Modernize SAS Workloads Across Cloud Data Platforms

SAS2PY automates the end-to-end migration of legacy SAS environments—including Base SAS, DI Studio, Enterprise Guide, Enterprise Miner, and SAS Viya—into scalable, cloud-native ecosystems designed for performance, flexibility, and long-term sustainability.

Target Platforms and Outputs Include:

  • Cloud Data Warehouses: Snowflake, Google BigQuery, Amazon Redshift, Teradata Vantage, Apache Iceberg, Microsoft Fabric, Cloudera
  • Data Processing Frameworks: Python, PySpark, Snowpark, SQL, and native Databricks notebooks
  • Workflow Orchestration: DBT, Airflow, Git, Google Dataproc, Amazon EMR
  • Execution Capabilities: Visual pipeline execution across Databricks, Snowflake, and other platforms
  • Data Validation & Lineage: Schema mapping, partition-level data checks, metadata comparison, column-level validation, and full audit trails
  • Merlin AI (Optional): Built-in AI assistant for interactive code assistance, query optimization, and debugging—all on-prem or within your own secure cloud

SAS2PY preserves metadata, accelerates migration timelines, and provides full visibility from original SAS code to optimized modern outputs—enabling a seamless, secure, and verifiable modernization of your analytics infrastructure.



See a Demo


Thumb





Validation & Testing for SAS

  • Leverage advanced automation and optional Generative AI to analyze, validate, and optimize the migration of legacy SAS code—including Base SAS, DI Studio, Enterprise Guide, Enterprise Miner, and Viya— into modern platforms like Snowflake, Databricks, BigQuery, Redshift, Microsoft Fabric, and PySpark.

  • Data Validation: Automatically verify data accuracy by comparing row counts, column values, aggregates (sum, average), and schema structures between the original SAS output and the converted target platform (e.g., Snowflake, Databricks, BigQuery, etc.).

  • Regression Testing: Perform side-by-side output comparisons between the SAS source and migrated pipelines to ensure business logic and analytical outcomes remain consistent.

  • Error Handling & Remediation: Detect and resolve syntax errors, data type mismatches, missing dependencies, and transformation logic discrepancies—before code reaches production.

  • Partitioned Testing & Lineage Checks: Validate subsets of data (by date, region, etc.) and trace full data lineage across all stages of the converted pipeline.

  • Optional AI Assistance (Merlin AI): Use built-in AI tools to suggest corrections, generate alternative logic, and explain transformation behavior across any target platform.

SAS2PY ensures your migration is not only fast—but functionally accurate, auditable, and ready for enterprise-scale deployment.


Frequently Asked Questions

What is SAS2PY, and how does it simplify SAS code migration?

SAS2PY automates the conversion of legacy SAS Base, EG, EM and DI Studio code into Python (Pandas or PySpark), SQL, and cloud‐native scripts. It replaces months of manual rewriting with an AI‐driven, traceable process.

You can convert up to 100,000 lines of SAS code in under 10 minutes, cutting timelines by up to 90% compared to manual approaches.

Absolutely. SAS2PY scales across millions of lines of SAS code, including nested macros and complex data step logic, while preserving dependencies.

We use automated row-by-row and hash comparisons, plus aggregate checks, to guarantee 100% parity between original SAS outputs and converted Python scripts.

Yes. By retiring legacy SAS licenses and automating migration, you can save up to 75% on software and support expenses.

Data validation runs at every stage: pre-conversion schema mapping, post-conversion row and column checks, and regression tests to catch any discrepancies.

Manual rewrites are time-consuming and error-prone. SAS2PY delivers consistent, auditable results with full audit trails and minimal human intervention.

Converted scripts are output as Python modules or notebooks and can be deployed via CLI, API or directly into your Airflow, DBT, or CI/CD pipelines.

Our parser fully expands and translates macros into reusable Python functions, preserving parameterization and control logic.

Yes. All conversion and validation processes run on-premise or in your own cloud VPC, so your data never leaves your secure environment.

Absolutely. We generate Python code optimized for Snowflake, Databricks, Redshift, BigQuery, or your cloud of choice.

Our engine flags any syntax or logic mismatches and either auto-resolves them with rule-based reconciliation or surfaces them for rapid developer review.

Yes—custom formats, informats, labels, and metadata are extracted and mapped into Python equivalents or documented for review.

SAS2PY is the only end-to-end, on-premise SAS migration platform with AI-driven optimization, full lineage tracking, and multi-cloud deployment support.