Stages, sequences & containers — lineage and automated conversion.10X SPEED
Everything MigryX ingests from DataStage, the engine that converts it, and every target it produces.
COMPASS scans your DataStage footprint — .dsx job exports, .isx interchange, parallel and server jobs, job sequences, shared containers, and stage-level dependencies — then classifies each asset as MIGRATE, ARCHIVE, or DELETE. Convert only what matters. Archive the rest. Delete the noise.
DataStage estates grow for years — overlapping parallel jobs, copy-pasted server jobs, duplicate DSX drops, stale sequences, and unclear container reuse. Moving off Designer and Director without inventory is high risk.
Hundreds of thousands of files with no visibility into what's actively used.
Legacy licenses and storage costs growing — much of it stale, unused data.
Complex web of programs and datasets. What connects to what is a major challenge.
Can't migrate everything at once. Need data-driven decisions, not guesswork.
Manual analysis takes months. Business needs a clear, prioritized plan — fast.
Which assets drive value? Which are technical debt? Unknown without automation.
One system to scan, score, and classify every DataStage asset automatically — from DSX/ISX inputs and Designer project folders to Director job runs and logs.
Automated scanning of every file, every program, every execution log. Build a comprehensive catalog with metadata, dependencies, and usage patterns.
Intelligent scoring engine evaluates each asset on multiple criteria. Get clear MIGRATE, ARCHIVE, or DELETE decisions with phased priorities.
Parse code to extract relationships and external references. Understand full impact before making any change.
Identify archival and cleanup opportunities. Project savings across storage, licensing, and cloud migration costs before committing.
Multi-factor scoring balances cost reduction with migration speed
What teams discover when they inventory DataStage (.dsx / .isx, parallel & server jobs) before a platform change
High-performance parallel processing. Hashing detects duplicates automatically.
Parses DSX/ISX job definitions, stage graphs, Transformer expressions, and embedded SQL with high accuracy.
Parses execution logs to track usage patterns, errors, and performance.
Evidence-based decisions: which assets are active vs. never accessed.
Multi-factor algorithm: usage, recency, size, complexity, execution quality.
Priority-based phase assignment optimized for risk and business continuity.
Executive dashboards, migration plans, cost projections, exportable data.
All analysis stored structurally. Run custom queries for any ad-hoc need.
From deployment to actionable migration intelligence — fast.
Point COMPASS at Designer project exports, DSX (.dsx) bundles, ISX (.isx) drops, or source-control checkouts. Automated scanning begins immediately.
Parse job XML, stage links and constraints, analyze Director logs, build a dependency graph. Score every job for priority.
Interactive reports show exactly what to migrate, archive, or delete.
Follow the phased plan. Track progress and realize immediate cost savings.
Scan IBM DataStage parallel and server jobs from .dsx exports, .isx interchange, and Designer project metadata to build a complete inventory. Discover Transformer and Lookup chains, shared containers, partition/sort keys, and parameter propagation — plus fan-in or fan-out hot spots. Produce visual lineage and impact maps that guide the entire migration.
Parser conversion turns DataStage job logic (from DSX/ISX) — Transformer, Lookup, Merge, Copy, and connector stages — 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 parallel and server jobs from DSX and ISX inputs, map complexity across stages and containers, and flag readiness. Get clear scope, a prioritized plan, safer cutovers, and faster production.
Visualize stages, links, constraints, and SQL across jobs, sequences, and shared containers. Speeds impact checks, lowers migration risk, supports audits, and proves outputs match.
Convert DataStage jobs into Python, PySpark, Snowpark, or SQL with matched outputs — preserving Transformer derivations, lookups, and pushdown SQL where it makes sense. 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 DataStage jobs and the new target code, detailing stage types, job parameters, environment variables, and cross-job 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 IBM DataStage assets — DSX (.dsx) job exports, ISX (.isx) interchange, parallel and server jobs, job sequences, shared containers, and Designer/Director 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.
Deploy MigryX yourself, run the pilot on your own schedule, and keep every DataStage asset (DSX/ISX) and converted artifact inside your network.
No consultant. No RFP. Install MigryX in your environment, convert real DataStage jobs from DSX/ISX, and see results — on your own terms.
| 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 are fully self-service — install, run, and evaluate independently inside your own environment. No external consultants required. 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 DataStage jobs 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 DataStage jobs (DSX/ISX). Review lineage, conversion, runs, and validation. Scale with confidence.
Questions about DataStage migration? Tell us about your estate and target platform.
