DATAFORM

Spin up a project, connect your warehouse, and ship reliable data models the same day. In DATAFORM, you write models as SQL-based files, declare dependencies with simple refs, and choose how each object materializes (table, view, or incremental). Use variables and parameterized helpers to avoid repeating logic across datasets and environments. When you run a build, Dataform compiles everything into warehouse-native SQL, publishes models in the right order, and processes only what changed, so you iterate quickly without wasting compute.

Keep quality tight by turning rules into automated checks. Add assertions for not-null, unique keys, foreign-key relationships, row counts, freshness, and custom business constraints. Run them locally during development and again in CI on every pull request; failing checks block promotion and surface clear error messages with compiled SQL for fast debugging. Document columns and models inline, tag them by domain, and generate browsable docs with lineage so analysts know exactly which tables to trust and how they’re produced.

Work as a team using Git flows. Create a branch, develop against a development schema, open a PR, and let CI preview the DAG, run tests, and show impact analysis. Approvals merge to main and trigger deployments to staging, then production. Use incremental strategies and backfills for large tables, run hooks for pre/post operations, and environment-specific configs for connection details. Observe each run with detailed logs, timings, and retries; send alerts to Slack or webhooks when something breaks, and rerun only the affected nodes. Orchestrate on a schedule with Dataform’s scheduler or call runs from Airflow, Dagster, or GitHub Actions via the API.

Apply this workflow to real work. For marketing analytics, standardize event streams, deduplicate sessions, join campaign metadata, and publish daily attribution tables that power BI dashboards. For finance, snapshot dimensions, enforce accounting calendars, and build incremental revenue recognition models with strict integrity checks. For product teams, normalize JSON payloads, maintain feature tables for machine learning, and keep a freshness SLA with automated monitoring. Whether you’re on BigQuery, Snowflake, or Redshift, the same project structure compiles to warehouse-native SQL and delivers consistent, tested datasets to your consumers.