Smart Export / XML · packages & load plans — lineage and automated conversion.10X SPEED
Everything MigryX ingests from ODI, the engine that converts it, and every target it produces.
Scan Oracle ODI metadata from repository XML, Smart Export, or checked-in design bundles to build a complete inventory. Discover mapping dependencies, KM chains, packages, load-plan steps, and datastore usage — plus fan-in or fan-out hot spots. Produce visual lineage and impact maps that guide the entire migration.
Parser conversion turns ODI mapping steps and KM-driven logic (from exports) into Python, PySpark, Snowpark, and SQL for Snowflake, Databricks, BigQuery, Redshift, and Fabric. All translations are explainable and auditable.
Run converted workloads in the right order with a driver notebook or job runner. Standardize on Delta and cloud storage, schedule, monitor, and auto retry with centralized logs and metrics.
Partitioned validation compares row level and aggregate outputs between legacy and modern systems. Automatic schema checks, data matching reports, and exception trails give confidence to go live.
Context aware assistance that knows your inventory, lineage, and conversion plans. Generate unit tests, explain diffs, suggest mappings, and draft notebooks with your rules applied.
Visual execution runs directly on Snowflake and Databricks, combining lineage and live code in one workspace with a direct warehouse session and step-by-step visibility to any failure point.
Assess thousands of ODI mappings and packages from repository exports, map complexity across KMs and datastores, and flag readiness. Get clear scope, a prioritized plan, safer cutovers, and faster production.
Visualize code across jobs, tables, and SQL to see sources, flows, and changes. Speeds impact checks, lowers migration risk, supports audits, and proves outputs match.
Convert ODI mappings and packages into Python, PySpark, Snowpark, or SQL with matched outputs — including pushdown SQL and KM behavior where it maps cleanly. Modernize faster, keep logic intact, and avoid risky rewrites.
Automatically map legacy schemas to Snowflake or Databricks with clear mappings. Cut migration risk, enforce naming and data types, and get audit-ready visibility.
Automatic documentation captures your ODI mappings and the new target code, detailing datasets, variables, KMs, and cross-package dependencies for clear traceability.
Compares source and target outputs at scale using configurable keys and rules. Flags mismatches, duplicates, and gaps with actionable reports for fast fixes.
This page is dedicated to migrating Oracle Data Integrator (ODI) — repository exports (XML / Smart Export), mappings, packages, load plans, knowledge modules (IKM, LKM, CKM), and topology metadata — into modern Python and cloud targets. Need other legacy engines? See the full platform.
Python (Pandas), PySpark, Snowflake/Snowpark, Databricks, and cloud platforms.
Everything runs inside your network. No external connections. No data leaves your environment in any scenario.
Assess, convert, and validate Oracle ODI repository exports and mappings safely inside your environment.
| Type | Migration Readiness | Full Pilot | Large Scale Pilot |
|---|---|---|---|
| Discovery | 100,000 LoC | 100,000 LoC | 1 Million LoC |
| Conversion | N/A | 10,000 LoC | 100,000 LoC |
| Duration | 1 week | 4 to 6 weeks | 2 to 4 months |
| Deliverables | Project reports Risk analysis |
Full reports Executed code |
Full reports Executed code |
| Reports | Inventory,lineage,risk | Full project | Full project and JCL |
| Execution | In your environment | In your environment | In your environment |
These pilots run securely within your environment. Pricing and scope can be adjusted to match complexity and urgency.
A compact view of what exists, how it connects, and where risk lives.
End to end view of JCL structure, datasets, and run control with conversion readiness.
Install on your servers or VMs. Optionally deploy inside Kubernetes or OpenShift. Use private cloud networks only.
Secure connectors to Snowflake, Databricks, BigQuery, and Redshift. Keys managed by you.
Project data stored inside your boundary. Logs and evidence live in your storage accounts.
Run on premise or inside your private cloud. No data leaves your boundary.
Role based access. SSO and MFA integration. Fine grained permissions.
Every action is logged. Evidence packs for internal and external reviews.
Templates, naming, and coding standards enforced at generate time.
Project backup and restore under your policies.
No shared services. Your environment only.
Inside your environment. On your hardware or private cloud. You hold the keys.
Python, PySpark, Snowpark, SQL, DBT models, and Databricks notebooks with comments and mapping sheets.
Validation reports and Data Matching show parity. Approval records provide evidence for audits.
Absolutely. Book a live walkthrough where we parse your own ODI mappings in real time and show you converted output, lineage, and validation results.
Integrate with Airflow, ADF, Composer, or Control M. Keep existing schedules or modernize them.
Begin with the pilot. Load a sample of ODI mappings or a Smart Export bundle. Review lineage, conversion, runs, and validation. Scale with confidence.
Questions about repository exports, knowledge modules, or topology? Tell us about your estate and target platform.
