MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects to AWS — Redshift, S3, Glue, EMR, Lambda, and Airflow — with +95% parsing accuracy and column-level lineage.
AWS Targets
Every migration generates production-ready AWS artifacts — leveraging Redshift, S3, Glue, EMR, Lambda, Step Functions, and Amazon MWAA (Airflow) across the AWS data ecosystem.
Legacy SQL and stored procedures converted to Redshift SQL — cloud data warehouse with columnar storage, distribution keys, sort keys, and Redshift Spectrum for S3 queries.
Data lake storage with Parquet, Iceberg, and CSV output formats — S3 bucket structures, partitioning strategies, and lifecycle policies generated from legacy file-based patterns.
Legacy ETL jobs converted to serverless AWS Glue jobs with PySpark and Spark SQL — Glue Data Catalog integration, crawlers, and job bookmarks for incremental processing.
Heavy-duty transformation workloads migrated to managed Spark and Hive on EMR clusters — with auto-scaling, spot instances, and EMR Serverless for cost-optimized compute.
Lightweight processing logic converted to serverless Python Lambda functions — event-driven triggers, API Gateway integration, and Step Functions orchestration for microservice patterns.
Legacy job sequences and orchestration converted to Step Functions state machines — visual workflow design, error handling, parallel execution, and native AWS service integration.
Pipeline scheduling and dependency management migrated to managed Apache Airflow on MWAA — DAGs, operators, sensors, and connections for end-to-end workflow orchestration.
Legacy analytics code converted to Python DataFrames and pandas — reusable analytics modules with NumPy, scikit-learn, and SageMaker integration for ML workloads on AWS.
Migration Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, AWS-native code — Redshift SQL, Glue PySpark, Lambda functions, or Airflow DAGs.
Automate SAS Base, Macro, PROC SQL, and IML conversion to AWS Glue PySpark and Redshift SQL. DATA step logic, FORMAT/INFORMAT handling, PROC SORT/MEANS/FREQ, and PROC MODEL translated to SageMaker ML.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to AWS Glue PySpark jobs and Step Functions orchestration with full component-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to AWS EMR PySpark and Glue jobs — tool-by-tool translation with full lineage preservation and Lambda functions for reuse.
Migrate IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to AWS Glue PySpark and Redshift — transformer logic translated to Glue ETL with S3 staging.
Migrate Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to AWS Glue PySpark with Step Functions orchestration and Glue Data Catalog lineage.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to Redshift SQL and AWS Glue jobs with full column-level lineage in Glue Data Catalog.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to AWS Glue PySpark pipelines and Step Functions orchestration with S3 ingestion.
Migrate Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY rewriting to Redshift window functions, BTEQ command translation, and PRIMARY INDEX → distribution key advisory for Redshift.
Migrate Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT → Redshift batching, and full package dependency resolution.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to Redshift SQL — 500+ function mappings, window function normalization, and SUPER/JSON semi-structured support.
Migrate SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to AWS Glue PySpark and Lambda UDFs with Glue Data Quality rules and SageMaker anomaly detection.
Before you migrate, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them directly into the AWS Glue Data Catalog for governance.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing natively on AWS.
Upload source artifacts — SAS scripts, Talend exports, DataStage XML, .dtsx packages — into MigryX for parsing.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage — with AWS-readiness scoring.
Parser-driven conversion to AWS Glue PySpark, Redshift SQL, Lambda functions, Step Functions, or Airflow DAGs — with auto documentation and AWS best-practice patterns.
Row-level and aggregate data matching between legacy and AWS outputs — using Redshift and Athena comparison queries for audit-ready sign-off.
Publish lineage, STTM, and data contracts to the AWS Glue Data Catalog. Merlin AI surfaces risk and recommends distribution keys, sort keys, and cluster sizing.
Platform Capabilities
Every MigryX migration leverages the full AWS platform — Redshift, S3, Glue, EMR, Lambda, Step Functions, MWAA Airflow, and SageMaker.
Purpose-built for each source language — SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx — full fidelity, no approximation, deterministic output.
Legacy ETL logic converted to AWS Glue PySpark, Redshift SQL, and Lambda functions — serverless execution with no infrastructure management. Glue jobs, Redshift stored procedures, and Lambda UDFs generated automatically.
Scheduled ETL converted to AWS Step Functions state machines and MWAA Airflow DAGs (event-driven orchestration) — replacing legacy job schedulers with AWS-native pipeline management.
Source-to-target column mappings and STTM tables published to the AWS Glue Data Catalog — Lake Formation governance, data classification, and lineage integration for compliance.
AI analyzes parsed metadata to recommend distribution keys, sort keys, and cluster sizing. SAS analytical models land in AWS SageMaker with automatic feature engineering and endpoint deployment.
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.
Measurable Results
Organizations using MigryX to land on AWS accelerate delivery, eliminate manual rewrite cost, and unlock AWS-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 migration-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 on AWS with full Glue, Redshift, and Step Functions support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to AWS Glue Data Catalog | ✓ | ~ |
| Native AWS Glue PySpark output generation | ✓ | ✗ |
| AWS Redshift SQL & Step Functions generation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| AWS SageMaker integration for analytical models | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & AWS Glue Data Catalog registration | ✓ | ~ |
| Redshift cluster & EMR sizing recommendations per workload | ✓ | ✗ |
| CloudFormation / CDK infrastructure-as-code templates | ✓ | ✗ |
| 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 | ✓ | ✗ |
| Teradata BTEQ command translation + 500+ SQL function maps | ✓ | ~ |
| Multi-target output (AWS + Snowflake + Databricks + BigQuery) | ✓ | ✗ |
| Deterministic AST-based parsing (not regex or AI-only) | ✓ | ✗ |
| Merlin AI risk analysis & optimization recommendations | ✓ | ✗ |
✓ 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 AWS Glue/Redshift output from code.