Implement dbt microbatch incremental models for event-stream tables with sub-daily refresh cadence

Steps

1. Set materialized='incremental' and incremental_strategy='microbatch' in the model config block; microbatch is available in dbt 1.9+ and requires a supported warehouse adapter
2. Declare event_time pointing to the timestamp column that defines the batch boundary and begin indicating the earliest date to process
3. Define batch_size as 'hour', 'day', or another supported granularity; dbt will automatically split historical backfills into batches of this size
4. Write the model SQL to filter only on the current batch window using {{ model.config.event_time }} implicitly; dbt injects the batch filter automatically — avoid adding manual is_incremental() blocks for the time filter
5. Run dbt run --select my_microbatch_model to process only the current batch window in production, or dbt run --full-refresh to reprocess all batches from begin
6. Monitor batch progress in the dbt logs; each batch logs its start and end timestamp so you can identify slow batches or gaps