Offer

SAS to Databricks Migration

Leaving SAS shouldn’t mean a rewrite or a gamble

Overview

A few thousand .sas programs, decades of logic, a model risk team watching. Here’s why we think the smarter way off SAS and onto Databricks combines automation with judgment – not one or the other.

30-40%

of a typical SAS estate doesn’t need migrating at all

70%

of what survives converts on certified, deterministic patterns

4

stages: convert, translate, review, prove

The Real Problem

It’s less a coding exercise than a knowledge-recovery exercise with a compiler attached.

Some of those thousands of programs are load-bearing – nightly batches that score customers, jobs that feed regulatory reports. A meaningful share of them aren’t. Few teams can say with confidence which is which, and that’s what makes a SAS exit hard, not the Spark SQL.

Two Tools, Two Limits

Two standard answers and both fail on their own.

Rule-Based Converter

Strong at the repetitive core

Handles well:

  • Standard DATA steps, PROC SQL, sort-means-freq pipelines – roughly 70% of what survives triage. Certified once, identical on the ten-thousandth program.

Stops at:

  • The long tail – macros that generate different code at runtime, PROC IML, hash-object lookups, x commands shelling out to the OS.

Large Language Model

Strong at the long tail

Handles well:

  • One-off constructs and decades-old idioms no rulebook was built to cover.

Stops at:

  • Trust at volume – missing values that sort differently than expected, dates measured from 1960, first./last. logic that assumes sorted input. Non-reproducible, so every output needs re-validation.

Our Approach

Every surviving program comes out the other side as runnable Databricks Spark SQL. A step only counts as converted when a certified pattern fully recognizes it – nothing is silently guessed, and nothing is quietly dropped.

Deterministic

Fully recognized, translated by a certified pattern. Proven once, identical on every recurrence.

LLM-Assisted

The converter’s rejects, translated by the model. Labeled in the output, original SAS kept alongside for audit. Never counted as deterministic coverage.

Needs Review

Passed through untouched, reason written inline e.g. an unsupported x command, plus a targeted question for your SMEs where intent is unclear.

See It in Action

Three Workstreams That Ride Alongside the Migration

Models and MRM

Models land in MLflow, with their paperwork.

Scorecards and SAS/STAT models extracted or retrained into the registry, with score-parity evidence generated as part of the process – the same ground we cover in our banking and financial services work.

Docs-as-Code

The logic gets written down, at last.

Each program’s business logic reverse-engineered into readable, versioned documentation captured before that knowledge walks out the door.

Modernize

Rebuilt for 2026, not embalmed.

Refactored to medallion and Delta Live Tables, lineage in Unity Catalog, deployed via Workflows and Asset Bundles – part of our broader lakehouse architecture practice.

How an Engagement Runs

1

Weeks 1–3

Census and station forecast. We run the census on your actual estate and return the split: what retires, what converts, what needs the LLM stage, what needs people.

2

Migration Waves

The line runs. Retirements and quick wins first, regulatory-critical pipelines with full SME involvement where the stakes are real.

3

Cutover

Evidence, then the switch. Old and new estates run in parallel; the cutover date is set by what the evidence shows.During the workshop, we’ll demonstrate how organizations use these capabilities to accelerate AI adoption, improve access to information, and unlock value from their data.

Put Your Estate Through the Consensus

We’ll run a sample of your programs through the process and hand you the station-by-station split. Including what you can simply retire before you commit to anything.

Let’s Talk!

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