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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 to Snowflake — Snowpark, Dynamic Tables, Streams & Tasks, Snowflake Cortex AI, and Virtual Warehouses — with 95%+ parsing accuracy and column-level lineage.
Snowflake Targets
Every modernization generates production-ready Snowflake artifacts — leveraging Snowpark, Dynamic Tables, Streams & Tasks, Zero-Copy Cloning, Snowflake Cortex, and the Snowflake Data Cloud.
Legacy ETL logic converted to Snowpark Python DataFrames — pushdown computation runs natively inside the Snowflake Virtual Warehouse, no data movement required.
Incremental transformation pipelines rewritten as Snowflake Dynamic Tables — declarative SQL with automatic refresh, lag targets, and full lineage tracking built in.
CDC patterns and scheduled ETL converted to Snowflake Streams (change capture on tables/views/stages) and Tasks (DAG-based orchestration with serverless compute).
Batch and near-real-time data ingestion replatformed to Snowpipe with auto-ingest from S3/Azure/GCS — replacing legacy file-based ETL landing patterns.
SAS analytical and scoring models converted to Snowflake Cortex — LLM functions (COMPLETE, SUMMARIZE, CLASSIFY), ML classification, regression, and anomaly detection inside Snowflake.
Workload-specific Virtual Warehouse sizing recommendations generated per pipeline — separating ETL, reporting, and ad hoc query workloads with auto-suspend/resume.
Legacy environment promotion patterns (dev → test → prod) replaced with Snowflake Zero-Copy Cloning — instant schema and table clones with no storage duplication.
Legacy data lake tables modernized to Snowflake-managed Apache Iceberg Tables — open format storage with Snowflake query performance and governance, on your own cloud storage.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Snowflake-native code — Snowpark, Dynamic Tables, or Snowflake SQL.
Automate SAS Base, Macro, PROC SQL, and IML conversion to Snowpark Python and Snowflake SQL. DATA step logic, FORMAT/INFORMAT handling, PROC SORT/MEANS/FREQ, and PROC MODEL translated to Cortex ML.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to Snowpark Python jobs and Snowflake Tasks DAGs with full component-level lineage.
Parse Qlik Sense .qvf apps, QlikView .qvw documents, and .qvs includes — load scripts, ApplyMap, JOIN/RESIDENT, QVD stores, and Section Access — converted to Snowpark Python jobs and Snowflake Tasks DAGs with full script-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to Snowpark Python and Snowflake SQL — tool-by-tool translation with full lineage preservation and UDTF/UDF output for reuse.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to Snowpark Python and Dynamic Tables — transformer logic translated to Snowflake SQL pushdown.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to Snowpark Python with Tasks orchestration and catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to Snowflake Dynamic Tables and Snowpark with full column-level lineage in Snowflake catalog.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to Snowpark Python pipelines and Task DAG orchestration with Snowpipe ingestion.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → QUALIFY rewriting (Snowflake supports it natively), BTEQ command translation, and PRIMARY INDEX → clustering key advisory.
Modernize Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT → Snowpark batching, and full package dependency resolution.
Parse COBOL programs with their copybooks — packed decimal, implied decimals, REDEFINES overlays, and OCCURS DEPENDING ON — to Snowpark Python. DB2 embedded SQL becomes Snowflake SQL, and JCL job steps become Streams & Tasks with condition codes preserved.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to Snowflake SQL — 500+ function mappings, window function normalization, and semi-structured VARIANT support.
Modernize SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to Snowpark Python UDFs and Snowflake data quality constraints with Cortex anomaly detection.
How It Works
The same proven methodology applies to every source — SAS, Talend, Qlik, Alteryx, DataStage, Informatica, or ODI — all landing natively on Snowflake.
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 Snowflake-readiness scoring.
Parser-driven conversion to Snowpark Python, Dynamic Tables, Snowflake SQL, Tasks DAGs, or Snowpipe — with auto documentation and Snowflake best-practice patterns.
Row-level and aggregate data matching between legacy and Snowflake outputs — using Snowflake-native comparison queries for audit-ready sign-off.
Publish lineage, STTM, and data contracts to the Snowflake object catalog. MigryX AI surfaces risk and recommends clustering, materialization, and Virtual Warehouse sizing.
Platform Capabilities
Every MigryX modernization leverages the full Snowflake platform — Snowpark compute, Dynamic Tables, Streams & Tasks, Zero-Copy Cloning, Time Travel, and Cortex AI.
Purpose-built for each source language — SAS macro expansion, DataStage XML, Talend .item files, Qlik .qvs/.qvf, SSIS .dtsx, COBOL copybooks — full fidelity, no approximation, deterministic output.
Legacy ETL logic converted to Snowpark Python DataFrames — pushdown execution inside Virtual Warehouses with no external compute required. UDFs, UDTFs, and Stored Procedures generated automatically.
Scheduled ETL converted to Snowflake Dynamic Tables (declarative, lag-based refresh) and Streams + Tasks DAGs (event-driven CDC) — replacing legacy job schedulers with Snowflake-native orchestration.
Source-to-target column mappings and STTM tables published to the Snowflake object catalog — TAG-based governance, data classification, and lineage API integration for compliance.
AI analyzes parsed metadata to recommend clustering keys, materialization strategies, and Virtual Warehouse sizing. SAS analytical models land in Snowflake Cortex ML with automatic feature engineering.
Full deployment behind your firewall. Source code and lineage never leave your network. Zero-Copy Clone promotion patterns for dev → test → prod. SOX, GDPR, BCBS 239 ready.
Deep Platform Integration
MigryX isn't a generic modernization tool retrofitted for Snowflake. Every output is built for Snowflake-native execution — Snowpark pushdown, Dynamic Tables, Cortex AI, and governed by Snowflake's object catalog.
