OQEN
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Impact analysis for Python + SQL pipelines

Understand blast radius before your next warehouse change.

OQEN reads the Python and SQL that actually move your data, connects them to the tables they touch, and shows what a change will hit before you commit it. AI guidance stays advisory and human-reviewed.

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Start on a sample pipeline, then bring one of your own change requests. Either way you see what depends on it, what could break, and what needs review before implementation.

orders_etl.py

Change under review · status_code → order_status

# orders_etl.py · nightly refreshsql = f"""  INSERT INTO finance.orders_mart    (order_id, status_code, amount)  SELECT o.id, o.status, p.amount  FROM raw.orders o  JOIN raw.payments p ON p.order_id = o.id"""conn.execute(sql)
Resolved from coderaw.orders readraw.payments readfinance.orders_mart write
Dependency graph: raw.orders and raw.payments feed orders_etl.py, which writes finance.orders_mart. Renaming status_code to order_status is the change under review; its predicted blast radius covers the finance.orders_daily view, the dbt test status_valid, and the 06:00 daily refresh. refunds_mart is unaffected.SOURCESPIPELINE LOGICCHANGE UNDER REVIEWPREDICTED BLAST RADIUSraw.ordersraw.paymentsorders_etl.pyPython · embedded SQLfinance.orders_martstatus_code → order_statusorders_dailyview · financedbt teststatus_validdaily refresh06:00refunds_martunaffectedChange under reviewPredicted blast radiusUnaffected path
Illustration built from a sample pipeline, not a live run.

Reads Python and SQL

Scripts, DAGs, and stored procedures, read as written.

PostgreSQL and Oracle

Warehouse metadata resolved to the same objects the code touches.

Built for live changes

Schema changes, migrations, model redesigns.

Why OQEN

Stop reverse-engineering dependencies before every change.

Dependency checks pull engineers into days of reading SQL, DAGs, and Python ETL. OQEN builds the picture once, from the code and metadata you already have.

Lineage across the Python–SQL boundary

Every table a job reads or writes, traced through the Python that builds the query, not only the SQL you can see.

Blast radius for one change

Pick the change you are about to make. See the downstream models, views, tests, and jobs it will reach, and which paths stay untouched.

Review notes you can defend

Cited, advisory guidance for the reviewer: what to stage, what to alias, what to check. Human-reviewed before anything is implemented.

How it works

From fragmented context to review-ready decisions.

  1. 01

    Connect

    Point OQEN at the warehouse metadata and the pipeline code behind the change: Python, SQL, DAGs, stored procedures.

  2. 02

    See

    OQEN resolves the code to the objects it touches and builds one dependency view with the likely blast radius.

  3. 03

    Decide

    Review AI-assisted explanations and design options as advisory input. Approve, stage, or stop the change with context.

Human-reviewed guidance. AI recommendations stay advisory. OQEN does not auto-apply schema changes.

Fit

Where OQEN fits, and where it does not.

A strong fit when

  • You are changing a live warehouse: schema changes, migrations, and model redesigns where downstream dependencies are unclear.
  • Pipeline logic lives in SQL and Python, including SQL embedded in scripts and DAGs, rather than in one clean, documented layer.

Not the right tool when

  • You only need a static inventory of tables and owners. OQEN is not a data catalog and not a governance suite; a catalog is the better tool.
  • You already trust your cross-source lineage and change risk is low. You may not need a dedicated impact-analysis step.

Scoped pilot

Start with one blocked redesign decision.

Run the sample to see how it works, then bring one active migration, schema change, or model-rationalization decision. Book a guided demo when you want us to walk through yours.

Run it on a sampleBook a demo