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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, and SQL dialects to DBT — DBT Core models, Jinja macros, tests, snapshots, and exposures — running on Snowflake, Databricks, BigQuery, or Postgres — with 95%+ parsing accuracy and column-level lineage.
dbt Targets
Every modernization generates production-ready DBT artifacts — leveraging DBT Core models, Jinja macros, schema tests, snapshots, seeds, exposures, sources, and packages for a complete DBT project.
SELECT-based transformations with ref() and source() for dependency management — staging, intermediate, and mart layers following DBT best practices.
Reusable parameterized SQL blocks replacing legacy macro systems — cross-database compatible Jinja templates with variable injection and control flow.
Schema tests (unique, not_null, relationships) plus custom data quality tests — auto-generated from legacy validation logic with full coverage reporting.
SCD Type-2 change tracking via snapshot strategy (timestamp or check) — legacy slowly changing dimension logic converted to dbt-native snapshot configurations.
Static lookup data as CSV-in-repo for version-controlled reference tables — legacy hardcoded mappings and lookup tables converted to managed seed files.
Downstream dependency documentation for BI dashboards and reports — legacy report metadata converted to exposure definitions with owner and maturity tracking.
Source freshness monitoring, contracts, and external table definitions — legacy source connections converted to schema.yml source blocks with freshness SLAs.
Reusable cross-project dependencies and shared macro libraries — legacy shared code converted to installable DBT packages with semantic versioning.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, dbt-native code — models, Jinja macros, tests, and snapshots.
Automate SAS Base, Macro, PROC SQL, and IML conversion to DBT models and Jinja macros. DATA step logic, FORMAT/INFORMAT handling, PROC SORT/MEANS/FREQ translated to DBT SQL with ref() dependency graphs.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to DBT models with Jinja macros and schema.yml tests 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 DBT models with Jinja macros and schema.yml tests with full script-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to DBT models and Jinja macros — tool-by-tool translation with full lineage preservation and DBT test auto-generation.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to DBT models and snapshots — transformer logic translated to Jinja-templated SQL with ref() graphs.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to DBT models with schema.yml tests and source freshness monitoring.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to DBT models, Jinja macros, and snapshots with full column-level lineage in DBT docs.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to DBT models with Jinja macros and schema.yml test definitions.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to DBT models with incremental materialization strategies.
Modernize Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY rewriting, BULK COLLECT conversion — to DBT models, Jinja macros, and custom test definitions.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to DBT models — 500+ function mappings, window function normalization, and cross-database Jinja macros.
Modernize SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to DBT models with custom data quality tests and schema.yml 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 DBT 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 dbt-readiness scoring.
Convert to DBT SQL models, Jinja macros, schema.yml tests, snapshot configurations, and source definitions — with auto documentation and DBT best-practice patterns.
Row-level and aggregate data matching between legacy and DBT outputs — using DBT test assertions and custom data quality checks for audit-ready sign-off.
Publish lineage, STTM, and data contracts to DBT docs. MigryX AI surfaces risk and recommends materialization strategies, model granularity, and test coverage.
Platform Capabilities
Every MigryX modernization leverages the full DBT ecosystem — models, Jinja macros, tests, snapshots, seeds, exposures, sources, and packages for a complete transformation layer.
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 DBT models, tests, macros, and snapshots in a production-ready DBT project structure — with dbt_project.yml, schema.yml, and packages.yml generated automatically.
Output runs on Snowflake, Databricks, BigQuery, Postgres, or Redshift via DBT adapters — cross-database Jinja macros ensure portability without rewriting transformation logic.
Source-to-target column mappings auto-generated as DBT docs with ref() graph visualization — full DAG exploration, column-level lineage, and impact analysis in the DBT docs site.
AI analyzes parsed metadata to recommend materializations (table/view/incremental), model layering (staging/intermediate/mart), and test coverage — with automatic schema.yml generation.
Full deployment behind your firewall. Source code and lineage never leave your network. DBT project promotion patterns for dev → test → prod. SOX, GDPR, BCBS 239 ready.
Measurable Results
Organizations using MigryX to land on DBT accelerate delivery, eliminate manual rewrite cost, and unlock dbt-native transformation patterns 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.
Generated code your team can read, with validation before go-live.
Deterministic parsers handle 95%+ of typical code out of the box.
Why MigryX
Generic ETL scanners approximate lineage. MigryX parses it exactly — every macro, every column, every dialect — then lands it natively in DBT with full model, test, and snapshot support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to DBT docs | ✓ | ~ |
| Native DBT model generation with ref()/source() | ✓ | ✗ |
| Jinja 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 & DBT docs integration | ✓ | ~ |
| dbt test auto-generation (schema + data tests) | ✓ | ✗ |
| Multi-warehouse adapter support (Snowflake/Databricks/BigQuery) | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported