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MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, COBOL, and SQL dialects to Microsoft Azure — Spark Notebooks, Data Warehouse, Lakehouse, Data Factory, Real-Time Analytics, and Power BI Dataflows — with +95% parsing accuracy and column-level lineage.
Fabric Targets
Every modernization generates production-ready Fabric artifacts — leveraging Spark Notebooks, Data Warehouse, Lakehouse, Data Factory, Real-Time Analytics, Power BI Dataflows, OneLake, and Fabric AI.
PySpark jobs on Fabric Spark compute, seamless integration with OneLake — legacy ETL logic converted to Spark DataFrames running natively inside Fabric compute pools.
T-SQL based warehouse with serverless billing and cross-database queries — legacy SQL workloads modernized to Fabric's fully managed warehouse experience.
Delta Lake tables managed via OneLake with SQL analytics endpoint — combining the flexibility of a data lake with the performance of a data warehouse.
Pipelines and dataflows for orchestration, replacing legacy schedulers — scheduled ETL converted to Fabric Data Factory pipelines with parameterized execution.
KQL queries and event streams for streaming analytics — legacy CDC and near-real-time patterns converted to Fabric Real-Time Analytics with Kusto Query Language.
Direct data prep for business intelligence dashboards — legacy reporting and data preparation logic modernized to Power BI Dataflows Gen2 for self-service analytics.
Unified storage layer with ACID compliance across all Fabric experiences — legacy data lake tables modernized to OneLake with open Delta Lake format and governance.
ML models and AI integration in Fabric notebooks and semantic models — SAS analytical and scoring models converted to Fabric AI with automatic feature engineering.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Fabric-native code — Spark Notebooks, Warehouse SQL, or Data Factory pipelines.
Automate SAS Base, Macro, PROC SQL, and IML conversion to Fabric Spark Notebooks and Warehouse SQL. DATA step logic, FORMAT/INFORMAT handling, PROC SORT/MEANS/FREQ, and PROC MODEL translated to Fabric AI.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to Fabric Spark Notebooks and Data Factory pipelines with full component-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to Fabric Spark Notebooks and Warehouse SQL — tool-by-tool translation with full lineage preservation and UDF output for reuse.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to Fabric Spark Notebooks and Data Factory pipelines — transformer logic translated to Warehouse SQL pushdown.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to Fabric Spark Notebooks with Data Factory orchestration and OneLake catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to Fabric Data Factory pipelines and Spark Notebooks with full column-level lineage in OneLake catalog.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to Fabric Spark Notebooks and Data Factory pipeline orchestration with Lakehouse ingestion.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to Fabric Data Warehouse SQL and Lakehouse Delta tables.
Modernize Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY rewriting, BULK COLLECT batching — to Fabric Warehouse SQL stored procedures and Spark Notebooks.
Parse COBOL programs with their copybooks — packed decimal, implied decimals, REDEFINES overlays, and OCCURS DEPENDING ON — to Fabric Spark Notebooks and Lakehouse tables. DB2 embedded SQL becomes Warehouse SQL, and JCL job steps become Data Factory pipeline activities with condition codes preserved.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to Fabric Warehouse SQL — 500+ function mappings, window function normalization, and Delta Lake table support.
Modernize SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to Fabric Spark Notebooks and data quality constraints with Fabric AI anomaly detection.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing natively on Azure Fabric.
Upload source artifacts — SAS scripts, Talend exports, DataStage XML, .dtsx packages — into MigryX for parsing.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage — with Fabric-readiness scoring.
Convert to Fabric Spark Notebooks, Data Warehouse SQL, Lakehouse tables, and Data Factory pipelines — with auto documentation.
Row-level and aggregate data matching between legacy and Fabric outputs — using Fabric-native comparison queries for audit-ready sign-off.
Publish lineage, STTM, and data contracts to OneLake catalog. Merlin AI surfaces risk and recommends compute pool sizing.
Platform Capabilities
Every MigryX modernization leverages the full Fabric platform — Spark Notebooks, Data Warehouse, Lakehouse, Data Factory, Real-Time Analytics, Power BI Dataflows, and Fabric AI.
Purpose-built for each source language — SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx, COBOL copybooks — full fidelity, no approximation, deterministic output.
Legacy ETL logic converted to Fabric Spark Notebooks running on Fabric Spark compute pools — pushdown execution with seamless OneLake integration. UDFs and Stored Procedures generated automatically.
Scheduled ETL converted to Fabric Data Factory pipelines replacing legacy schedulers — parameterized execution, dataflows, and orchestration with native Fabric triggers and monitoring.
Source-to-target column mappings published to OneLake catalog for governance — data classification, lineage visualization, and compliance tracking across all Fabric experiences.
AI analyzes parsed metadata for optimization, models land in Fabric AI. SAS analytical models converted to Fabric notebooks with Copilot-assisted feature engineering and semantic model integration.
Full deployment behind your firewall. Source code and lineage never leave your network. Fabric workspace promotion patterns for dev → test → prod. SOX, GDPR, BCBS 239 ready.
Why MigryX
Generic ETL scanners approximate lineage. MigryX parses it exactly — every macro, every column, every dialect — then lands it natively on Azure Fabric with full Spark Notebook and Data Warehouse support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to OneLake catalog | ✓ | ~ |
| Native Fabric Spark Notebook output generation | ✓ | ✗ |
| Data Warehouse SQL & Lakehouse Delta Table generation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| COBOL copybook fidelity (packed decimal, REDEFINES, OCCURS DEPENDING ON) | ✓ | ✗ |
| Fabric AI/Copilot integration for analytical models | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & OneLake catalog registration | ✓ | ~ |
| Fabric Data Factory pipeline generation | ✓ | ✗ |
| OneLake Delta Lake table optimization recommendations | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported
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