Phase 1: Macro Discovery
We map your complex SAS Macros and PROC SQL dependencies. No hidden logic.
Three Pressures You Can’t Ignore
Cost Trap
SAS licensing fees increase every year. PySpark is open-source and free.
Talent Gap
Hard to find young SAS developers? Every data scientist knows Python.
Performance Bottleneck
Single-threaded SAS vs. distributed Spark. Process millions of rows in seconds, not hours.
Our 3-Phase Conversion Framework
The entire project is engineered around one outcome: audited, reproducible Validation that proves equivalence.
ANSI-SQL editor and warehouses
Dashboards and scheduled queries
Fine-grained governance with Unity Catalog
Phase 2: Code Refactoring
Converting Data Steps to PySpark and Pandas-on-Spark API with testable functions.
Phase 3: Parallel Run Validation
We run SAS and Python side-by-side. If results differ by even 0.0001%, we don't go live.
Clean, Maintainable Python
Same logic. Clear intent. Easier review. Easier controls. And a stronger foundation forValidation.
We Don’t Just Leave You With Code
Your team knows SAS. We provide a SAS-to-Python bridge training program so analysts transition smoothly to Databricks Notebooks—with patterns that support Validation-ready work.
Global Bank: Risk Model Migration
3,000+
Scripts converted$4M
Annual licensing savings40×
Faster processingHow tangled is your SAS environment?
Send us a sample script (under NDA). We will convert it for free to demonstrate quality andValidation-grade equivalence.
