Karsient CodeShift
Transform Legacy Code. Accelerate Modernization.
CodeShift transforms legacy code and data workloads into modern, production-ready implementations — converting SQL, stored procedures, and ETL jobs into Databricks-native code while preserving business logic and transformation intent.
Targeted Challenges
Critical Problems CodeShift Solves
Manual code conversion is slow and inconsistent across a large team
Legacy stored procedures and ETL jobs don't map 1:1 to modern equivalents
Limited documentation increases the risk of losing business logic in translation
Inconsistent legacy coding patterns make automated conversion harder to trust
No structured workflow for human review of AI-generated conversions
Under The Hood
How CodeShift converts legacy workloads
CodeShift combines a deterministic, dialect-specific rules engine with AI-assisted pattern generation — so common constructs convert instantly, and only genuinely ambiguous logic goes to a model.
Applies dialect-specific transformation rules for direct, high-confidence conversions
Uses AI to generate idiomatic PySpark/SQL for logic that doesn't map 1:1
Cross-checks every conversion against the business rules ShiftIQ identified upstream
Assigns a confidence score per object and routes anything below threshold to review
Comprehensive Feature Matrix
AI-assisted code conversion
AI-driven translation of legacy code into modern, idiomatic Databricks implementations.
Legacy SQL modernization
Converts legacy SQL dialects and functions into Databricks SQL.
ETL transformation
Re-platforms ETL logic into Databricks-native pipelines and workflows.
Talend-to-Databricks conversion
Converts Talend jobs into PySpark and Delta Live Tables pipelines.
Informatica-to-Databricks conversion
Converts Informatica mappings and workflows into Databricks-native equivalents.
Vertica-to-Databricks conversion
Converts Vertica SQL and projection logic into Delta Lake-optimized implementations.
Stored-procedure modernization
Converts stored procedures into PySpark functions or SQL-based equivalents.
SQL-to-PySpark transformation
Transforms SQL-centric logic into distributed PySpark where it improves performance.
Source-to-target mapping
Produces a traceable map from every source object to its converted target implementation.
Business-rule preservation
Validates that business logic identified upstream is preserved through conversion.
Modern coding pattern generation
Generates code that follows current Databricks engineering best practice, not literal translation.
Automated migration documentation
Documents every conversion automatically, keeping the migration audit-ready.
Human review & approval workflows
Routes lower-confidence conversions to an engineer before anything ships to production.
Supported Technologies & Platforms
Integrates directly into your existing cloud infrastructure, code repositories, and lakehouse platforms.
TALENDVERTICA
ORACLE
INFORMATICA
DATABRICKS
DELTALAKE
PYTHON
SNOWFLAKE
FABRIC
AZURE
AWS
GOOGLECLOUDFrequently Asked Questions
Technical Questions About CodeShift
No — CodeShift maximizes safe automation while explicitly flagging anything it isn't confident about, rather than silently producing incorrect code.
Deploy CodeShift in your environment
Talk with a technical solutions engineer to scope an assessment, evaluate sample logic, or schedule a sandbox walkthrough.