Modernize IBM DataStage Workloads Across Cloud Data Platforms

SAS2PY automates the end-to-end migration of legacy IBM DataStage environments— transforming job designs, workflows, and data pipelines 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 metadata, accelerates migration timelines, and provides full visibility from original IBM DataStage jobs to optimized modern outputs—enabling a seamless, secure, and verifiable modernization of your data engineering infrastructure.



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Validation & Testing for IBM DataStage

  • Leverage advanced automation and optional Generative AI to analyze, validate, and optimize the migration of legacy IBM DataStage ETL pipelines 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 DataStage output and the converted target platform (e.g., Snowflake, Databricks, BigQuery, etc.).

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

  • Error Handling & Remediation: Detect and resolve syntax issues, data type mismatches, missing references, and logic translation gaps during the validation phase—prior to production deployment.

  • Partitioned Testing & Lineage Checks: Validate subsets of transformed data (by date, region, etc.) and trace end-to-end lineage across all stages of the converted workflow.

  • Optional AI Assistance (Merlin AI): Use built-in AI tools to debug issues, optimize logic blocks, and gain insight into how transformations were applied across any modern execution environment.

SAS2PY ensures your IBM DataStage migration is not only fast—but also functionally accurate, fully traceable, and ready for production at scale.


Frequently Asked Questions

What is SAS2PY, and how does it simplify IBM DataStage migration?

SAS2PY automates the conversion of legacy IBM DataStage jobs, flows, and ETL logic into Python, SQL, and modern cloud-native pipelines. It replaces months of manual re-engineering with a parser-driven, traceable process.

You can migrate up to 100,000 lines of DataStage ETL logic in under 10 minutes, cutting project timelines by up to 90% compared to manual refactoring.

Absolutely. SAS2PY scales across millions of lines of DataStage logic—including shared containers, sequences, parallel jobs, and nested transformations—while preserving control flow and dependencies.

We use row-by-row and aggregate-level validation, including schema checks and output comparisons, to ensure 100% alignment between your original DataStage outputs and the modernized environment.

Yes. By retiring DataStage and transitioning to open-source and cloud-native platforms, customers typically save 50–75% on software, infrastructure, and support.

SAS2PY performs schema mapping, metadata comparison, row-level output matching, and regression tests to ensure all DataStage outputs are faithfully reproduced in the target environment.

Manual migrations are slow, error-prone, and costly. SAS2PY offers automation, auditability, and full traceability—reducing both risk and cost while ensuring consistency across jobs.

Migrated logic can be deployed into Airflow, DBT, Databricks, Snowflake, or any modern pipeline orchestration tool using Python modules, SQL scripts, or parameterized notebooks.