dbt Microbatch: Incremental Models & Backfills Like a Pro

dbt Microbatch: Incremental Models & Backfills Like a Pro Learn how dbt microbatch changes the way you build dbt incremental models. This tutorial walks through the microbatch incremental strategy: configuring event_time, batch_size, begin and lookback, running a dbt backfill, and retrying only failed batches. It's for analytics and data engineers running large time-series models who are tired of hand-writing is_incremental logic and full refreshes. What dbt microbatch helps you solve: Fragile is_incremental filters and `{{ this }}` lookups you have to write and maintain yourself Expensive full refreshes just to reprocess a few days of bad data Late-arriving records that slip past your incremental window One failed run forcing you to rebuild everything instead of only the failed batches Choosing between merge, delete+insert and insert_overwrite for each warehouse Upstream tables being fully scanned on every run Chapters: 0:00 Intro 1:02 Microbatch vs incremental, how it works 2:10 When to use microbatch 3:34 Configuring microbatch 4:46 How microbatch works with dbt threads 6:33 When can microbatch parallelise 7:11 Backfilling and retries 7:45 Orchestra example of a dbt microbatch backfill 10:51 Wrap up Orchestra is the AI-native data orchestration platform. It lets you run your dbt projects as part of end-to-end pipelines, so incremental microbatch runs and targeted backfills sit alongside ingestion and downstream tasks. You get one place to schedule, monitor and rerun the jobs that keep your incremental models fresh. Learn more about Orchestra: https://getorchestra.io Sign up: https://app.getorchestra.io Read the docs: https://docs.getorchestra.io #dbt #analyticsengineering #batchprocessing #dataengineering #dataplatform #developertutorial #orchestration #orchestra