Convert SAS programs to native PySpark

How does SAS become PySpark? SAS in. PySpark out.

Deterministic parsers read the SAS estate and emit native PySpark code, not a Python while-loop that still reads one row.

SAS programs → SAS2PY parser → Native PySpark

SAS

Base SAS DATA step / macros

DI Studio Jobs + mappings

EG / EM Projects + flows

Viya / CAS CASL + actions

→

SAS2PY Parser

Deterministic parse AI where it helps

Row-level parity Before cutover

PySpark emit Set-based

Notebook / .py Your repo

→

PySpark

PySpark DataFrames

Python modules Reusable

Git Reviewed diffs

Any cluster Databricks / EMR / k8s

MigryX AI handles the logic parsers cannot resolve alone, and every change it makes goes through the same parity checks. It runs on a model you approve, air-gapped if your estate requires it.

SAS DO loop to a set operation

A row-at-a-time running total, emitted as a filter and window, not a Python for-loop.

/* SAS row loop */
data gold;
  set txn;
  if amount > 1000;
  tot + amount;
run;

# loop → filter + window
from pyspark.sql import functions as F
from pyspark.sql.window import Window
gold = txn.filter(F.col("amount") > 1000).withColumn(
    "tot", F.sum("amount").over(Window.orderBy("txn_date"))
)

The running total is a window. The filter is a predicate. No iterator.

How is the conversion proven? Every conversion validated to row-level parity

SAS output compared to PySpark output: row by row, column by column. Differences flagged before sign-off.