Accelerate the

exit

[ DataStage ]

IBM's DataStage is a bit like an old diesel engine : powerful, robust, but expensive to maintain, complex to configure , and not really designed for modern cloud architectures.

To complete the picture, young computer scientists don't really want to get involved in it.

The future of this ETL seems uncertain at present.

Faced with this, the most commonly implemented option for DataStage migrations remains "copy-paste" (Lift & Shift) to one target or another.

And here, three major problems arise:

After 10, 15 or 20 years of development, DataStage flows look (perhaps ) like a huge "plate of spaghetti": technical debt, accumulated complexity... good luck.
Migrating to "T&M" mode is long, it's expensive... and on top of that, it's uncertain.
The chosen target technology, however modern it may be today, will sooner or later end up burdened with debt, inflationary licensing costs — in short, almost a problem in the making.

This is where {openAudit} our software comes in:
to automate the migration to a universal technical base, but also to precede it with a real "detox cure".

3 steps:

1. Turn on the light!

[ Mapping ]

We don't guess. We analyze.

{openAudit} scan:

ISX files,
Shared Containers,
And even the famous RCP (Runtime Column Propagation), etc.

The result: a complete data lineage, from the source—and potentially all the way to the dashboard—without any breaks, continuously replayed.
A precise inventory of what's really hidden in the data, to understand before acting.

Some examples of output:

1. "Job Graph" view in DataStage

Understanding the overall job flow -

This view allows you to visualize the complete pipeline of a DataStage job in graphical form.

Each node represents a step in the processing (source, transformation, join, aggregation, target table) and the links materialize the data flows.

This is the overall understanding view of the DataStage job and its execution logic.

2. "Job Lineage" view in DataStage

Trace each field, from the target to the sources -

This view presents the detailed downward traceability of the data, field by field.

It allows you to trace the origin of a field from the target table back to the sources, transformation after transformation, by precisely identifying the rules applied.

This is the audit and fine-grained explainability view in DataStage

3. “Data Lineage multi techno” view

Connecting technologies and understanding information usage -

A macroscopic and cross-sectional view of data flows, beyond DataStage.

It connects the different drivers of transformation and business uses within a single interface, and highlights the actual consumption of information.

This is the holistic view of data flows and uses .

2. We are getting rid of a large part of the jobs [ Rationalization ]

Why migrate something that is no longer useful?

By cross-referencing data lineage with actual usage from logs , {openAudit} makes it possible to identify "dead branches", i.e., flows that "build" data that is never used.

The observation: on average, 40 to 60% of a DataStage asset can be deleted before the migration even begins.

3. We translate into intelligible, transposable SQL

[ Migration ]

{openAudit} does not produce unreadable machine code.
It translates complex logic (CDCs, Before/After statements, Filters, Transformations, etc.) into standardized , factored SQL:

Each request is commented on, linked to its job , its internship and its original DataStage project.
The code is flat, readable, explainable - therefore understandable by the teams.
When logic cannot be translated into SQL, it is isolated in an open , clearly identified toolbox.
The same applies to gap analysis and double running: equipped, documented, therefore mastered!