MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects to Google BigQuery — BigQuery ML, Dataform, Dataproc PySpark, Dataplex governance, Vertex AI, and BigLake — with +95% parsing accuracy and column-level lineage.
Google Cloud Targets
Every migration generates production-ready Google Cloud artifacts — leveraging BigQuery's serverless architecture, Dataform SQL pipelines, Dataproc Spark, Cloud Composer orchestration, Vertex AI, and Dataplex governance.
Legacy SQL and ETL logic translated to BigQuery Standard SQL — partitioned & clustered tables, materialized views, authorized views, and row-level security policies.
ETL pipelines converted to Dataform SQL workflows — SQLX definitions, table declarations, incremental models, assertions, and Git-backed CI/CD deployment via Dataform repositories.
Heavy-lifting ETL converted to Dataproc PySpark jobs — serverless or cluster-based execution on Google Cloud, with BigQuery Spark connector for direct table read/write.
Streaming and complex batch pipelines migrated to Apache Beam / Dataflow — fully managed, auto-scaling, with native BigQuery sinks and Pub/Sub source integration.
Legacy scheduler and ETL orchestration replatformed to Cloud Composer — DAG-based scheduling, BigQueryOperator tasks, dependency management, and SLA monitoring.
SAS analytical and scoring models converted to BigQuery ML (CREATE MODEL SQL) and Vertex AI — AutoML, feature engineering, model registry, and online/batch prediction endpoints.
Legacy data lake assets migrated to BigLake external tables or BigQuery-managed Apache Iceberg tables — open format on GCS with BigQuery query performance and fine-grained access control.
Column-level lineage, STTM mappings, data quality rules, and business glossary terms published to Google Dataplex — automated data catalog tagging and policy propagation.
Legacy ETL visual workflows converted to Cloud Data Fusion pipelines — CDAP-based visual DAGs with BigQuery sinks, Wrangler transforms, and reusable plugin configurations for batch and real-time ingestion.
Migration Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, BigQuery-native code — Standard SQL, Dataform SQLX, or Dataproc PySpark.
Automate SAS Base, Macro, PROC SQL, and IML conversion to BigQuery Standard SQL and Dataproc PySpark. DATA step logic, FORMAT/INFORMAT, PROC SORT/MEANS/FREQ — with PROC MODEL landing in BigQuery ML or Vertex AI AutoML.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to Dataform SQLX pipelines and Cloud Composer DAGs with full component-level lineage in Dataplex.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to BigQuery SQL and Dataproc PySpark — tool-by-tool translation with full lineage, and UDFs registered in BigQuery for reuse.
Migrate IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to Dataproc PySpark and Cloud Dataflow — transformer logic translated to BigQuery-native patterns with Dataplex lineage.
Migrate Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to Dataform SQLX models and Cloud Composer orchestration with Dataplex catalog registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to Dataform SQLX incremental models and BigQuery partitioned tables with column-level lineage.
Parse SSIS .dtsx packages and .ispac archives — data flow, control flow, SSIS expressions, C#/VB.NET script tasks — to Cloud Composer Airflow DAGs with BigQueryOperator tasks and Dataflow streaming jobs.
Migrate Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → BigQuery window functions, BTEQ command translation, PRIMARY INDEX → partition/cluster key advisory, and 500+ function remappings.
Migrate Oracle PL/SQL procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT → BigQuery ARRAY_AGG, and full package dependency resolution.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica to BigQuery Standard SQL — 500+ function mappings, ARRAY/STRUCT nested data handling, and partitioning advisories.
Migrate SAS DataFlux dfPower Studio jobs and DQ schemes — standardize/parse/match/validate patterns — to BigQuery SQL data quality routines and Dataplex data quality rules with Vertex AI anomaly detection.
Before you migrate, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them directly to Google Dataplex for catalog, tags, and data quality governance.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing natively on Google Cloud.
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 BigQuery readiness and partition/cluster key scoring.
Parser-driven conversion to BigQuery Standard SQL, Dataform SQLX, Dataproc PySpark, or Cloud Composer DAGs — with BigQuery best-practice partitioning, clustering, and materialized view patterns.
Row-level and aggregate data matching between legacy and BigQuery outputs — Dataplex data quality scans for audit-ready parity evidence and go-live sign-off.
Publish lineage, STTM, and data contracts to Dataplex. Merlin AI recommends partition strategies, slot reservations, and BigQuery ML vs. Vertex AI routing per workload.
Platform Capabilities
Every MigryX migration is engineered for the full Google Cloud stack — BigQuery serverless analytics, Dataform SQL pipelines, Dataproc Spark, Dataplex governance, and Vertex AI for analytical models.
Purpose-built for each source language — SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx — full fidelity, deterministic output, no approximation.
