## TL;DR

The batch window dbt computed is invalid, usually because event_time is misconfigured or the begin/end values do not make sense. Check the model's event_time config and batch_size, make sure event_time points at a real timestamp column, and re-run.

## Error

```text
dbt microbatch: batch window error
```

## Steps

1. Open the model config and check event_time and batch_size. event_time must reference a timestamp column present in the model output.
   Expected: You see the configured event_time column and batch size (hour, day, month, year).

2. Verify the column exists and is a timestamp: run a quick select on the model's output or the compiled SQL.
   Expected: The column exists with timestamp values; or you find it missing or mistyped.

3. Fix the config: point event_time at the right column, or change batch_size to match your data grain. If filtering manually, ensure your begin/end logic aligns with the batch.
   Expected: Config references real columns and a sane batch size.

4. Run dbt run --select the_model and watch the batch boundaries in the logs.
   Expected: Batches process with valid windows; no window error.

## When to use

- microbatch model fails with a batch window error
- event_time was just added or changed
- the model worked as incremental but fails as microbatch

## When not to use

- regular incremental model errors (different materialization, different fix)
- event_time column missing entirely (that errors differently; add the column)
- the batch runs but produces wrong data (that is logic, not the window)

## Tool compatibility

- dbt microbatch materialization (1.9+)
- event_time and batch_size model configs
- dbt run logs show the computed batch windows

## Variant phrasings

### batch window error on backfill
Backfills pass explicit begin/end; make sure they align to batch boundaries or dbt rejects the window.

### window error only for month batches
Month boundaries plus timezone-naive timestamps cause off-by-one windows. Check the timestamp timezone handling.

## Why it happens

Microbatch slices the model by event_time into batches and processes each window. If event_time is wrong, the column is not a timestamp, or the window math produces an end before the begin, dbt cannot form a valid batch and fails fast.

## Edge cases

- Microbatch needs the event_time column in the final select; filtering it out in a later CTE breaks the window.
- Changing batch_size on an existing microbatch model changes window boundaries; treat it like a schema change and backfill deliberately.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_aC7PwxsEUSk9hqAn-LM7Eg