Modernize Alteryx Workloads Across Cloud Data Platforms

SAS2PY automates the end-to-end migration of legacy Alteryx workflows— converting analytic apps, macros, workflows, and transformation logic into scalable, cloud-native architectures optimized 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 business logic and metadata, accelerates migration timelines, and provides full visibility from original Alteryx workflows to optimized modern outputs—enabling a seamless, secure, and verifiable modernization of your enterprise data infrastructure.



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Validation & Testing for Alteryx

  • Leverage advanced automation and optional Generative AI to analyze, validate, and optimize the migration of legacy Alteryx workflows and transformation logic 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 Alteryx output and the converted target platform (e.g., Snowflake, Databricks, BigQuery, etc.).

  • Regression Testing: Perform side-by-side output comparisons between the original Alteryx workflows and the migrated versions to ensure consistency in business logic and data transformation outcomes.

  • Error Handling & Remediation: Detect and resolve transformation errors, expression mismatches, tool configuration gaps, and logic inconsistencies during validation—before production deployment.

  • Partitioned Testing & Lineage Checks: Validate transformed data subsets (by date, region, etc.) and trace lineage across all stages of the migrated pipeline for auditability and compliance.

  • Optional AI Assistance (Merlin AI): Use built-in AI tools to identify anomalies, suggest query optimizations, and explain translation logic across Snowflake, Databricks, BigQuery, and other cloud platforms.

SAS2PY ensures your Alteryx migration is not only fast—but also functionally accurate, fully auditable, and production-ready at enterprise scale.


Frequently Asked Questions

What is SAS2PY, and how does it simplify Alteryx migration?

SAS2PY automates the conversion of legacy Alteryx workflows, analytic apps, macros, and transformation logic into Python, SQL, and modern cloud-native pipelines. It replaces months of manual re-engineering with a parser-driven, auditable process.

You can migrate up to 100,000 lines of Alteryx logic in under 10 minutes, reducing migration timelines by up to 90% compared to manual rebuilding.

Absolutely. SAS2PY scales across thousands of Alteryx workflows—including nested macros, reusable components, analytic apps, and parameterized flows—while preserving all dependencies.

We use row-by-row and aggregate-level validation, including schema mapping and output comparisons, to ensure 100% accuracy between your original Alteryx workflows and the converted results.

Yes. By transitioning from Alteryx to open-source and cloud-native platforms, organizations typically save 50–75% on software licensing, infrastructure, and support costs.

SAS2PY performs schema matching, metadata comparison, column-level validations, and full regression tests to ensure that Alteryx transformations are fully reproduced in the modern environment.