Semantic ETL
Transforming Business Logic Into Production‑Ready ETL Code
Modern data teams struggle with a simple truth: business logic lives in documents, meetings, and analysts’ heads—not in code.
I built a tool that changes this.
It takes annotated SQL‑like business rules, written by analysts in the language they already understand, and automatically generates clean, consistent, production‑ready ETL code.
No more translation gaps. No more duplicated logic. No more ambiguity.
Just a single source of truth.
Transforming Business Logic Into Production‑Ready ETL Code
Modern data teams struggle with a simple truth: business logic lives in documents, meetings, and analysts’ heads—not in code.
I built a tool that changes this.
It takes annotated SQL‑like business rules, written by analysts in the language they already understand, and automatically generates clean, consistent, production‑ready ETL code.
No more translation gaps. No more duplicated logic. No more ambiguity.
Just a single source of truth.
1. Analysts write business rules in a familiar SQL‑like DSL
A simple, expressive syntax:
-- Rule: High-value active customers
-- Definition: LTV > 5000 in last 12 months
-- Source: crm.customers, finance.transactions
SELECT
c.customer_id,
SUM(t.amount) AS ltv
FROM crm.customers c
JOIN finance.transactions t
ON c.customer_id = t.customer_id
WHERE t.date >= CURRENT_DATE - INTERVAL '12 MONTHS'
GROUP BY 1
HAVING SUM(t.amount) > 5000;
This is not code—it’s intent.
2. The system parses and validates the rule
It extracts:
- Entities
- Transformations
- Definitions
- Dependencies
- Grain and semantics
This becomes a logical plan.
3. The tool generates ETL code automatically
Depending on your environment, it can produce:
- SQL transformations
- dbt models
- PySpark pipelines
- Merge/SCD logic
- Orchestration‑ready tasks
The generated code is consistent, traceable, and aligned with the business rule.
Why This Matters
A single source of truth
Business logic is defined once—in a human‑readable DSL — and reused everywhere.
No more translation errors
Analysts write the rules. The system generates the pipelines. Nothing gets lost in interpretation.
Governance and lineage built-in
Every ETL artifact can be traced back to the exact business rule that created it.
Faster delivery, fewer mistakes
Data engineers focus on architecture and quality, not rewriting business logic.