SQL Server-to-Databricks Migration Accelerator | KPI Partners

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4-1-GenAI-Driven_T-SQL Conversion

GenAI-Driven T-SQL Conversion

Convert complex procedural T-SQL logic, including cursors, temp tables, and loops, into modern, distributed PySpark DataFrame operations using context-aware GenAI interpretation.

4-2-Automated Data_Hydration

Automated Data Hydration

Load schema and historical data directly from SQL Server into Databricks Delta tables, ensuring the target environment is populated with real data for immediate testing and validation.

4-3-Intelligent Object_Discovery

Intelligent Object Discovery

Automatically scan SQL Server system catalogs to identify and categorize tables, views, and stored procedures, providing instant visibility into migration scope.

4-4-Interactive Code_Review

Interactive Code Review

Review side-by-side T-SQL and PySpark output with embedded AI comments that highlight potential logic gaps and areas requiring human validation.

Scalable, Cloud-Native Processing

Unlock scalability by moving from monolithic, scale-up SQL Server architectures to distributed Spark processing, without manual rewrites.

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Lower Modernization Effort

Reduce modernization of friction by automating code translation, data movement, and validation within a single workflow.

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Faster Proof-of-Value

Accelerate PoCs by generating deployable PySpark notebooks and loading production-like data in minutes.

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Bridging the Skills Gap

Help SQL-centric teams transition to Databricks using auto-generated, Pythonic Spark code that doubles as a learning accelerator.

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Predictable Delivery at Scale

Simplify planning by efficiently handling large volumes of SQL Server objects, enabling controlled execution, and predictable timelines.

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