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Show HN: Rocky – Rust SQL engine with branches, replay, column lineage (github.com)

122 points by hugocorreia90 ↗ HN
Hi HN, I'm Hugo. I've been building Rocky over the past month, shipping fast in the open. The binary is on GitHub Releases, `dagster-rocky` on PyPI, and the VS Code extension on the Marketplace. I held off on a broader announcement until the trust-system surface was coherent enough to talk about as one thing. The governance waveplan — column classification, per-env masking, 8-field audit trail on every run, `rocky compliance` rollup, role-graph reconciliation, retention policies — landed end-to-end last week in engine-v1.16.0 and rounded out in v1.17.4 (tagged 2026-04-26). That's the milestone I'd been waiting for.

The pitch: keep Databricks or Snowflake. Bring Rocky for the DAG. Rocky is a Rust-based control plane for warehouse pipelines. Storage and compute stay with your warehouse. Rocky owns the graph — dependencies, compile-time types, drift, incremental logic, cost, lineage, governance. The things your current stack can't give you because it doesn't own the DAG.

A few things I think are interesting:

- Branches + replay. `rocky branch create stg` gives you a logical copy of a pipeline's tables (schema-prefix today; native Delta SHALLOW CLONE and Snowflake zero-copy are next). `rocky replay <run_id>` reconstructs which SQL ran against which inputs. Git-grade workflow on a warehouse.

- Column-level lineage from the compiler, not a post-hoc graph crawl. The type checker traces columns through joins, CTEs, and windows. VS Code surfaces it inline via LSP.

- Governance as a first-class surface. Column classification tags plus per-env masking policies, applied to the warehouse via Unity Catalog (Databricks) or masking policies (Snowflake). 8-field audit trail on every run. `rocky compliance` rollup that CI can gate on. Role-graph reconciliation via SCIM + per-catalog GRANT. Retention policies with a warehouse-side drift probe.

- Cost attribution. Every run produces per-model cost (bytes, duration). `[budget]` blocks in `rocky.toml`; breaches fire a `budget_breach` hook event.

- Compile-time portability + blast radius. Dialect-divergence lint across Databricks / Snowflake / BigQuery / DuckDB (12 constructs). `SELECT *` downstream-impact lint.

- Schema-grounded AI. Generated SQL goes through the compiler — AI suggestions type-check before they can land.

What Rocky isn't:

- Not a warehouse — it's the control plane on top.

- Not a Fivetran replacement. `rocky load` handles files (CSV/Parquet/JSONL); for SaaS sources use Fivetran, Airbyte, or warehouse-native CDC.

- Not dbt Cloud — no hosted UI, no managed scheduler. First-class Dagster integration if you need orchestration.

Adapters: Databricks (GA), Snowflake (Beta), BigQuery (Beta), DuckDB (local dev / playground). Apache 2.0.

I'd love feedback on the trust-system framing, the governance surface (particularly classification-to-masking resolution in `rocky compile` and the `rocky compliance` CI gate), the branches/replay design, the cost-attribution primitives, or anything else that catches your eye. Happy to go deep in the thread.

18 comments

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hasyimibhar
Looks cool, I've been waiting for someone to build this since dbt and SQLMesh acquisition. It would be great to have model versioning and support for ClickHouse SQL.
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mollerhoj
Its a bit confusing to claim that "The things your current stack can't give you because it doesn't own the DAG" and use DataBricks as your example: DataBricks includes jobs and pipelines, so it very much owns the DAG, no?
ramon156
If your introduction message already includes a bunch of uncurated claims and LLM smells, then what does that say about the code I'm about to run?
PeterWhittaker
Congrats on the work, but have you considered another name? Naming is hard and always will be: When I first scanned the headline, my initial thought was "that's an interesting area for the Rocky Linux team to explore". After a moment, "wait, no, that's confusing, it's some other Rocky".
Xiaoher-C
The compile-time lineage part is the most interesting bit to me. A lot of “data lineage” tools feel like archaeology after the fact: parse logs, reconstruct what probably happened, then hope it matches reality.

Having the compiler know “this column flows into these downstream models” before execution changes the workflow quite a bit. It makes refactors and masking policies much less scary.

Do you expose any kind of “lineage diff” between branches? For example: this PR changes the downstream impact of `customer.email` from A/B/C to A/B/D. That would be useful in code review.

jtagliabuetooso
Data contracts as types and compile time checks (even across languages) are not new - this is a recent paper exposing the idea of correctness-by-design pipeline, which is a super set of this particular issue obviously (disclaimer: I'm one of the author of the paper): https://arxiv.org/pdf/2602.02335
data_ders
hiya, anders from dbt here. cool project -- I especially love the branching and budgeting options you've built in. both are things that I'd love for the dbt standard to include one day. was it dbt's lack of those feature that inspired you to start this project? It also seems you have an aversion to Jinja, which, believe me, I get!

FYI dbt-fusion [1] is going GA next week (though GA for Databricks will come later) Most of it is source-available and ELv2-licensed, but there's a number of crates that are Apache 2.0, namely: dbt-xdbc, dbt-adapter, dbt-auth, dbt-jinja, dbt-agate. We also have plans to OSS more as time goes on (stay tuned).

I just wanted to call out the OSS crates in case you'd rather focus on "making your beer taste better" than have to re-build foundations. I'd love to hear if any of those crates come in handy for you (even more so if they don't work for you).

Feel free to reach out on LinkedIn or dbt community Slack if you ever want to chat more!

[1]: https://github.com/dbt-labs/dbt-fusion

jtagliabuetooso
Cool release.

IMO, "Why it's distinctive" is a bit misleading on a few points: certainly the dbt and DX folks can add their POV, but even considering stuff I know / authored ;-), https://arxiv.org/pdf/2308.05368 from 2023 (and following releases) cover branches in a native way (no clone), immutability (re-run), and lineage.

Extensions to be considered are different languages (what about Python), and branch semantics. Two immediate questions would be: can you nest branches? How does merge works across systems if you don't control compute?

hnarn
It’s quite odd to choose the name ”Rocky” when that is already the name of one of the most popular Linux distributions.
hugocorreia90
First time I heard about Rocky Linux distribution. Thank you for the heads up.