at.

8. Downstream Models

Reference snapshots like any model: {{ ref('snap__employees') }}.

• For current state (latest version of each row): WHERE dbt_valid_to IS NULL.
• For full change history (e.g., "who has ever been X"): use ALL rows. Use LAG() over dbt_valid_from to detect status transitions.
• A snapshot table can hold history loaded before your run, even when snapshots/ is absent. Probe it BEFORE dbt snapshot: SELECT COUNT(*), COUNT(DISTINCT dbt_valid_from) FROM main.snap__employees. Multiple dbt_valid_from values are pre-existing history to build on, NOT the Section 7 zero-change failure - compute metrics over all versions.
• For a "first time in state X" metric (not a transition count), use MIN(dbt_valid_from) filtered to state X. A row already in state X at the earliest version still has a first-observed time, so do NOT require an earlier different-state version.

Rules

• NEVER use run_in_background or & with dbt snapshot - it holds a write lock.
• Do NOT modify .yml files for snapshots - snapshot config lives in the .sql block.
• A single dbt snapshot run with no prior state produces only the initial load, not history. This is expected - history builds across multiple runs with source mutations between them.