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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 Apache Iceberg — open table format running on Spark, Trino, Flink, or Dremio — with schema evolution, hidden partitioning, time travel, and full column-level lineage.
Iceberg Targets
Every modernization generates production-ready Iceberg artifacts — leveraging PySpark, Trino SQL, Flink streaming, schema evolution, hidden partitioning, time travel, and catalog integration.
ACID-compliant tables with hidden partitioning, metadata-driven pruning, and snapshot isolation — the open table format for modern data lakehouses.
PySpark reading/writing Iceberg tables natively via Iceberg catalog integration — pushdown computation with partition pruning and predicate filtering.
High-performance SQL analytics on Iceberg tables with predicate pushdown — federated queries across Iceberg catalogs with sub-second latency.
Streaming writes and CDC to Iceberg tables via Flink Iceberg connector — real-time data ingestion with exactly-once semantics and automatic compaction.
Add, rename, drop, or reorder columns without full table rewrite — Iceberg tracks schema changes in metadata, ensuring backward and forward compatibility.
Point-in-time queries via snapshot isolation, rollback to any previous state — audit, debug, and reproduce historical results with zero data duplication.
Change partition strategy (daily to hourly, add new partition columns) without data rewrite — Iceberg handles mixed partition layouts transparently.
Hive Metastore, AWS Glue, Nessie, Polaris, Unity Catalog, and REST catalog — register Iceberg tables in any catalog for unified governance and discovery.
Modernization Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Iceberg-native code — PySpark, Trino SQL, or Flink streaming inserts.
Automate SAS Base, Macro, PROC SQL, and IML conversion to PySpark with Iceberg table writes. DATA step logic, FORMAT/INFORMAT handling, PROC SORT/MEANS/FREQ translated to Spark SQL on Iceberg.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to PySpark Iceberg jobs with full component-level lineage and catalog registration.
Parse Qlik Sense .qvf apps, QlikView .qvw documents, and .qvs includes — load scripts, ApplyMap, JOIN/RESIDENT, and QVD stores — converted to PySpark Iceberg jobs with full script-level lineage and catalog registration.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to PySpark with Iceberg catalog writes — tool-by-tool translation with full lineage preservation and partition strategy recommendations.
Modernize IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to PySpark with Iceberg table writes — transformer logic translated to Spark SQL with hidden partitioning.
Modernize Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to PySpark with Iceberg writes and catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to PySpark Iceberg writes with full column-level lineage in Iceberg REST catalog.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to PySpark Iceberg pipelines with Flink CDC for streaming ingestion patterns.
Modernize Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY rewriting, BTEQ command translation, and PRIMARY INDEX converted to Iceberg hidden partition strategies and sort orders.
Modernize Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY to recursive CTE rewriting, BULK COLLECT to PySpark batching, writing to Iceberg tables with full lineage.
Parse COBOL programs with their copybooks — packed decimal, implied decimals, REDEFINES overlays, and OCCURS DEPENDING ON — to PySpark writing Apache Iceberg tables. DB2 embedded SQL becomes Spark SQL, and JCL job steps become orchestrated Spark jobs with condition codes preserved.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to Trino SQL on Iceberg tables — 500+ function mappings, window function normalization, and schema evolution support.
Modernize SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to PySpark UDFs writing Iceberg tables with data quality constraints and 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 Apache Iceberg.
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 Iceberg-readiness scoring.
Convert to PySpark with Iceberg catalog writes, Trino SQL views, or Flink streaming inserts — with auto documentation and partition strategy recommendations.
Row-level and aggregate data matching between legacy and Iceberg outputs — using Spark-native comparison queries for audit-ready sign-off.
Publish lineage, STTM, and data contracts to Iceberg REST catalog. MigryX AI surfaces risk and recommends partition strategies, sort orders, and compaction policies.
Platform Capabilities
Every MigryX modernization leverages the full Apache Iceberg ecosystem — PySpark compute, Trino analytics, Flink streaming, schema evolution, hidden partitioning, time travel, and multi-catalog integration.
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 Iceberg table writes via PySpark — no vendor lock-in. Output runs on any engine that supports Iceberg: Spark, Trino, Flink, Dremio, or Databricks.
Output runs on Spark, Trino, Flink, Dremio, Snowflake, or Databricks — the same Iceberg tables are queryable from any engine with no data movement or format conversion required.
Source-to-target column mappings registered in Iceberg REST catalog, AWS Glue, or Nessie — full STTM export with partition metadata, schema versions, and snapshot history for compliance.
AI analyzes data patterns to recommend hidden partitioning, sort orders, and compaction policies. Automatic partition evolution suggestions based on query patterns and data volume growth.
Full deployment behind your firewall. Source code and lineage never leave your network. Iceberg catalog integration works with on-prem Hive Metastore or Nessie. SOX, GDPR, BCBS 239 ready.
Measurable Results
Organizations using MigryX to land on Apache Iceberg accelerate delivery, eliminate manual rewrite cost, and unlock multi-engine lakehouse 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 Apache Iceberg with full PySpark and multi-engine support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to Iceberg catalog | ✓ | ~ |
| Native PySpark + Iceberg output generation | ✓ | ✗ |
| Multi-engine support (Spark, Trino, Flink, Dremio) | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| COBOL copybook fidelity (packed decimal, REDEFINES, OCCURS DEPENDING ON) | ✓ | ✗ |
| Parser-driven partition strategy optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & Iceberg REST catalog registration | ✓ | ~ |
| Schema evolution & partition evolution support | ✓ | ✗ |
| Hidden partitioning recommendation engine | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported