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PipelineX by IO Pipelines

Your next platform.
Your existing logic.

PipelineX is a DataStage migration tool. Understand your jobs, translate their logic for Microsoft Fabric, Databricks or Snowflake, and see what needs your attention.

Explore the CatalogSee the translation↓
From source to possibility
Interactive example
Source

IBM DataStage

XML export
customer_orders5 stages
↳
The logic comes with you.

Joins, lookups, filters, and derivations—not just a new destination.

Read the sourceInspect the logicReview the output
Destination

Microsoft Fabric

</>
customer_orders.pyPySpark notebook

ⓘ Example only. Validate generated code against your source data before deployment.

Start with your DataStage export. See the jobs, dependencies, and migration work ahead.
Import & explore

AI Migration Assistant

Bring the job’s logic into the conversation.

Explain transformations, review saved code and plan validation with your selected DataStage job as context.

  • SQL & derivations
  • Schemas & sequences
  • Saved migration evidence
Explore the AI Migration Assistant See what it can read
PipelineX demo: the AI Migration Assistant focuses on a selected DataStage job, with questions about its flow, transformation rules and migration risks. The Catalog sits behind it.
Your selected job. Its context. A place to ask.

A clear path through
the migration work.

Start with what the source tells you. Build and test the next step with that context intact.

1

Understand the estate

Explore jobs, inspect derivations and follow dependencies. Find the logic and the questions behind the migration.

Explore PipelineX capabilities
2

Translate and review

Generate target code, read it alongside the source and work through the changes your environment requires.

Inspect function mappings
3

Test the result

Run the target against controlled inputs. Compare outputs and keep the evidence behind your acceptance decision.

Plan the migration

Where you go next.

Keep the business rules in view while choosing how they run on your next platform.

Microsoft Fabric

Bring DataStage logic into a Microsoft data platform. Plan notebook execution, lakehouse tables and operational ownership together.

Explore the migration path

Databricks

Translate transformation logic for Spark. Make runtime configuration, Delta tables and workload testing part of the move.

Explore the migration path

Snowflake

Rework DataStage processing for Snowflake. Review SQL and Snowpark logic alongside warehouse, role and data model decisions.

Explore the migration path

Make room for
the right questions.

Useful guidance, when you need the detail.

PlanningHow to plan a DataStage migration Source understandingWhat lineage can tell you What comes nextPrepare your data for AI workloads Browse all resources

One workspace, from scope to handover.

The project connects the people, source jobs and delivery plan. Explore how the work stays connected through migration and release.

Your starting point

Projects & teams

Connect the outcome, Catalog, owners and migration waves in one project.

Scope & assign
  • Source & intended scope
  • People & destination
  • Waves & milestones
Explore projects & teams
  1. 1Catalog

    Import diagnostics

    See what the XML captured, what needs investigation and which findings affect each destination.

    Jobs, sequences & dependencies
  2. 2Migrations

    Generate & review

    Group related jobs, choose a destination and keep code revisions, owners and review decisions connected to the source.

    Validate & accept
  3. 3Releases

    Releases & CI/CD

    Take prepared Fabric packages into repository review, checks and runtime validation.

    Release & hand over

Migration reports

Follow assigned work, blockers, current acceptance evidence and operational handover.

Explain job logic, discuss migration risks and review saved code. Explore the AI Migration Assistant.

Start with your estate.

Bring a representative job and the question you need answered.

Talk about your migration