SAS2PY converts SAS, and SQL dialects directly to Databricks — PySpark, Delta Lake (MERGE, Z-ORDER, Liquid Clustering), Medallion Architecture, Unity Catalog, DLT Pipelines, and Databricks Workflows — with +95% parsing accuracy and column-level lineage.
Databricks Targets
Every modernization generates production-ready Databricks Lakehouse artifacts — following Medallion Architecture (Bronze → Silver → Gold), optimized for Photon engine, and governed by Unity Catalog.
Production-grade PySpark code with Auto Loader ingestion, Change Data Feed (CDF) CDC patterns, and Medallion Architecture layering — Bronze raw, Silver cleansed, Gold aggregated.
ACID-compliant Delta tables with MERGE INTO upserts, OPTIMIZE & Z-ORDER compaction, Liquid Clustering, schema evolution, time travel, and Change Data Feed enabled.
Column-level lineage, STTM mappings, attribute-based tags, and fine-grained access controls registered in Unity Catalog — full data contract governance across the Lakehouse.
ETL pipelines converted to Databricks Workflows via Asset Bundles (DABs) — dependency-aware multi-task DAGs, serverless compute, job clusters, and triggered/scheduled orchestration.
Streaming and batch ETL converted to DLT pipelines — declarative @dlt.table definitions, Auto Loader CDC ingestion, data quality expectations, and enhanced autoscaling.
Converted code delivered as annotated Databricks Notebooks — with %sql / %python cells, Databricks Connect compatibility, lineage comments, and inline validation cells.
SAS analytical models and scoring logic converted to Python — MLflow experiment tracking, model registry, Feature Store integration, AutoML baselines, and Model Serving endpoints.
Legacy SQL dialects transpiled to Databricks SQL — Photon-optimized queries, serverless SQL Warehouses, ANSI SQL compliance, and 500+ dialect function mappings.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Databricks-native code.
Automate SAS Base, Macro, PROC SQL, and IML conversion to PySpark and Databricks SQL. Full macro expansion, DATA step logic, FORMAT/INFORMAT handling, and PROC SORT/MEANS/FREQ translation.
Before you modernize, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them directly into Databricks Unity Catalog.
How It Works
The same proven methodology applies to every source — SAS — all landing on Databricks.
Upload source artifacts — SAS scripts — into SAS2PY.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage maps.
Parser-driven conversion to PySpark, Delta Lake, Databricks SQL, Workflows, or DLT — with full documentation.
Row-level and aggregate data matching between legacy and Databricks outputs — audit-ready evidence for sign-off.
Publish lineage, STTM, and data contracts to Unity Catalog. Merlin AI surfaces risk and recommends optimization paths.
Platform Capabilities
Every SAS2PY modernization is engineered for the full Databricks Lakehouse — Medallion Architecture, Photon-optimized SQL, Unity Catalog governance, Delta Lake storage, and Asset Bundle deployment.
Purpose-built for each source language. SAS macro expansion - full fidelity, deterministic output, no approximation.
Legacy pipelines restructured into Bronze (raw ingestion via Auto Loader), Silver (cleansed, deduplicated), and Gold (aggregated, business-ready Delta tables) layers automatically.
Tables generated with MERGE INTO upserts, OPTIMIZE & Z-ORDER compaction, Liquid Clustering, Change Data Feed, schema enforcement, and time travel — production-ready from day one.
Source-to-target column mappings, STTM tables, and data contracts published to Unity Catalog — fine-grained access, attribute tags, and Databricks Lineage API integration.
AI analyzes parsed metadata to recommend Photon optimization, Z-ORDER keys, and partition strategies. SAS models land in MLflow Feature Store with AutoML baseline generation.
Full deployment behind your firewall with Asset Bundle (DAB) packaging for CI/CD. Source code and lineage never leave your network. SOX, GDPR, BCBS 239 ready.
Deep Platform Integration
SAS2PY isn't a generic modernization tool retrofitted for Databricks. Every output is built for Databricks-native execution — Photon-optimized, Unity-governed, serverless-ready, and deployed via Asset Bundles.
Generated SQL and PySpark leverage Photon-compatible patterns — vectorized column operations, predicate pushdown hints, and join strategies optimized for Photon's C++ execution engine.
Photon RuntimeModernized workloads target Serverless SQL Warehouses and Serverless Jobs compute — auto-provisioned, zero-management clusters with instant startup and cost-efficient scaling.
ServerlessSource system connections mapped to Databricks LakeFlow Connect ingestion pipelines — replacing legacy source connectors with managed, incremental CDC ingestion into Delta Lake.
LakeFlowSAS analytical models (PROC LOGISTIC, PROC GLM, PROC MIXED) converted to Python and registered in Mosaic AI — with Model Serving endpoints, A/B testing, and Feature Engineering tables.
Mosaic AIAll modernized artifacts packaged as Databricks Asset Bundles — version-controlled YAML definitions, CI/CD-ready deployment, environment promotion (dev → staging → prod), and git integration.