Generated Python leverages Snowpark DataFrame API for full pushdown execution — all computation runs inside the Virtual Warehouse, no data movement or external compute clusters required.
SnowparkIncremental ETL pipelines converted to declarative Dynamic Tables with target lag, automatic refresh scheduling, and built-in lineage — replacing legacy batch scheduling entirely.
Dynamic TablesCDC patterns and job scheduling converted to Snowflake Streams (change capture) and Tasks (DAG-based orchestration) — serverless compute, retry logic, and cron/event-driven triggers.
Streams & TasksSAS analytical models (PROC LOGISTIC, PROC GLM, PROC MIXED) converted to Snowflake Cortex — ML classification, regression, anomaly detection, and LLM functions (COMPLETE, SUMMARIZE) running natively.
Cortex AIBatch and near-real-time ingestion replatformed to Snowpipe with auto-ingest from S3, Azure Blob, and GCS — replacing legacy file-based ETL landing patterns with continuous streaming.
SnowpipeColumn-level lineage, STTM mappings, data classification TAGs, row access policies, and masking policies published directly to Snowflake's object catalog — full governance from day one.
Catalog & TAGsLegacy environment promotion patterns (dev → test → prod) replaced with Zero-Copy Cloning — instant schema and table clones with no storage duplication for CI/CD and testing workflows.
Zero-Copy CloneCross-organization data exchange patterns preserved during modernization — legacy file-based sharing converted to Snowflake Secure Data Sharing, listings, and Marketplace distributions.
Data SharingLegacy data lake tables modernized to Snowflake-managed Apache Iceberg Tables — open format storage with full Snowflake query performance, governance, and catalog integration on your own cloud storage.
IcebergModernization Architecture
Every MigryX modernization follows a deterministic pipeline that lands production-ready artifacts directly on Snowflake — governed, validated, and deployment-ready.
Measurable Results
Organizations using MigryX to land on Snowflake accelerate delivery, eliminate manual rewrite cost, and unlock Snowflake-native performance 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 on Snowflake with full Snowpark and Dynamic Table support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, Qlik, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to Snowflake catalog | ✓ | ~ |
| Native Snowpark Python output generation | ✓ | ✗ |
| Snowflake Dynamic Tables & Streams/Tasks generation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| COBOL copybook fidelity (packed decimal, REDEFINES, OCCURS DEPENDING ON) | ✓ | ✗ |
| Snowflake Cortex AI integration for analytical models | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & Snowflake object catalog registration | ✓ | ~ |
| Virtual Warehouse sizing recommendations per workload | ✓ | ✗ |
| Zero-Copy Clone promotion patterns (dev→test→prod) | ✓ | ✗ |
| Alteryx .yxmd workflow XML parsing & conversion | ✓ | ✗ |
| IBM DataStage .dsx / parallel job XML parsing | ✓ | ✗ |
| Informatica PowerCenter XML + IDMC/IICS mapping parsing | ✓ | ~ |
| Oracle ODI Knowledge Module (IKM/LKM/CKM) translation | ✓ | ✗ |
| SSIS .dtsx package parsing (data flow + control flow) | ✓ | ~ |
| Talend .item artifact & tMap conversion | ✓ | ✗ |
| Qlik .qvs / .qvf / .qvw load-script conversion | ✓ | ✗ |
| Teradata BTEQ command translation + 500+ SQL function maps | ✓ | ~ |
| Multi-target output (Snowflake + Databricks + BigQuery) | ✓ | ✗ |
| Deterministic AST-based parsing (not regex or AI-only) | ✓ | ✗ |
| Parser-driven risk analysis & Snowflake optimization | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported
Frequently Asked Questions
Common questions from teams evaluating MigryX for Snowflake modernization programs.
Snowflake-native. MigryX generates Snowpark Python with full pushdown execution inside Virtual Warehouses, Dynamic Tables with declarative refresh, Streams & Tasks DAGs, and Snowflake SQL — not generic Python or Spark code adapted for Snowflake.
MigryX analyzes the source pipeline pattern and recommends the optimal target: Dynamic Tables for declarative incremental refresh with target lag, Streams & Tasks for event-driven CDC with complex DAG dependencies. Both are generated automatically — the choice is based on parsed source semantics.
Yes. MigryX produces column-level STTM (Source-to-Target Mapping) tables and publishes them to the Snowflake object catalog with data classification TAGs, row access policies, and masking policies — providing full governance from day one of the modernization.
Yes. SAS PROC LOGISTIC, PROC GLM, PROC MIXED, and PROC MODEL are converted to Snowflake Cortex ML functions — classification, regression, anomaly detection, and forecasting — running natively inside Snowflake with no external compute required.
Legacy job schedulers (Control-M, Autosys, SAS batch flows, Talend triggers, DataStage sequences) are converted to Snowflake Tasks with DAG dependencies, serverless compute, retry logic, and cron-based scheduling — or to Dynamic Tables with automatic refresh targets.
Yes. Complex legacy logic that cannot be expressed as pure SQL is converted to Snowpark Python UDFs, UDTFs (for table-returning functions), and Stored Procedures — all executing natively inside the Virtual Warehouse with full pushdown.
Yes. MigryX supports full on-premise and air-gapped deployment. Source code, lineage data, and metadata never leave your network. Zero-Copy Cloning is used for environment promotion (dev → test → prod) without data duplication.
MigryX generates row-level and aggregate-level data comparison queries that run natively in Snowflake — comparing legacy output against Snowflake-produced output. Validation includes row counts, column checksums, business rule assertions, and statistical parity proofs for audit-ready sign-off.