SAS to PySpark

Architecture

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 optional

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

AI is an optional add-on, off by default — the conversion runs end to end without it, air-gapped if your estate requires it.

Why PySpark

The rewrite has to be set-based

Line-by-line Python is lift-and-shift

A for-loop over a SAS dataset is the same program in a new costume. We emit joins, windows, and aggregations.

You already picked Spark

Databricks, EMR, or on-prem Spark — the parser does not care. The notebook is portable.

Macros must disappear

Expanded before emit. The output is Python a Spark engineer can read.

Parser output

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

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

→

SAS2PY
converts

PySpark

# 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.

Coverage

SAS to PySpark — artifact mapping

SAS	PySpark	Notes
DATA step	DataFrame API	No row loops
PROC SQL	spark.sql	Same joins
PROC MEANS / SUMMARY	groupBy + agg	Native
Macro	Expanded Python	Readable
SAS dataset	Parquet / Delta / Iceberg	Your choice

Validation

Every conversion validated to row-level parity

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

See how Data Matching works →

5,100

jobs

4.4M

LOC

$7.4M

saved

88%

no rewrite

National Grocer: 5,100 SAS jobs to open PySpark

Grid and DI Studio jobs rewritten as Apache PySpark on EMR and Glue. Same S3 prefixes. No lakehouse control plane required.

Read the case study →

← SAS2PY Home SAS → Databricks SAS → AWS Case studies

Convert SAS programs to native PySpark