MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects to production-ready Python — pandas DataFrames, PySpark pipelines, Polars LazyFrames, and Snowpark procedures — with +95% parsing accuracy and column-level lineage.
Python Targets
Every migration generates production-ready Python artifacts across the full ecosystem — pandas DataFrames, PySpark pipelines, Polars LazyFrames, Snowpark procedures, dbt models, Airflow DAGs, and pip-installable packages.
DataFrames, data wrangling, and analytics pipelines — the most widely adopted Python data library, with full NumPy and scikit-learn interop.
Distributed DataFrames and Spark SQL on any cluster — Databricks, EMR, HDInsight, or standalone — for petabyte-scale ETL and analytics.
High-performance Rust-backed DataFrames with LazyFrame query optimization, Apache Arrow memory layout, and streaming execution for terabyte-scale data.
Python APIs for Snowflake compute — DataFrames, stored procedures, and UDFs that execute natively inside Snowflake's elastic warehouse engine.
SQL transformations with Jinja templating — modular, version-controlled data models that run on Snowflake, BigQuery, Databricks, or Redshift.
Interactive analysis and documentation — code, visualizations, and markdown in a single shareable document for exploratory data work and validation.
Python-native pipeline orchestration — task dependencies, scheduling, retries, and monitoring for production data workflows on any infrastructure.
Modular, testable, pip-installable code — proper project structure with pyproject.toml, type hints, unit tests, and CI/CD-ready packaging.
Migration Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable Python — pandas, PySpark, Polars, or Snowpark — with full lineage.
Automate SAS Base, Macro, PROC SQL, and IML conversion to pandas DataFrames, PySpark pipelines, or Polars LazyFrames. 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 PySpark pipelines, pandas scripts, or Airflow DAGs with full component-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to pandas DataFrames and Polars pipelines — tool-by-tool translation with full lineage preservation and Jupyter notebook output.
Migrate IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to PySpark pipelines, pandas scripts, or Airflow DAGs — transformer logic fully preserved.
Migrate Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to PySpark, Snowpark procedures, or dbt models with catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to pandas pipelines, Snowpark procedures, or Airflow DAGs with 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, PySpark jobs, or Airflow DAGs.
Migrate Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → window function rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to PySpark, dbt models, or Snowpark.
Migrate Oracle PL/SQL stored procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT/FORALL — targeting pandas, PySpark, or Snowpark.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to PySpark SQL, dbt models, or Snowpark — with 500+ function mappings and dialect-aware query rewriting.
Migrate SAS DataFlux dfPower Studio jobs, DMS Data Jobs, and Real-time Services — standardize/parse/match/validate schemes — to pandas pipelines with data quality profiling integration.
Before you migrate, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them to your data catalog for Python-based pipelines.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing on production-ready Python.
Upload source artifacts — SAS scripts, Talend exports, 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, PySpark, Polars, Snowpark, dbt, or Airflow — your choice of Python target — with full documentation.
Row-level and aggregate data matching between legacy and Python outputs — audit-ready evidence for sign-off.
Publish lineage, STTM, and data contracts to your catalog. Merlin AI surfaces risk and recommends optimization paths.
Platform Capabilities
Every MigryX migration is engineered for the full Python ecosystem — pandas, PySpark, Polars, Snowpark, dbt, Airflow — with catalog-integrated governance and production-grade packaging.
Purpose-built for each source language. SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx — full fidelity, deterministic output, no approximation.
Choose your target — pandas, PySpark, Polars, Snowpark, or dbt — and MigryX generates idiomatic, production-ready code for each framework with full API coverage.
Generated Python code follows best practices — type hints, proper project structure, pyproject.toml, unit tests, and CI/CD-ready packaging with pip-installable modules.
Source-to-target column mappings, STTM tables, and data contracts — full lineage from legacy source through Python pipelines to final output.
AI analyzes parsed metadata to recommend Python framework selection, optimization strategies, and pipeline architecture. Surfaces migration 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.
Measurable Results
Organizations using MigryX to land on Python accelerate delivery, reduce risk, and eliminate manual rewrite costs across every modernization program.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite work.
Complete visibility into dependencies prevents production incidents and migration-related data defects.
Reduced consulting spend, accelerated time-to-value, and eliminated rework deliver 60%+ cost savings.
Deterministic custom parsers deliver +95% accuracy out of the box. Optional AI augmentation pushes accuracy up to 99%.
Why MigryX
Generic ETL scanners approximate lineage. MigryX parses it exactly — every macro, every column, every dialect — then lands it natively on Python.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage | ✓ | ~ |
| Multi-target Python output (pandas, PySpark, Polars, Snowpark) | ✓ | ✗ |
| Production-grade Python packaging (pyproject.toml, tests, CI/CD) | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Parser-driven risk analysis & Python optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & catalog registration | ✓ | ~ |
| Airflow DAG & dbt model generation | ✓ | ~ |
| Jupyter notebook & interactive documentation output | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported
Schedule a technical deep-dive on your specific source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI. We'll show you parsed lineage and Python output from code.