MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects directly to Polars — LazyFrame pipelines, Polars expressions, Polars SQL, Arrow IPC & Parquet output — with +95% parsing accuracy and column-level lineage. The fastest DataFrame library, powered by Apache Arrow and Rust.
Polars Targets
Every migration generates production-ready Polars artifacts — LazyFrame pipelines with automatic query optimization, Polars expressions, Arrow-native output, and streaming execution for terabyte-scale data.
Lazy evaluation pipelines with predicate pushdown, projection pruning, and automatic query optimization — up to 50x faster than eager pandas execution.
Expressive column-level operations using Polars' expression API — .filter(), .with_columns(), .group_by().agg() — fully type-safe, composable, and parallelized.
SQL interface on top of Polars DataFrames — register DataFrames as tables, execute SQL queries, and mix SQL with expression API seamlessly.
Native Apache Arrow memory format with zero-copy reads/writes. Output to Parquet, Arrow IPC, CSV, JSON, Delta Lake, or any Arrow-compatible format.
Process datasets larger than memory using Polars' streaming execution engine — chunked lazy evaluation for terabyte-scale data on a single machine.
Extend Polars with custom Rust-based plugins for domain-specific operations — compiled to native code, integrated via the expression plugin API.
Automated data quality profiling — null counts, cardinality, distributions, schema drift detection — generated alongside every migration output.
Lineage and STTM mappings published to data catalogs (Unity Catalog, DataHub, OpenMetadata) — full governance for Polars-based pipelines.
Migration Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Polars-native code.
Automate SAS Base, Macro, PROC SQL, and IML conversion to Polars LazyFrame pipelines and Polars SQL. Full macro expansion, DATA step logic, FORMAT/INFORMAT handling, and PROC SORT/MEANS/FREQ translation.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to Polars LazyFrame pipelines and expressions with full component-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to Polars LazyFrame pipelines and Polars SQL — tool-by-tool translation with full lineage preservation and expression output.
Migrate IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to Polars LazyFrame pipelines and Arrow IPC — transformer logic fully preserved.
Migrate Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to Polars expressions with catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to Polars LazyFrame pipelines and Parquet 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 Polars LazyFrame pipelines and expressions.
Migrate Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → window function rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to Polars SQL and LazyFrame pipelines.
Migrate Oracle PL/SQL stored procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT/FORALL — targeting Polars SQL and expressions.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica directly to Polars SQL — 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 Polars expressions 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 Polars-based pipelines.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing on Polars.
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 Polars LazyFrame pipelines, expressions, Polars SQL, or streaming — with full documentation.
Row-level and aggregate data matching between legacy and Polars 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 Polars ecosystem — LazyFrame query optimization, Apache Arrow memory layout, Rust-powered multi-threaded execution, and catalog-integrated governance.
Purpose-built for each source language. SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx — full fidelity, deterministic output, no approximation.
Polars is built on Apache Arrow — zero-copy memory layout, columnar execution, SIMD vectorization, and interop with any Arrow-compatible engine (DuckDB, Spark, Trino).
Written in Rust with multi-threaded execution. LazyFrame query optimizer pushes down predicates, prunes columns, and parallelizes operations — up to 50x faster than pandas.
Source-to-target column mappings, STTM tables, and data contracts — full lineage from legacy source through Polars expressions to final output.
AI analyzes parsed metadata to recommend LazyFrame optimizations, partition strategies, and streaming boundaries. 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 Polars 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 Polars.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage | ✓ | ~ |
| Native Polars LazyFrame output | ✓ | ✗ |
| Polars expression API generation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Parser-driven risk analysis & Polars optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & catalog registration | ✓ | ~ |
| Arrow IPC & Parquet output generation | ✓ | ~ |
| Streaming engine for larger-than-memory data | ✓ | ✗ |
✓ 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 Polars output from code.