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Plan your migration →
MigryX converts SAS, Talend, Qlik, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects to SQLMesh — SQL and Python models, macros, audits, incremental kinds, and virtual data environments — running on Snowflake, Databricks, BigQuery, or Postgres — with +95% parsing accuracy and column-level lineage.
SQLMesh Targets
Every modernization generates a production-ready SQLMesh project — SQL and Python models, macros, audits, incremental kinds, seeds, unit tests, and virtual data environments.
SQL and Python MODEL definitions with explicit dependencies — FULL, VIEW, and INCREMENTAL_BY_TIME_RANGE kinds following SQLMesh project layout.
Reusable @macro blocks replacing legacy macro systems — Python or SQL macros with typed arguments and environment-aware evaluation.
UNIQUE, NOT_NULL, and custom audits — auto-generated from legacy validation logic and run as part of sqlmesh plan.
INCREMENTAL_BY_TIME_RANGE and INCREMENTAL_BY_UNIQUE_KEY kinds — legacy SCD and delta loads become first-class incremental models.
Static lookup data as CSV-in-repo for version-controlled reference tables — legacy hardcoded mappings and lookup tables converted to managed seed files.
Dev and prod as virtual data environments — plan diffs show exactly what will change before apply, without cloning physical tables.
External models and freshness checks for warehouse tables the project does not own — legacy source connections become explicit upstream contracts.
Shared macro libraries and reusable model patterns — legacy shared code converted to SQLMesh macros the rest of the project can call.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, SQLMesh-native code — models, macros, audits, and incremental kinds.
Automate SAS Base, Macro, PROC SQL, and IML conversion to SQLMesh models and macros. DATA step logic, FORMAT/INFORMAT handling, PROC SORT/MEANS/FREQ translated to SQLMesh SQL with model dependency graphs.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to SQLMesh models with macros and audits with 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 SQLMesh models with macros and audits with full script-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to SQLMesh models and macros — tool-by-tool translation with full lineage preservation and audit auto-generation.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to SQLMesh models and incremental kinds — transformer logic translated to SQLMesh SQL with model dependencies.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to SQLMesh models with audits and freshness checks.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to SQLMesh models, macros, and incremental kinds with full column-level lineage.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to SQLMesh models with macros and audits.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to SQLMesh models with incremental kinds.
Modernize Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY rewriting, BULK COLLECT conversion — to SQLMesh models, macros, and custom audits.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to SQLMesh models — 500+ function mappings, window function normalization, and cross-database macros.
Modernize SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to SQLMesh models with custom audits and validation constraints.
How It Works
The same proven methodology applies to every source — SAS, Talend, Qlik, Alteryx, DataStage, Informatica, or ODI — all landing natively in a production-ready SQLMesh project.
Upload source artifacts — SAS scripts, Talend exports, Qlik .qvs/.qvf, DataStage XML, .dtsx packages — into MigryX for parsing.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage — with SQLMesh-readiness scoring.
Convert to SQLMesh SQL and Python models, macros, audits, incremental kinds, and external models — with auto documentation and plan/apply patterns.
Row-level and aggregate data matching between legacy and SQLMesh outputs — using SQLMesh audits and custom data quality checks for audit-ready sign-off.
Publish lineage, STTM, and data contracts into the SQLMesh plan. Merlin AI surfaces risk and recommends model kinds, incrementality, and audit coverage.
Platform Capabilities
Every MigryX modernization leverages the full SQLMesh project — models, macros, audits, incremental kinds, seeds, unit tests, and virtual data environments.
Purpose-built for each source language — SAS macro expansion, DataStage XML, Talend .item files, Qlik .qvs/.qvf, SSIS .dtsx — full fidelity, no approximation, deterministic output.
Legacy ETL logic converted to SQLMesh models, audits, and macros in a production-ready project — with config.yaml, model files, and audits generated automatically.
Output runs on Snowflake, Databricks, BigQuery, Postgres, or Redshift via SQLMesh engines — cross-database macros ensure portability without rewriting transformation logic.
Source-to-target column mappings auto-generated in the SQLMesh plan DAG — virtual environment diffs, column-level lineage, and impact analysis before apply.
AI analyzes parsed metadata to recommend model kinds (FULL/VIEW/INCREMENTAL_BY_TIME_RANGE), layering, and audit coverage — with automatic audit generation.
Full deployment behind your firewall. Source code and lineage never leave your network. SQLMesh virtual-environment promotion for dev → test → prod. SOX, GDPR, BCBS 239 ready.
Measurable Results
Organizations using MigryX to land on SQLMesh accelerate delivery, eliminate manual rewrite cost, and unlock virtual environments from day one.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite.
Complete dependency visibility prevents production incidents and modernization-related data defects.
Automated conversion, 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 in SQLMesh with full model, audit, and incremental support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage in the SQLMesh plan | ✓ | ~ |
| Native SQLMesh MODEL generation with dependencies | ✓ | ✗ |
| SQLMesh @macro output from legacy macro systems | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Parser-driven materialization recommendations | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & SQLMesh plan integration | ✓ | ~ |
| SQLMesh audit auto-generation (UNIQUE, NOT_NULL, custom) | ✓ | ✗ |
| Multi-warehouse adapter support (Snowflake/Databricks/BigQuery) | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported