MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects directly to Apache PySpark — then deploy anywhere: AWS Glue, EMR, SageMaker, Azure Fabric, Google Dataproc, Databricks, Cloudera, or plain open-source Apache PySpark. +95% parsing accuracy, column-level lineage, and production-ready code for any Spark runtime.
Deploy Anywhere
MigryX generates production-ready PySpark that runs on any Spark runtime — managed cloud services, on-premise clusters, or standalone open-source Apache Spark. You choose where it runs.
No vendor lock-in. No proprietary APIs. Pure Apache PySpark.
Run PySpark on managed Hadoop/Spark clusters — auto-scaling, Spot Instances, S3 integration, and EMR Serverless for cost-optimized batch & streaming.
Serverless PySpark ETL — zero infrastructure. Glue Catalog integration, job bookmarks, crawler-driven schema discovery, and pay-per-DPU execution.
PySpark feature engineering in SageMaker Processing — feed ML pipelines with Spark-prepared data, integrated with SageMaker Studio notebooks and MLOps.
Run PySpark natively in Fabric Lakehouses — OneLake integration, notebook-driven Spark pools, Power BI DirectLake, and unified analytics across the Microsoft ecosystem.
Managed Spark on GCP — ephemeral clusters, Dataproc Serverless, BigQuery connector, and Cloud Storage integration with sub-second cluster autoscaling.
Lakehouse Platform with Photon engine — Unity Catalog, Delta Live Tables, MLflow, and collaborative notebooks. MigryX PySpark runs natively on Databricks Runtime.
Enterprise Spark on Cloudera Data Platform — CDE Spark jobs, Iceberg tables, SDX governance, and hybrid cloud deployment across on-premise and public cloud.
Pure open-source — standalone clusters, YARN, Kubernetes, or Docker. No managed service required. Full control over your Spark runtime, configurations, and infrastructure.
MigryX outputs standard Apache PySpark code — no proprietary extensions. Run it on any Spark-compatible runtime today, and switch tomorrow without rewriting a line.
PySpark Targets
Every migration generates production-ready PySpark artifacts — distributed DataFrames with Catalyst query optimization, Spark SQL, Structured Streaming, MLlib Pipelines, and Delta Lake output for petabyte-scale data processing.
Distributed DataFrames with the Catalyst optimizer — predicate pushdown, column pruning, and adaptive query execution across YARN, Kubernetes, or standalone clusters.
Full ANSI SQL on distributed data — register DataFrames as tables, execute SQL queries, and integrate with Hive Metastore, AWS Glue Catalog, or Unity Catalog seamlessly.
Real-time stream processing with exactly-once semantics — Kafka, Kinesis, and file-based sources with watermarking, windowed aggregations, and stateful processing.
Distributed machine learning pipelines — feature engineering, model training, cross-validation, and hyperparameter tuning at scale using Spark MLlib's Pipeline API.
ACID transactions on data lakes — time travel, schema evolution, MERGE/UPSERT, Z-ordering, and optimized writes for reliable lakehouse architectures on open formats.
Enterprise data warehousing on open table formats — Hive managed/external tables, Apache Iceberg tables with partition evolution, and schema-on-read compatibility.
Automated data quality checks — null counts, cardinality, distributions, schema drift detection — generated alongside every migration output for validation.
Lineage and STTM mappings published to Unity Catalog, Hive Metastore, or AWS Glue Catalog — full governance for PySpark-based pipelines.
Migration Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, PySpark-native code.
Automate SAS Base, Macro, PROC SQL, and IML conversion to PySpark DataFrames and Spark SQL. Full macro expansion, DATA step logic, FORMAT/INFORMAT handling, and PROC SORT/MEANS/FREQ translation to distributed operations.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to PySpark DataFrames and Spark SQL with full component-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to PySpark DataFrames and Spark SQL — tool-by-tool translation with full lineage preservation and MLlib pipeline output.
Migrate IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to PySpark DataFrames and Delta Lake — transformer logic fully preserved as distributed operations.
Migrate Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to PySpark DataFrames with catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to PySpark DataFrames and Spark SQL with full column-level lineage.
Parse SQL Server Integration Services .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to PySpark DataFrames and Structured Streaming.
Migrate Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → window function rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to Spark SQL and PySpark DataFrames.
Migrate Oracle PL/SQL stored procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT/FORALL — targeting Spark SQL and PySpark DataFrames.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica directly to Spark SQL — with 500+ function mappings and dialect-aware query rewriting.
Migrate SAS DataFlux dfPower Studio jobs, DMS Data Jobs, and Real-time Services — standardize/parse/match/validate schemes — to PySpark DataFrames with data quality profiling integration.
Before you migrate, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them to your data catalog for PySpark-based pipelines.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing on Apache PySpark.
Upload source artifacts — SAS scripts, Talend exports, DataStage XML, .dtsx packages — into MigryX.
Custom parsers build complete ASTs, expand macros, resolve dependencies, and produce column-level lineage maps.
Parser-driven conversion to PySpark DataFrames, Spark SQL, Structured Streaming, or MLlib Pipelines — with full documentation.
Row-level and aggregate data matching between legacy and PySpark outputs — audit-ready evidence for sign-off.
Publish lineage, STTM, and data contracts to your catalog. Merlin AI surfaces risk and recommends optimization paths.
Platform Capabilities
Every MigryX migration is engineered for the full Apache Spark ecosystem — Catalyst query optimization, Tungsten execution, cluster-mode deployment on YARN/Kubernetes, and catalog-integrated governance.
Purpose-built for each source language. SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx — full fidelity, deterministic output, no approximation.
Native distributed execution on YARN, Kubernetes, or standalone clusters. Adaptive query execution, dynamic partition pruning, and shuffle optimization for petabyte-scale workloads.
Spark's Catalyst query optimizer generates optimal execution plans — predicate pushdown, column pruning, join reordering, and whole-stage code generation for maximum throughput.
Source-to-target column mappings, STTM tables, and data contracts — full lineage from legacy source through PySpark transformations to final output.
AI analyzes parsed metadata to recommend partition strategies, broadcast join thresholds, and caching boundaries. Surfaces migration risk and complexity scoring.
Full deployment behind your firewall with CI/CD packaging. Source code and lineage never leave your network. SOX, GDPR, BCBS 239 ready.
Measurable Results
Organizations using MigryX to land on PySpark accelerate delivery, reduce risk, and eliminate manual rewrite costs across every modernization program.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite work.
Complete visibility into dependencies prevents production incidents and migration-related data defects.
Reduced consulting spend, 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 PySpark.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage | ✓ | ~ |
| Native PySpark DataFrame output | ✓ | ✗ |
| Spark SQL generation with Catalyst optimization | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Parser-driven risk analysis & Spark optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & catalog registration | ✓ | ~ |
| Delta Lake & Iceberg table output | ✓ | ~ |
| Structured Streaming & MLlib Pipeline generation | ✓ | ✗ |
✓ 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 PySpark output from code.