Legacy SQL translated to BigQuery Standard SQL — partitioned tables (time, range), clustered tables, materialized views, authorized views, row-level security, and INFORMATION_SCHEMA metadata queries.
ETL workflows converted to Dataform SQLX — incremental table models, table declarations, data assertions, tags, and Git-backed CI/CD via Dataform repositories and workflow configurations.
Source-to-target column mappings and STTM tables published to Google Dataplex — automated Data Catalog tags, policy tags for column-level security, data quality scans, and Lineage API integration.
AI analyzes parsed metadata to recommend partition keys, clustering columns, and slot reservation sizing. SAS PROC MODEL and scoring logic land in BigQuery ML or Vertex AI AutoML automatically.
Full deployment behind your firewall. Source code and lineage never leave your network. Dataform Git integration for dev → staging → production promotion. SOX, GDPR, BCBS 239 ready.
Cloud Data Fusion
MigryX converts visual ETL from Alteryx, Talend, DataStage, Informatica, SSIS, and ODI into Cloud Data Fusion pipelines — generating both SQL-based and idiomatic CDAP approaches depending on transformation complexity.
When legacy ETL logic is fundamentally SQL — SELECT, JOIN, GROUP BY, window functions, MERGE — MigryX generates Data Fusion pipelines that push SQL directly to BigQuery. The pipeline becomes a thin orchestration layer while BigQuery's serverless engine handles the heavy compute.
When legacy ETL involves visual dataflow logic — multi-branch routing, row-level transformations, conditional splits, custom functions — MigryX generates native CDAP plugins that map 1:1 to legacy transformation semantics inside Data Fusion's visual pipeline designer.
Each legacy source maps differently. Alteryx multi-output tools become Data Fusion Splitter plugins. Talend tMap lookup joins become Joiner stages. DataStage sequential files become GCS source connectors. MigryX chooses the right Data Fusion pattern for each source construct.
MigryX produces Data Fusion pipeline JSON (CDAP artifact spec) ready for import — sources, transforms, sinks, connections, and error handling pre-configured. No manual assembly. Deploy directly via the Data Fusion REST API or Studio UI.
Real-world ETL mixes SQL and procedural logic. MigryX generates hybrid pipelines that push SQL to BigQuery for set operations and use CDAP plugins for row-level transforms — combining the best of both approaches in a single Data Fusion pipeline.
Legacy batch ETL converts to Data Fusion batch pipelines. Event-driven or near-real-time sources convert to Data Fusion real-time pipelines with Pub/Sub sources and streaming BigQuery sinks — preserving the original processing semantics.
Every generated Data Fusion pipeline includes lineage metadata. Column-level source-to-target mappings are published to Dataplex Data Catalog automatically — ensuring governance continuity from legacy ETL to Data Fusion without manual documentation.
MigryX validates Data Fusion pipeline outputs against legacy system outputs — row counts, aggregate comparisons, and column-level data matching. Evidence-based parity reports prove the migration is correct before cutover.
MigryX's parser analyzes each legacy workflow and classifies every transformation as SQL-expressible or plugin-required. Pure SQL flows get BigQuery pushdown pipelines. Visual dataflow logic gets CDAP plugins. Mixed workloads get hybrid pipelines. The decision is automatic, auditable, and overridable.
Measurable Results
Organizations using MigryX to land on BigQuery accelerate delivery, eliminate manual rewrite cost, and unlock BigQuery's serverless performance and Vertex AI capabilities from day one.
Automated lineage extraction and parser-driven analysis eliminate months of manual discovery and rewrite work.
Complete dependency visibility prevents production incidents and migration-related data defects on BigQuery.
Automated conversion, accelerated time-to-value, and eliminated rework deliver 60%+ project 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 BigQuery with Dataform, Dataproc, and Dataplex support.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to Dataplex catalog | ✓ | ~ |
| Native BigQuery SQL with partitioning & clustering | ✓ | ~ |
| Dataform SQLX pipeline generation | ✓ | ✗ |
| Dataproc PySpark & Cloud Dataflow output | ✓ | ✗ |
| Cloud Composer DAG generation | ✓ | ✗ |
| BigQuery ML / Vertex AI analytical model migration | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Dataplex data quality rules & policy tag generation | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| Cloud Data Fusion pipeline generation (SQL + CDAP) | ✓ | ✗ |
| BigQuery slot reservation & partition key recommendations | ✓ | ✗ |
| 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 (BigQuery + Snowflake + Databricks) | ✓ | ✗ |
| Deterministic AST-based parsing (not regex or AI-only) | ✓ | ✗ |
| Parser-driven risk analysis & BigQuery optimization | ✓ | ✗ |
| STTM export & Dataplex catalog registration | ✓ | ~ |
✓ 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 BigQuery SQL output from code.