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Lakebridge does not read SAS. SAS2PY does — DATA step, macros, DI Studio jobs, SAS/STAT — and emits native PySpark notebooks, Delta tables, and Workflows, with column lineage in Unity Catalog and a parity report your auditors can keep.
4–6 weeks · about 10K lines of your SAS · credited 100% against a license signed within 90 days
Deterministic parsers read the SAS estate and emit native Databricks code — not SAS sitting on a cheaper cluster.
SAS programs → SAS2PY parser → PySpark + Delta + Workflows
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, not loops
Workflow emit
Job DAGs
Databricks
PySpark notebooks
Set-based, not loops
Delta Lake
ACID + time travel
Workflows
Replaces SAS Grid
Unity Catalog
Column lineage
Repos + DABs
CI/CD promote
MLflow
SAS/STAT models
AI is an optional add-on, off by default — the conversion runs end to end without it, air-gapped if your estate requires it.
Use the free tool for the part it was built for. Pay for the part it was not.
| Capability | Lakebridge | SAS2PY |
|---|---|---|
| SQL dialects → Databricks SQL | Yes, free | Yes — not the lead |
| SAS DATA step, macros, %INCLUDE | No | Yes, deterministic parser |
| DI Studio jobs → Workflows | No | Yes |
| SAS/STAT, IML → MLflow / Python | No | Yes |
| Row-level parity + exception report | Reconcile module | Audit-grade — the deliverable |
| Signed evidence pack, as-of lineage | No | Yes (Atlas) |
| Air-gapped / on-prem | No | Yes |
Already part-way through with Lakebridge? The $25K certify pilot proves that output instead of redoing it.
The first engagement
Fixed fee, fixed scope, fixed exit. The fee is credited 100% against a license signed within 90 days, so the only thing you are really spending is calendar time.
Want a smaller first step? The $15K readiness scan inventories the estate in a week and is credited the same way.
SAS Grid scales by adding more of the same. Databricks distributes the rewritten job across a cluster. The batch that finished at 6am now finishes before the trading desk opens.
A thousand %INCLUDE files is not a platform. The parser expands macros, then emits readable PySpark so a new hire can follow the match criteria.
SPD Server and .sas7bdat are not a lakehouse table. We land ACID tables with time travel so yesterday's extract is a version, not a copy job.
A classic SAS match-merge — two sorted datasets, BY key, IF IN= flags. Emitted as an explicit join, not a record loop.
/* SAS: customer + transactions */ proc sort data=cust; by cust_id; run; proc sort data=txn; by cust_id; run; data gold; merge cust (in=a) txn (in=b); by cust_id; if a and b; if amount > 1000 then segment = 'GOLD'; else segment = 'STD'; run;
# SAS match-merge → PySpark join
from pyspark.sql import functions as F
gold = (
cust.join(txn, "cust_id", "inner")
.withColumn(
"segment",
F.when(F.col("amount") > 1000, "GOLD")
.otherwise("STD"),
)
)
BY-group merge becomes an inner join. The IN= flags disappear because the join type is explicit. No sort required.
| SAS | Databricks | Notes |
|---|---|---|
| DATA step | PySpark DataFrame | Assignment, IF/THEN, BY-groups |
| PROC SQL | spark.sql / DataFrame | Joins, GROUP BY, CASE |
| Macro / %INCLUDE | Expanded then emitted | No leftover macro language |
| DI Studio job | Databricks Workflows | Replaces SAS Grid |
| SAS dataset / SPDS | Delta Lake | ACID + time travel |
| SAS/STAT | MLflow + Spark ML | Where a model actually ran |
3.8M lines of SAS modernized to PySpark on Databricks. Pipelines 3–6X faster, $5.8M of the savings from retired SAS licensing. Macro libraries expanded, SAS/STAT models landed in MLflow. The pilot above is the first four weeks of exactly this method.
Read the case study →SAS output compared to Databricks output — row by row, column by column. Differences flagged before sign-off, and the evidence lives on in Atlas after the migration ends. Atlas attaches at $45K in year one, $90K/yr standalone.
See how Data Matching works → · What Atlas is →$40K for the SAS → Databricks pilot: 4–6 weeks, about 10K lines of production SAS, validated row by row. The fee is credited 100% against a license signed within 90 days. If you want a smaller first step, the $15K readiness scan inventories the estate in a week and is credited the same way.
Lakebridge converts SQL dialects and it is free, so keep using it for that. It does not read SAS DATA step, macros, DI Studio jobs, or SAS/STAT, and it does not leave you with an evidence pack an auditor will accept. SAS2PY covers the SAS estate and produces the parity proof. If Lakebridge has already converted a workload, the $25K certify pilot validates that output rather than repeating the work.
Published annual bands by estate size: $120K up to 250K lines, $250K from 250K to 1M, $450K from 1M to 5M, and custom from $750K above that. Same price whether you run it as SaaS or self-hosted. Full detail is on the pricing page.
A representative SAS workload, agreed inputs with the original outputs (or a run in your environment so we can compare), and one named owner for weekly checkpoints. No code upload is needed to start — the intake form only asks what you are modernizing and when your SAS renews.
Three ways to start
Every route below ends with a person who has done this before, not a sales sequence.
Not ready to name a workload? The free sample assessment converts a representative piece of SAS at no cost — request one here.