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Hard sources — not covered by free tools
Also parsed — certify what free tools miss
Runtimes
After migration
MigryX converts SAS, Talend, Qlik, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, COBOL, and SQL dialects directly to Anaconda-managed Python — conda environments, Jupyter Notebooks, pandas/NumPy pipelines, scikit-learn workflows — with 95%+ parsing accuracy and column-level lineage. The world's most popular data science platform.
Anaconda Targets
Every modernization generates production-ready Anaconda artifacts — conda environments with pinned dependencies, Jupyter Notebooks, pandas/NumPy data pipelines, scikit-learn ML workflows, and SQLAlchemy database integration.
Fully reproducible conda environments with environment.yml — pinned package versions, channel configurations, and cross-platform compatibility for consistent deployments.
Interactive Jupyter Notebooks (.ipynb) with documented code cells, markdown explanations, inline visualizations, and parameterized execution for data exploration and reporting.
Idiomatic pandas DataFrames — read/write, merge, groupby, pivot, window functions, and method chaining for data manipulation, transformation, and analysis workflows.
NumPy arrays and vectorized operations — statistical computations, linear algebra, matrix operations, and broadcasting for high-performance numerical computing.
ML pipelines using scikit-learn — preprocessing, feature engineering, model training, cross-validation, and prediction pipelines replacing SAS Enterprise Miner and PROC steps.
Database connectivity via SQLAlchemy — connection strings, ORM models, parameterized queries, and connection pooling replacing legacy ODBC/JDBC configurations.
Publication-quality visualizations — charts, plots, dashboards, and statistical graphics replacing SAS ODS, PROC GPLOT, and legacy reporting output.
Custom conda packages for shared libraries and utilities — conda-build recipes, private channel distribution, and dependency management for enterprise teams.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Anaconda-native Python code with conda environment specifications.
Automate SAS Base, Macro, PROC SQL, and IML conversion to pandas DataFrames, NumPy arrays, and scikit-learn pipelines within conda environments. Full macro expansion, DATA step logic, FORMAT/INFORMAT handling, and PROC translation.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to pandas ETL pipelines and Jupyter Notebooks with conda-managed dependencies and full component-level lineage.
Parse Qlik Sense .qvf apps, QlikView .qvw documents, and .qvs includes — load scripts, ApplyMap, JOIN/RESIDENT, and QVD stores — converted to pandas ETL pipelines and Jupyter Notebooks with conda-managed dependencies and full script-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to pandas pipelines and Jupyter Notebooks — tool-by-tool translation with conda environments, full lineage preservation, and parameterized execution.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to pandas ETL pipelines within reproducible conda environments — transformer logic fully preserved with NumPy vectorization.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to pandas DataFrames and SQLAlchemy with conda-managed catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to pandas pipelines and SQLAlchemy with conda environments and full column-level lineage.
Parse SQL Server Integration Services .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to pandas pipelines and Jupyter Notebooks with conda environments.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → window function rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to pandas and SQLAlchemy within conda environments.
Modernize Oracle PL/SQL stored procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT/FORALL — targeting pandas and SQLAlchemy within conda environments.
Parse COBOL programs with their copybooks — packed decimal, implied decimals, REDEFINES overlays, and OCCURS DEPENDING ON — to Anaconda-managed Python with pandas and NumPy. DB2 embedded SQL becomes SQLAlchemy queries, and JCL job steps become orchestrated Python tasks with condition codes preserved.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica directly to pandas read_sql and SQLAlchemy — with 500+ function mappings and dialect-aware query rewriting.
Modernize SAS DataFlux dfPower Studio jobs, DMS Data Jobs, and Real-time Services — standardize/parse/match/validate schemes — to pandas data quality pipelines with great-expectations integration in conda environments.
How It Works
The same proven methodology applies to every source — SAS, Talend, Qlik, Alteryx, DataStage, Informatica, or ODI — all landing on Anaconda-managed Python.
Upload source artifacts — SAS scripts, Talend exports, Qlik .qvs/.qvf, DataStage XML, .dtsx packages — into MigryX.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage maps.
Parser-driven conversion to pandas pipelines, Jupyter Notebooks, scikit-learn workflows, and conda environments — with full documentation.
Row-level and aggregate data matching between legacy and Anaconda outputs — audit-ready evidence for sign-off.
Publish lineage, STTM, and data contracts to your catalog. MigryX AI surfaces risk and recommends optimization paths.
Platform Capabilities
Every MigryX modernization is engineered for the full Anaconda ecosystem — conda environment management, the PyData stack (pandas, NumPy, scikit-learn, Matplotlib), Jupyter Notebooks, and enterprise-grade reproducibility.
Purpose-built for each source language. SAS macro expansion, DataStage XML, Talend .item files, Qlik .qvs/.qvf, SSIS .dtsx, COBOL copybooks — full fidelity, deterministic output, no approximation.
Every modernization ships with environment.yml — pinned dependency versions, channel configs, cross-platform builds, and conda-lock files for 100% reproducible execution environments.
Interactive notebooks with documented cells, inline visualizations, parameterized execution via Papermill, and nbconvert export to HTML/PDF for stakeholder reporting.
Source-to-target column mappings, STTM tables, and data contracts — full lineage from legacy source through pandas operations to final output.
AI analyzes parsed metadata to recommend pandas optimizations, vectorization strategies, and conda package selections. Surfaces modernization risk and complexity scoring.
Full deployment behind your firewall with CI/CD packaging. Source code and lineage never leave your network. SOX, GDPR, BCBS 239 ready. Anaconda Enterprise compatible.
Why MigryX
Generic ETL scanners approximate lineage. MigryX parses it exactly — every macro, every column, every dialect — then lands it natively on Anaconda-managed Python.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage | ✓ | ~ |
| Conda environment generation (environment.yml) | ✓ | ✗ |
| Jupyter Notebook output with documentation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| COBOL copybook fidelity (packed decimal, REDEFINES, OCCURS DEPENDING ON) | ✓ | ✗ |
| Parser-driven risk analysis & pandas optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & catalog registration | ✓ | ~ |
| scikit-learn pipeline generation (SAS EM replacement) | ✓ | ✗ |
| Reproducible conda-lock builds | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported