Oracle to Databricks Migration Accelerator | KPI Partners

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5-1-Automated Migration_& Validation

Automated Migration & Validation

Convert complex Oracle PL/SQL stored procedures into optimized PySpark notebooks with minimal manual effort, preserving business logic through structured parsing, standardized output, and review-ready validation.

5-2-Logic-Aware_Conversion

Logic-Aware Conversion

Decompose complex PL/SQL logic, including cursors, loops, and conditional flows, into an intermediary representation before generating clean, Pythonic PySpark code for Databricks.

5-3-Integrated Code &_Data Migration

Integrated Code & Data Migration

Migrate schema and historical data alongside code conversion to enable functional validation directly on Databricks, without separate ingestion or replication tooling.

5-4-Automated Assessment_& Review

Automated Assessment & Review

Scan Oracle objects to assess complexity and flag low-confidence conversion areas with AI-generated review comments, focusing human effort where it matters most.

Reduced Manual Effort

Automates the parsing and conversion of complex Oracle PL/SQL stored procedures into PySpark notebooks.

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Faster Time-to-Validation

Accelerates migration timelines by converting logic and migrating schema and historical data together.

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High Logic Fidelity

Preserves transformation behavior through deep Oracle grammar parsing and an intermediary representation layer, minimizing rework.

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Maintainable, Cloud-Native Output

Produces clean, consistent, and Pythonic PySpark code aligned with Databricks best practices.

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Scalable Across Large Estates

Handles high volumes of Oracle stored procedures with predictable delivery and controlled execution effort.

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