Incident Report: Data Staleness Due to Undetected Incremental Model Failure in dbt-Snowflake Pipeline
Summary
Unreported failure in an incremental loading logic caused critical dashboards to display stale data for 72+ hours. Silent model execution without errors led to undetected data gaps in Snowflake.
Root Cause
A dbt incremental model failed to process new data due to misconfigured filtering logic. Key factors:
- Incremental predicate misalignment between Snowflake data arrival patterns and model configuration
- Missing error propagation for late-arriving data edge cases
- Data watermark drift caused by unsynchronized timestamp columns
Why This Happens in Real Systems
Common systemic pitfalls with dbt + Snowflake:
- Late-arriving data triggering incorrect incremental snapshots
- Snowflake’s auto-suspend feature delaying query errors until compute restarts
- Implicit timezone conversions invalidating date-range filters
- Model dependencies creating hidden failure propagation paths
Real-World Impact
Business consequences observed:
❗️ $230k projected revenue loss from stale sales pipeline data
❗️ 18-hour engineering effort for data repair and validation
❗️ Snowflake compute spike (400% over baseline) during manual backfill
❗️ Stakeholder trust erosion in finance/operational dashboards
Example or Code
Problem configuration:
-- Original flawed incremental logic
{{ config(materialized='incremental') }}
select *
from {{ source('sales', 'transactions') }}
where transaction_time > (select max(transaction_time) from {{ this }})Operational fix:
-- Corrected with safety checks
{{ config(
materialized='incremental',
unique_key='id',
incremental_strategy='time',
timestamp_field='transaction_time_at_utc' -- Enforced timezone
) }}
with new_records as ( ... )
select *
from new_records
-- Explicit late-data handling
where transaction_time_at_utc >= coalesce(
(select max(transaction_time_at_utc) from {{ this }}),
'2020-01-01':: microorganisms
)