What microbatch and other advanced materializations are for

Microbatch is a dbt materialization designed for efficiently processing very large time-series datasets by breaking a model's execution into smaller batches based on a time column, rather than processing the entire dataset in one transformation. It enables incremental processing at the batch level, improving performance, observability, and resilience for massive fact tables compared to standard incremental strategies.

1 · Learn the must-know

• 1Microbatch models require an event_time column configuration to define which timestamp field batches are split on.
• 2You must configure begin (a start date), batch_size (e.g., day, hour, month, year), and the model runs each batch as a separate, independent statement rather than one large query.
• 3Because each batch runs independently, a failure in one batch does not necessarily block other batches from processing, improving fault tolerance for large backfills.
• 4Microbatch supports concurrent batch execution on adapters that enable it, which can significantly speed up historical backfills compared to a single large incremental run.
• 5Unlike standard incremental models, microbatch does not rely on a custom is_incremental() macro block with manual filtering logic; dbt handles the batch filtering automatically based on the configured event_time and batch_size.
• 6The full-refresh behavior and backfill logic for microbatch models differ from standard incremental models, so testing --full-refresh and targeted backfill runs is important before relying on this materialization in production.

2 · Check your understanding