## TL;DR
A timestamp column arriving as string breaks ordering, filtering, and freshness logic downstream: find the load or writer that started emitting strings, cast explicitly at ingestion with a strict format, and quarantine values that do not parse instead of letting a permissive cast null them silently. Then re-run the dependent freshness and drift checks.

```text
column type drift error failed: timestamp column changed to string failure
```

## Use this when
- The check or run fails with this symptom: the timestamp column is now stored or read as string.
- You are working on a column that changed from timestamp to string in the warehouse and the failure repeats on the same data or configuration.
- You need the failing rows, rule, or stage isolated before changing thresholds or code.

## Not for this skill when
- A string column that was always textual dates by design, which needs parsing at use rather than an ingestion fix instead.
- The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

## Steps
1. Sample the string values and record their formats, including whether multiple formats or timezones are mixed. Verify: the value formats present are catalogued
2. Locate the writer change: a CSV export, a JSON flatten, or a schema-on-read change that stopped parsing dates. Verify: the writer change is identified
3. Add an explicit parse at ingestion using the dominant format, with unparseable values routed to a quarantine table with the raw value kept. Verify: parsed values land as timestamps and failures are quarantined
4. Backfill the affected partitions from the raw strings so history is typed consistently. Verify: historical partitions read back as timestamps
5. Rerun freshness and range checks that depend on the column and confirm they evaluate real time again. Verify: dependent checks pass on the retyped column

## Variant phrasings
### timestamp column changed to string schema drift
The verbatim drift phrasing.
### column type drift timestamp to string warehouse
The type-drift phrasing for stored column types.
### string dates breaking freshness check type change
The downstream-impact phrasing for freshness and ordering.

Compatibility: Snowflake, BigQuery, Spark, and dbt models consuming timestamp columns

## Why it happens
Date parsing is often implicit: file readers and JSON flatteners produce timestamps only while the incoming format stays consistent. One format change, one quoted export, or one union with a string branch flips the column to text, and every comparison downstream quietly becomes lexicographic.

## Edge cases / pitfalls
- Store the raw string alongside the parsed timestamp during the transition so parsing decisions stay auditable.
- Multiple incoming formats need an explicit format list. A single guessed format will silently null the rest.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_SpeGppD2f0VOs-21GtDctw