DataMove, DataVault and DataLens applied to an Agile programme: extraction that understands the Agile object model, versioned mapping rules, FBDI and REST generation, an attachment pipeline that survives a multi-terabyte vault, and reconciliation across every object family.
A general-purpose ETL tool can move rows between databases. An Agile to Fusion programme needs something that understands revision-controlled objects, a file vault and a target that imports through templates in a specific order.
Extraction has to follow the Agile object model rather than a table list, so that revisions stay attached to items, AML to manufacturer parts, redlines to changes and files to the objects that cite them. Transformation has to carry load-order dependencies alongside field mappings, because a structure cannot load before the components it references exist at the right revision.
Generation has to produce what Fusion actually accepts. That means the item, item structure and change order FBDI templates, populated correctly across their interface sheets, or REST payloads where a REST resource fits the object better. And it has to validate against target rules before submission rather than discovering the rejections after a load window has been spent.
Then there is the vault. Files have to be extracted with checksums, staged into the target document store with stable document identity so revision-specific versions stay distinct, and reconciled by count and integrity afterwards. Very few generic tools do any of this, which is why most Agile programmes that start with one end up writing scripts around it.
The things that separate PLM migration tooling from generic ETL.
Reads Agile through its relationships, so the extracted record is navigable rather than a set of disconnected tables.
Load order derived from the object graph, so structures, AML and changes land in an order that resolves.
Item, item structure and change order templates populated across their interface sheets and validated before submission.
Vault extraction with checksums, staged upload, document identity assignment and count reconciliation.
Completed changes sequenced by effective date to rebuild revision history rather than flattening it to current state.
Counts, control totals and record-level tracing produced by the run, not assembled afterwards by hand.
Configured against your instance, then run repeatedly.
Read-only connection to the Agile database and vault, automated profiling of configuration, volumes and data condition.
Crosswalks and transformation rules captured as versioned configuration rather than as code, reviewable by the business.
FBDI and REST payloads produced and validated against target rules in staging before any import job runs.
Sequenced loading with rejections captured as exceptions, and reconciliation produced per cycle by the run itself.
The same configuration run at full volume repeatedly, so cutover executes known behaviour rather than new behaviour.
The tooling is best understood by its outputs rather than its architecture. At every stage it produces something a person can inspect, which is what makes the programme auditable rather than opaque.
Each artefact below is generated by the run itself rather than assembled by hand afterwards.
Three ways to build the pipeline, with different half-lives.
Viable for a pilot. At production volume the rework dominates, and nothing produced is reusable next cycle.
The ETL tool moves rows; the scripts handle Agile semantics, the vault and FBDI. The scripts become the real system, and nobody owns them.
Object-model extraction, dependency sequencing, FBDI generation, attachment handling and reconciliation in one governed pipeline.
Three products working as one pipeline. DataMove handles Agile extraction, versioned transformation, FBDI and REST generation, attachment processing and sequenced loading. DataVault retains the source snapshot, transformation lineage, reconciliation results, exception register and audit evidence for every cycle. DataLens provides the dashboards, data-quality metrics and cutover-readiness analytics that show whether rehearsals are converging.
Yes. It produces the item, item structure and change order templates that Oracle's PLM import processes consume, populated across their interface sheets, and validates the output against target business rules in staging before anything is submitted. Where a REST resource fits an object better than a file import, or where incremental loads are needed after the bulk move, it generates REST payloads instead.
Generic ETL moves rows between systems and knows nothing about revision-controlled product records, file vaults or import templates. The Agile-specific work is exactly what it leaves to you: extracting through the object model so relationships survive, deriving load order from the object graph, populating FBDI templates correctly, handling vault files with document identity, and replaying completed changes in effective-date order. Teams that start with generic ETL usually end up writing that layer as scripts, and the scripts become the system nobody owns.
Custom subclasses, custom attributes including Page Two and Page Three fields, and custom relationship types are discovered during profiling and handled through configuration rather than code changes. That matters practically: a business user can review what happens to an attribute without reading a script, and a mapping change is a versioned configuration amendment rather than a code release.
No. The same extraction and transformation layer feeds archival, historical reporting and ongoing integration, which is usually why it stays in place after go-live. The archive is built from the same extract, the historical reporting runs over it, and any coexistence integration during a phased transition uses the same identity crosswalk. One pipeline serves the whole lifecycle rather than being retired at cutover.
Either model works. SyntraETL can run the pipeline as a delivered service, with your team providing the business decisions and validating outputs, or your team can operate it with our support after configuration and handover. The second is more common where the same organisation has further legacy systems to retire, because the capability transfers to the next programme rather than being rebuilt.
We will connect read-only to a copy of your Agile instance, run profiling, and show you real extraction, mapping and FBDI output from your own records rather than a demo dataset.