Modernize Oracle ODI Workloads Across Cloud Data Platforms

SAS2PY automates the end-to-end migration of legacy Oracle Data Integrator (ODI) environments— converting mappings, knowledge modules, and ELT workflows 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 Oracle ODI flows to optimized modern outputs—enabling a seamless, secure, and verifiable modernization of your data integration infrastructure.



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Validation & Testing for ODI

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

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

  • Error Handling & Remediation: Detect and resolve syntax errors, data type mismatches, missing lookups, and logic gaps 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.

  • Optional AI Assistance (Merlin AI): Use built-in AI tools to identify logic anomalies, suggest optimizations, and explain transformation mappings across target platforms.

SAS2PY ensures your Oracle ODI 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 Oracle ODI migration?

SAS2PY automates the conversion of legacy Oracle Data Integrator (ODI) mappings, procedures, and ELT 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 Oracle ODI logic in under 10 minutes, reducing migration timelines by up to 90% compared to manual refactoring.

Absolutely. SAS2PY scales across millions of lines of ODI logic—including mappings, reusable procedures, knowledge modules, and complex ELT workflows—while preserving dependencies and control logic.

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

Yes. By transitioning from Oracle ODI 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 Oracle ODI transformations are fully reproduced in the modern environment.

Manual migrations are slow, error-prone, and hard to scale. SAS2PY offers parser-based automation, audit trails, and guaranteed output consistency—making your Oracle ODI modernization effort faster, safer, and more cost-effective.

Migrated logic from Oracle ODI can be deployed as Python modules, SQL scripts, or notebooks directly into modern orchestrators such as Airflow, DBT, Databricks, or Snowflake pipelines.