dagster-rocky
Dagster integration for Rocky, the typed graph between your code and your warehouse.
dagster-rocky wraps the rocky CLI as a Dagster ConfigurableResource and exposes Rocky-managed
tables as materializable Dagster assets. Compile-time contracts, column-level lineage, schema-drift
detection, quality check results, and per-model cost surface as native Dagster events, so the
guarantees Rocky enforces at compile time appear directly in the Dagster asset graph.
Install
uv add dagster-rocky
You'll also need the Rocky CLI on your $PATH:
https://github.com/rocky-data/rocky/releases?q=engine
Quick start (component)
Add a defs.yaml next to your other Dagster definitions:
type: dagster_rocky.RockyComponent
attributes:
binary_path: rocky
config_path: config/rocky.toml
models_dir: models
Dagster's component loader will:
- Run
rocky discover(androcky compile, when models are present) and cache the result. - Build one subset-aware
multi_assetper Rocky group, with declaredrow_count/column_match/freshnesschecks per table. - On materialization, shell out to
rocky run --filter <key>=<value>for the selected subset and yieldMaterializeResult+AssetCheckResultevents with rich metadata.
Quick start (resource)
import dagster as dg
from dagster_rocky import RockyResource
rocky = RockyResource(
binary_path="rocky",
config_path="config/rocky.toml",
timeout_seconds=3600,
)
defs = dg.Definitions(resources={"rocky": rocky})
Then in an asset:
@dg.asset
def acme_orders(rocky: RockyResource) -> dg.MaterializeResult:
result = rocky.run(filter="tenant=acme")
return dg.MaterializeResult(
metadata={"tables_copied": result.tables_copied, "duration_ms": result.duration_ms},
)
Public API
| Symbol | Purpose |
|---|---|
RockyResource |
ConfigurableResource wrapping the Rocky CLI |
RockyComponent |
State-backed Dagster component that loads Rocky tables as assets |
RockyDagsterTranslator |
Subclass to customize asset key / group / tag mapping |
RockyMetadataSet |
Namespaced metadata (source_id, strategy, watermark, …) |
load_rocky_assets() |
Functional helper that returns AssetSpec for each Rocky table |
emit_materializations() / emit_check_results() |
Convert a RunResult into Dagster events |
check_metadata() |
Build a metadata mapping for a single Rocky CheckResult |
cost_metadata_from_optimize() |
Extract per-model cost recommendations from OptimizeResult |
parse_rocky_output() |
Auto-detect a Rocky JSON payload and return the matching Pydantic model |
Documentation
- Dagster Integration docs: resource, component, translator, schedules, sensors, pipes, and more
- DEVELOPMENT.md: local setup, architecture, testing
- CHANGELOG.md: release notes
Related projects
- Rocky: the Rust SQL transformation engine
- Rocky VS Code extension: VS Code extension with LSP and AI features
License
Apache 2.0
1 maintainer
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