DABs / CI/CDColumn-level lineage, STTM mappings, data classification tags, row-level security policies, and attribute-based access controls published directly to Unity Catalog — not sidecar metadata.
Unity CatalogLegacy reports (SAS PROC REPORT, Crystal Reports, SSRS) converted to Databricks SQL queries with AI/BI Dashboard definitions — parameterized queries, scheduled refreshes, and alert triggers.
AI/BI DashboardsCross-organization data sharing patterns preserved during modernization — legacy file-based data exchange converted to Delta Sharing recipients, providers, and shares with fine-grained access control.
Delta SharingModernization status dashboards, lineage explorers, and validation reports deployed as Databricks Apps — custom Streamlit/Gradio applications running natively inside the Databricks workspace.
Databricks AppsModernization Architecture
Every SAS2PY modernization follows a deterministic pipeline that lands production-ready artifacts directly on the Databricks Lakehouse — governed, validated, and deployment-ready.
Measurable Results
Organizations using SAS2PY to land on Databricks accelerate delivery, reduce risk, and eliminate manual rewrite costs across every modernization program.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite work.
Complete visibility into dependencies prevents production incidents and modernization-related data defects.
Reduced consulting spend, accelerated time-to-value, and eliminated rework deliver 60%+ cost savings.
Deterministic custom parsers deliver +95% accuracy out of the box. Optional AI augmentation pushes accuracy up to 99%.
Why SAS2PY
Generic ETL scanners approximate lineage. SAS2PY parses it exactly — every macro, every column, every dialect — then lands it natively on Databricks.
| Capability | SAS2PY | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, etc.) | ✓ | ✗ |
| 100% column-level lineage to Unity Catalog | ✓ | ~ |
| Native Delta Lake & DLT output generation | ✓ | ✗ |
| Databricks Workflows DAG generation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Parser-driven risk analysis & Databricks optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & Unity Catalog registration | ✓ | ~ |
| Medallion Architecture (Bronze/Silver/Gold) generation | ✓ | ✗ |
| Delta Lake MERGE INTO & Liquid Clustering patterns | ✓ | ✗ |
| Databricks Asset Bundles (DABs) for CI/CD deployment | ✓ | ✗ |
| Deterministic AST-based parsing (not regex or AI-only) | ✓ | ✗ |
| MLflow model modernization from SAS PROC MODEL | ✓ | ✗ |
| PySpark notebook output with inline lineage comments | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported
Frequently Asked Questions
Common questions from teams evaluating SAS2PY for Databricks modernization programs.
Databricks-native. SAS2PY generates PySpark that leverages Databricks-specific APIs — Delta Lake MERGE INTO with CDC patterns, Auto Loader for ingestion, Unity Catalog references for table governance, DLT @dlt.table decorators, and Databricks Workflows YAML definitions. It is not generic Spark code adapted for Databricks.
SAS2PY automatically restructures legacy pipelines into Bronze (raw ingestion via Auto Loader or COPY INTO), Silver (cleansed, deduplicated, schema-enforced Delta tables), and Gold (aggregated, business-ready views and tables) layers. The layering is deterministic based on parsed source logic — not manual mapping.
Yes. SAS2PY produces column-level STTM (Source-to-Target Mapping) tables and publishes them to Unity Catalog via the Lineage API. This includes data classification tags, attribute-based access policies, and data contract definitions — providing full governance from day one of the modernization.
Yes. SAS PROC LOGISTIC, PROC GLM, PROC MIXED, and PROC MODEL are converted to equivalent Python (scikit-learn / statsmodels) with MLflow experiment tracking, model registry, and Feature Store integration. Model serving endpoints and AutoML baselines are generated automatically.
Legacy job schedulers (Control-M, Autosys, SAS batch flows) are converted to Databricks Workflows with multi-task DAG dependencies, cluster policies, retry logic, and cron-based scheduling. Orchestration logic is preserved, not approximated.
Yes. Streaming and batch ETL patterns are converted to DLT pipelines with declarative @dlt.table and @dlt.view definitions, APPLY CHANGES for CDC, data quality EXPECT constraints, and enhanced autoscaling. DLT is the recommended target for continuous ingestion workloads.
Yes. SAS2PY supports full on-premise and air-gapped deployment. Source code, lineage data, and metadata never leave your network. Output artifacts are packaged as Databricks Asset Bundles (DABs) for secure CI/CD deployment into your Databricks workspace.
SAS2PY generates row-level and aggregate-level data comparison reports between legacy system output and Databricks-produced output. Validation includes row counts, column checksums, business rule assertions, and statistical parity proofs — producing audit-ready evidence for sign-off.
As a Databricks Technology Partner, we'll run a technical deep-dive on your specific source — SAS, or ODI. We'll show you parsed lineage, PySpark output, and Unity Catalog registration from code.