SAS to Azure | SAS2PY

Architecture

SAS in. Azure out.

Deterministic parsers read the SAS estate and emit native Azure code — not SAS installed on an Azure VM.

SAS programs → SAS2PY parser → Spark + ADLS + Fabric

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

Spark emit Notebooks

Pipeline emit ADF / Fabric

Azure

Spark notebooks Databricks or Fabric

ADLS Gen2 Delta Lake

Fabric / ADF Replaces SAS Grid

Azure DevOps CI/CD

Unity Catalog When on Databricks

Python jobs Non-SQL remainder

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 Azure

The lake should run the job

A SAS box in Azure is still a SAS box

Lift-and-shift keeps the license and the overnight window. We emit native Spark that ADLS and Fabric already know how to run.

No catalog by accident

Lineage from the parse lands in Purview or Fabric — not a comment in a .sas file.

Windows SAS Grid is a standing ops cost

Azure Data Factory or Fabric pipelines replace the scheduler.

Parser output

SAS BY-group to a Spark window

A DATA step running total with FIRST. reset — emitted as PySpark that runs on Azure Databricks or Fabric Spark.

SAS

/* SAS */
data gold;
  set txn;
  by cust_id;
  if first.cust_id then tot = 0;
  tot + amount;
run;

→

SAS2PY
converts

PySpark on Azure

# BY-group → Spark window
from pyspark.sql import functions as F
from pyspark.sql.window import Window
w = Window.partitionBy("cust_id").orderBy("txn_date")
gold = txn.withColumn("tot", F.sum("amount").over(w))

FIRST. and RETAIN become a window. Storage is ADLS, not a SAS library.

Coverage

SAS to Azure — artifact mapping

SAS	Azure	Notes
DATA step	PySpark / Spark SQL	Azure Databricks or Fabric
PROC SQL	Spark SQL	Lakehouse tables
SAS dataset	ADLS + Delta	ACID on the lake
SAS Grid	ADF / Fabric pipeline	Scheduler replacement
Macro	Expanded then emitted	No leftover SAS

Validation

Every conversion validated to row-level parity

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

See how Data Matching works →

6,400

jobs

5.4M

LOC

$9.2M

saved

3

clouds

Manufacturer: SAS Grids to Iceberg, including Azure

One Iceberg spec across AWS, Azure, and GCP. Azure HQ jobs landed on ADLS with a Hive Metastore. Same parser, native catalog.

Read the case study →

← SAS2PY Home SAS → Databricks SAS → AWS Case studies

Convert SAS programs to Azure