MigryX converts SAS, Talend, Alteryx, IBM DataStage, Informatica, Oracle ODI, SSIS, Teradata, and SQL dialects directly to Databricks — PySpark, Delta Lake (MERGE, Z-ORDER, Liquid Clustering), Medallion Architecture, Unity Catalog, DLT Pipelines, and Databricks Workflows — with +95% parsing accuracy and column-level lineage.
Databricks Targets
Every migration generates production-ready Databricks Lakehouse artifacts — following Medallion Architecture (Bronze → Silver → Gold), optimized for Photon engine, and governed by Unity Catalog.
Production-grade PySpark code with Auto Loader ingestion, Change Data Feed (CDF) CDC patterns, and Medallion Architecture layering — Bronze raw, Silver cleansed, Gold aggregated.
ACID-compliant Delta tables with MERGE INTO upserts, OPTIMIZE & Z-ORDER compaction, Liquid Clustering, schema evolution, time travel, and Change Data Feed enabled.
Column-level lineage, STTM mappings, attribute-based tags, and fine-grained access controls registered in Unity Catalog — full data contract governance across the Lakehouse.
ETL pipelines converted to Databricks Workflows via Asset Bundles (DABs) — dependency-aware multi-task DAGs, serverless compute, job clusters, and triggered/scheduled orchestration.
Streaming and batch ETL converted to DLT pipelines — declarative @dlt.table definitions, Auto Loader CDC ingestion, data quality expectations, and enhanced autoscaling.
Converted code delivered as annotated Databricks Notebooks — with %sql / %python cells, Databricks Connect compatibility, lineage comments, and inline validation cells.
SAS analytical models and scoring logic converted to Python — MLflow experiment tracking, model registry, Feature Store integration, AutoML baselines, and Model Serving endpoints.
Legacy SQL dialects transpiled to Databricks SQL — Photon-optimized queries, serverless SQL Warehouses, ANSI SQL compliance, and 500+ dialect function mappings.
Migration Sources
Purpose-built parsers for each source platform. Not generic scanners. Every conversion produces explainable, auditable, Databricks-native code.
Automate SAS Base, Macro, PROC SQL, and IML conversion to PySpark and Databricks SQL. Full macro expansion, DATA step logic, FORMAT/INFORMAT handling, and PROC SORT/MEANS/FREQ translation.
Parse Talend project exports (ZIP/Git), .item artifacts, tMap joins, metadata, contexts, and connections — converted to PySpark jobs and Databricks Workflows with full component-level lineage.
Convert Alteryx Designer workflows (.yxmd/.yxwz), macros, and apps to PySpark and Databricks SQL — tool-by-tool translation with full lineage preservation and Databricks notebook output.
Migrate IBM DataStage parallel and server jobs, sequences, shared containers, and XML definitions to PySpark, Delta Live Tables, and Databricks Workflows — transformer logic fully preserved.
Migrate Informatica PowerCenter (.xml exports) and IDMC/IICS mappings — sources, targets, transformations, and workflows — to PySpark jobs with Unity Catalog lineage registration.
Parse Oracle ODI repository exports — mappings, interfaces, knowledge modules, packages, and load plans — converted to PySpark and Delta Lake 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 and Databricks Workflows.
Migrate Teradata BTEQ, FastLoad, MultiLoad, and Teradata SQL — QUALIFY → window function rewriting, BTEQ command translation, and PRIMARY INDEX advisory — to Databricks SQL and PySpark.
Migrate Oracle PL/SQL stored procedures, packages, and triggers with 2000+ function mappings, CONNECT BY → recursive CTE rewriting, BULK COLLECT/FORALL — targeting Databricks SQL.
Transpile SQL from Oracle, T-SQL, Teradata, DB2, Netezza, Greenplum, Hive HQL, and Vertica directly to Databricks 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 Python on Databricks with Great Expectations integration.
