Not every Agile programme ends at Fusion. SyntraETL moves Agile product data to Oracle Fusion Cloud PLM, to another PLM platform, into a data lake, or out as structured files — with the same extraction, transformation and reconciliation controls whichever target you pick.
The hard part of an Agile migration is not reading the database. It is keeping seven interdependent object families consistent with each other while every one of them changes shape on the way to the target.
Items and their revisions carry the attributes, including the Page Two and Page Three fields that most long-lived Agile instances use for business-critical data under generic names. Structures carry components, quantities, effectivity, reference designators and substitutes, each pointing at other items that are themselves revision-controlled. Manufacturers and manufacturer parts sit behind every AML entry.
Change orders tie the whole thing together. An ECO is not a document about a change; it is the record of which objects changed, when the change took effect, and what the redline showed. Strip the changes out and the product record loses its audit trail. Replay them out of order and the revisions that result are wrong.
SyntraETL treats these as one dependent set. Extraction follows the relationships, transformation applies rules that know which objects reference which, loading runs in an order the dependencies allow, and reconciliation checks each family independently as well as the links between them.
The difference between a migration that imports and one that reconciles.
Volumes, cardinality, orphaned components, duplicate manufacturers and unused custom attributes measured against the real instance before a single rule is written.
Class, lifecycle, UOM and code-set translations held as versioned lookups, so the same input produces the same output in every cycle.
Renumbering, padding and case rules applied consistently across structures, AML, changes and attachments, not just the item master.
Name variants consolidated to one governed record before AML is rebuilt against it, which is the only way the AML counts reconcile.
Overlapping revision effectivity resolved during transformation, since most targets reject overlapping ranges outright.
Every rejected or unmapped object is listed with its reason, owner and status, rather than disappearing between cycles.
Cutover should be the fourth time the load has run, not the first.
Full extract to staging with a signed snapshot, so every later comparison is against a fixed reference rather than a source that has moved on.
Rules applied end to end, output validated against target business rules before anything is submitted for import.
Import jobs run in dependency order. Rejections become tracked exceptions with owners, not a line in a log.
Counts and record-level traces compared; the gaps drive mapping changes, and the mapping changes are versioned.
Full-volume run against the clock, so the cutover window is a measured number rather than a hope.
Across programmes the failures cluster. Almost none are exotic, and almost all are visible in profiling weeks before they cause a load to fail, which is why profiling earns its place at the front of the schedule.
Each of the six below has a known fix. What determines whether a programme absorbs them in week four or discovers them in rehearsal three is whether anyone looked.
The same extraction and reconciliation controls behind each target.
The most common target. Loaded through Oracle's item, item structure and change order FBDI templates and REST resources, then reconciled by object.
Agile data extracted and transformed into the target's own import format, with the same profiling, crosswalks and reconciliation applied.
Governed product data published for analytics, with relationships preserved as keys rather than flattened into one wide table.
Items and item classes with their standard attributes plus Page Two and Page Three fields, item revisions with lifecycle phases and statuses, multi-level bills of material with components, quantities, effectivity, reference designators and substitutes, manufacturers and manufacturer parts, Approved Manufacturer List entries, engineering changes including ECRs, ECOs, ECNs, MCOs and SCOs with their affected items and redlines, attachments with their metadata and revision associations, and supplier, cross-reference and custom relationship data. Coverage for any given instance depends on its version, configured classes and custom attributes, which is what profiling establishes.
Yes. The constraint is sequencing rather than depth. Every component on a structure is itself a revision-controlled item, so components must exist at the correct revision before the parent structure referencing them can load. SyntraETL resolves the dependency graph during transformation and loads bottom up, which is why multi-level structures land in one pass instead of needing repeated retries to catch missing references.
They are discovered, not guessed. Profiling reports which flex fields are populated, on which classes, with what cardinality and what value distribution. That evidence drives a business conversation about what each field actually means, because the field names themselves rarely say. Each one is then mapped to a target attribute, to an extensible or descriptive flexfield, defaulted, or explicitly archived rather than migrated. The decision is recorded either way.
No, and most organisations should not. Carrying every superseded revision and closed change into a new PLM adds licence cost, load time and noise without adding operational value. The usual split moves current operational product information and retains the rest in an archive that stays searchable. What belongs on each side is a scoping decision taken during discovery, with volumes attached so the trade-off is concrete.
It is surfaced early and fixed deliberately. Profiling finds duplicate and variant manufacturer names, orphaned components referencing items that no longer exist, invalid or missing references, attributes populated on some revisions and not others, and effectivity ranges that overlap. Each becomes a remediation item with an owner. Cleansing rules run inside the transformation layer so they are applied identically in every cycle, rather than being hand-fixed in a spreadsheet that the next extract overwrites.
Yes, and a two-phase go-live is common. Phase one loads the latest released revisions of top-level items and their components plus the current structures, which is enough to operate. Phase two follows with historical component revisions, substitutes and the completed change orders that carry redlines. Splitting it shortens the cutover window and moves the slowest, highest-volume work out of the critical path.
Send us your Agile version, rough item and revision counts, structure depth and vault size. We will come back with an object inventory, the likely mapping effort and where the risk actually sits.