Migrate from SAS with your coding agents

data work.big_orders;
  set sales.orders;
  where amt > 1000;
  tier = 'Key';
  net = amt * 0.9;
run;

proc sql;
  create table work.by_region as
  select region, sum(net) as net
  from work.big_orders
  group by region;
quit;

# Converted by Alchemist
from pyspark.sql import functions as F

big_orders = (
  spark.table("sales.orders")
  .filter(F.col("amt") > 1000)
  .withColumn("tier", F.lit("Key"))
  .withColumn("net", F.col("amt") * 0.9)
)

by_region = (
  big_orders.groupBy("region")
  .agg(F.sum("net").alias("net"))
)
by_region.createOrReplaceTempView(
  "by_region"
)

Same engine, same PySpark, whether the code runs on Databricks, Snowflake or Microsoft Fabric. Agents are one way into the SAS to PySpark migration toolkit, which also analyzes your SAS estate and automates the migration around the conversion.

1. Time

   A manual SAS rewrite is measured in months, and most of that time goes to repetitive translation rather than design.

2. Consistency

   Each developer translates a little differently, and across hundreds of programs those differences add up to logic drift.

3. Who can take part

   When only engineers can convert, business and analytics teams queue for their turn. Agents let them run their own scope.

1. 1 · Your agentDatabricks Genie, Snowflake Cortex, Microsoft Copilot, Claude Codenatural-language request →
2. 2 · AlchemistSkill → Alchemist APIapi.getalchemist.ioconverted code →
3. 3 · Your notebookReview, refine, run

• Databricks Genie
• Snowflake Cortex
• Microsoft Copilot
• Claude Code
• Any MCP client (in development)