Before you migrate, map your estate. Compass extracts column-level lineage, STTM, and dependency graphs from any source — and publishes them directly into Databricks Unity Catalog.
How It Works
The same proven methodology applies to every source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI — all landing on Databricks.
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, Delta Lake, Databricks SQL, Workflows, or DLT — with full documentation.
Row-level and aggregate data matching between legacy and Databricks outputs — audit-ready evidence for sign-off.
Publish lineage, STTM, and data contracts to Unity Catalog. Merlin AI surfaces risk and recommends optimization paths.
Platform Capabilities
Every MigryX migration is engineered for the full Databricks Lakehouse — Medallion Architecture, Photon-optimized SQL, Unity Catalog governance, Delta Lake storage, and Asset Bundle deployment.
Purpose-built for each source language. SAS macro expansion, DataStage XML, Talend .item files, SSIS .dtsx — full fidelity, deterministic output, no approximation.
Legacy pipelines restructured into Bronze (raw ingestion via Auto Loader), Silver (cleansed, deduplicated), and Gold (aggregated, business-ready Delta tables) layers automatically.
Tables generated with MERGE INTO upserts, OPTIMIZE & Z-ORDER compaction, Liquid Clustering, Change Data Feed, schema enforcement, and time travel — production-ready from day one.
Source-to-target column mappings, STTM tables, and data contracts published to Unity Catalog — fine-grained access, attribute tags, and Databricks Lineage API integration.
AI analyzes parsed metadata to recommend Photon optimization, Z-ORDER keys, and partition strategies. SAS models land in MLflow Feature Store with AutoML baseline generation.
Full deployment behind your firewall with Asset Bundle (DAB) packaging for CI/CD. Source code and lineage never leave your network. SOX, GDPR, BCBS 239 ready.
Deep Platform Integration
MigryX isn't a generic migration tool retrofitted for Databricks. Every output is built for Databricks-native execution — Photon-optimized, Unity-governed, serverless-ready, and deployed via Asset Bundles.
Generated SQL and PySpark leverage Photon-compatible patterns — vectorized column operations, predicate pushdown hints, and join strategies optimized for Photon's C++ execution engine.
Photon RuntimeMigrated workloads target Serverless SQL Warehouses and Serverless Jobs compute — auto-provisioned, zero-management clusters with instant startup and cost-efficient scaling.
ServerlessSource system connections mapped to Databricks LakeFlow Connect ingestion pipelines — replacing legacy source connectors with managed, incremental CDC ingestion into Delta Lake.
LakeFlowSAS analytical models (PROC LOGISTIC, PROC GLM, PROC MIXED) converted to Python and registered in Mosaic AI — with Model Serving endpoints, A/B testing, and Feature Engineering tables.
Mosaic AIAll migrated artifacts packaged as Databricks Asset Bundles — version-controlled YAML definitions, CI/CD-ready deployment, environment promotion (dev → staging → prod), and git integration.
DABs / CI/CDColumn-level lineage, STTM mappings, data classification tags, row-level security policies, and attribute-based access controls published directly to Unity Catalog — not sidecar metadata.
Unity CatalogLegacy reports (SAS PROC REPORT, Crystal Reports, SSRS) converted to Databricks SQL queries with AI/BI Dashboard definitions — parameterized queries, scheduled refreshes, and alert triggers.
AI/BI DashboardsCross-organization data sharing patterns preserved during migration — legacy file-based data exchange converted to Delta Sharing recipients, providers, and shares with fine-grained access control.
Delta SharingMigration status dashboards, lineage explorers, and validation reports deployed as Databricks Apps — custom Streamlit/Gradio applications running natively inside the Databricks workspace.
Databricks AppsMigration Architecture
Every MigryX migration follows a deterministic pipeline that lands production-ready artifacts directly on the Databricks Lakehouse — governed, validated, and deployment-ready.
