Rocky: the typed graph between your code and your warehouse

The disasters Rocky prevents

Rocky exists because the most expensive failures in modern data platforms aren’t slow queries. They’re trust failures. A column type changes upstream and a revenue dashboard quietly diverges for three days. A SELECT * pulls a new column nobody designed for. A Snowflake-only function lands in a Databricks-targeted project and only fails in prod. Warehouse spend doubles and nobody can attribute which model caused it. An auditor asks who changed fct_revenue.amount and when.

Rocky moves correctness into the compiler. Each disaster above becomes something you catch before a row is written, invisible to the warehouse and out of reach of the templating layer above it. Rocky is the typed graph in between.

Typed compiler, not a templating engine

Column-level type inference across the full DAG. 35+ diagnostic codes with actionable suggestions. E013 blocks the PR before a row is written.

Column-level lineage at compile time

Every column traced through every transformation, before execution. rocky lineage-diff main lists per-column downstream blast radius for PR review.

Branches + content-addressed run records

Named branches as isolated schemas. rocky replay <run_id> inspects and verifies a run against its content-addressed record: per-model SQL hashes, row counts, and bytes. --execute --verify re-runs a deterministic content-addressed model to reproduce its output bit-for-bit, locally or on the live warehouse in an isolated replay schema.

Per-model cost attribution

Cost is a column on every run, not a dashboard. [budget] blocks fail the run on overspend. rocky preview cost projects spend at PR time.

AI gated through the compiler

Every AI suggestion type-checks before it lands. Generate, compile, auto-fix, ship. The closed loop nobody else has.

Dialect-divergence lint

P001 catches Snowflake-only constructs in a Databricks project, and the reverse. Cross-warehouse teams stop discovering portability bugs in prod.

Declarative tests and keys, not templated SQL

Tests are declarative TOML and Rocky generates the assertion SQL for your target dialect. Define an assertion once in models/test_definitions.toml and apply it by name with [[use_test]]. Fixture-driven [[test]] blocks seed mock inputs, run the model SQL, and compare the rows on rocky test via DuckDB, no warehouse required. A [[surrogate_key]] block injects a deterministic hash into your SELECT whose value matches dbt_utils.generate_surrogate_key on the same warehouse.

A tested exit door, not a one-way door

rocky emit-sql renders the compiled SQL for your transformation models with no warehouse connection. The output is plain runnable SQL with no Rocky-specific syntax, so adopting Rocky never locks you in.

Get Started in 60 Seconds

curl -fsSL https://raw.githubusercontent.com/rocky-data/rocky/main/engine/install.sh | bash
rocky playground my-first-project
cd my-first-project
rocky compile            # type-check
rocky test               # run assertions locally
rocky run                # materialize the DAG against local DuckDB

No credentials needed; the playground is DuckDB-backed and seeds itself on first run.

rocky run is the one-step path for local iteration and automation. For production or PR-gated deploys, split it into rocky plan (persists an auditable plan to .rocky/plans/<id>.json) and rocky apply <plan-id>.

One trace, one cost graph, one replay handle

rocky run writes a single RunRecord to the state store. Three read commands project that record from different angles: rocky trace for causality and concurrency, rocky cost for per-model warehouse spend, and rocky replay for the byte-for-byte reproduction. Walk through it end-to-end in POC #17 (trace + cost + replay against the same run_id).

Who Rocky is for

Rocky is built first for data platform engineers running production-critical, multi-tenant pipelines on Databricks, the team where silent failures cost real money and dbt Core has hit a ceiling. The trust primitives are most battle-tested there.

The next ring out: Snowflake and BigQuery shops evaluating SQLMesh, who want correctness moved to the compiler rather than the planner, and prefer SQL by default. Adapters are Beta today; see the Roadmap.

Coming from dbt?Run rocky import-dbt against your project, get a Rocky repo on disk, and ship the gains incrementally with no rewrite.

Using Dagster?dagster-rocky wraps the CLI as a ConfigurableResource: auto-discovery, asset checks, and Pipes through one subprocess hop.