Test on your laptop. Ship to your warehouse. Same SQL, same YAML, no rewrite.

Starflow is a declarative Extract · Load · Transform · Orchestrate engine: describe what your pipeline does in YAML and plain SQL, and Starflow works out how: schemas, merges, quality checks, lineage, and the DAGs to run it all.

And because it transpiles your warehouse SQL to DuckDB, the whole pipeline, loads included, runs and tests locally in seconds. Engine choice becomes an environment variable, not a replatforming program.

A pipeline in 30 seconds

Describe the table once: file pattern, merge strategy, validation:

# metadata/load/crm/customers.sl.yml
table:
  pattern: "customers.*.csv"              # files landing in your incoming folder
  metadata:
    writeStrategy:
      type: UPSERT_BY_KEY_AND_TIMESTAMP   # incremental merge, no MERGE SQL to write
      key: [id]
      timestamp: signup
  attributes:
    - name: id
      type: string
      required: true
    - name: signup
      type: timestamp
    - name: email
      type: string                        # or one of your own semantic types, validated at load time

Then let Starflow do the engineering:…