Measurable Results
Organizations using MigryX to land on Databricks 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 Databricks.
| Capability | MigryX | Generic Tools |
|---|---|---|
| Custom parser per source (SAS, Talend, DataStage, etc.) | ✓ | ✗ |
| 100% column-level lineage to Unity Catalog | ✓ | ~ |
| Native Delta Lake & DLT output generation | ✓ | ✗ |
| Databricks Workflows DAG generation | ✓ | ✗ |
| SAS macro expansion & full dialect support | ✓ | ✗ |
| Parser-driven risk analysis & Databricks optimization | ✓ | ✗ |
| On-premise / air-gapped deployment | ✓ | ✗ |
| Row-level data validation & parity proof | ✓ | ✗ |
| STTM export & Unity Catalog registration | ✓ | ~ |
| Medallion Architecture (Bronze/Silver/Gold) generation | ✓ | ✗ |
| Delta Lake MERGE INTO & Liquid Clustering patterns | ✓ | ✗ |
| Databricks Asset Bundles (DABs) for CI/CD deployment | ✓ | ✗ |
| 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 (Databricks + Snowflake + BigQuery) | ✓ | ✗ |
| Deterministic AST-based parsing (not regex or AI-only) | ✓ | ✗ |
| MLflow model migration from SAS PROC MODEL | ✓ | ✗ |
| PySpark notebook output with inline lineage comments | ✓ | ✗ |
✓ Full support ~ Partial / approximate ✗ Not supported
Frequently Asked Questions
Common questions from teams evaluating MigryX for Databricks modernization programs.
Databricks-native. MigryX generates PySpark that leverages Databricks-specific APIs — Delta Lake MERGE INTO with CDC patterns, Auto Loader for ingestion, Unity Catalog references for table governance, DLT @dlt.table decorators, and Databricks Workflows YAML definitions. It is not generic Spark code adapted for Databricks.
MigryX automatically restructures legacy pipelines into Bronze (raw ingestion via Auto Loader or COPY INTO), Silver (cleansed, deduplicated, schema-enforced Delta tables), and Gold (aggregated, business-ready views and tables) layers. The layering is deterministic based on parsed source logic — not manual mapping.
Yes. MigryX produces column-level STTM (Source-to-Target Mapping) tables and publishes them to Unity Catalog via the Lineage API. This includes data classification tags, attribute-based access policies, and data contract definitions — providing full governance from day one of the migration.
Yes. SAS PROC LOGISTIC, PROC GLM, PROC MIXED, and PROC MODEL are converted to equivalent Python (scikit-learn / statsmodels) with MLflow experiment tracking, model registry, and Feature Store integration. Model serving endpoints and AutoML baselines are generated automatically.
Legacy job schedulers (Control-M, Autosys, SAS batch flows, Talend triggers, DataStage sequences) are converted to Databricks Workflows with multi-task DAG dependencies, cluster policies, retry logic, and cron-based scheduling. Orchestration logic is preserved, not approximated.
Yes. Streaming and batch ETL patterns are converted to DLT pipelines with declarative @dlt.table and @dlt.view definitions, APPLY CHANGES for CDC, data quality EXPECT constraints, and enhanced autoscaling. DLT is the recommended target for continuous ingestion workloads.
Yes. MigryX supports full on-premise and air-gapped deployment. Source code, lineage data, and metadata never leave your network. Output artifacts are packaged as Databricks Asset Bundles (DABs) for secure CI/CD deployment into your Databricks workspace.
MigryX generates row-level and aggregate-level data comparison reports between legacy system output and Databricks-produced output. Validation includes row counts, column checksums, business rule assertions, and statistical parity proofs — producing audit-ready evidence for sign-off.
As a Databricks Technology Partner, we'll run a technical deep-dive on your specific source — SAS, Talend, Alteryx, DataStage, Informatica, or ODI. We'll show you parsed lineage, PySpark output, and Unity Catalog registration from code.