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Artifact Fields and Analysis Options

What this page helps with

Artifacts are where users inspect concrete integration objects in detail. This page explains which visible fields matter most and which analysis options are useful in practice.

Main table fields

The artifact list usually highlights:

  • name
  • type
  • package
  • version
  • deploy status
  • deployed by
  • status
  • label or tag

These fields help answer the first operational questions:

  • which artifact is this
  • where does it belong
  • is the visible version plausible
  • is it deployed as expected

Important detail fields

When a row is expanded, users typically look for:

  • identifiers
  • artifact type
  • package relation
  • version and deployment timestamps
  • sender and receiver where relevant
  • last content-check information

These values are most useful when troubleshooting inconsistencies or comparing environments.

Metadata

Metadata is helpful when the visible fields are not enough.

Typical use:

  • compare technical detail between artifacts
  • verify whether a loaded object contains the expected structure
  • export or copy metadata for discussion or follow-up analysis

Where-used analysis

Where-used information is one of the most valuable artifact tools because it helps estimate impact before a change.

Use it when you need to know:

  • where a value or object is referenced
  • which iFlows are affected by a change
  • whether a planned adjustment has a wider consequence

Content checks and hashes

Content-check values help validate whether an artifact changed and when the last verification happened.

Treat these values as analysis aids, especially when:

  • versions look inconsistent
  • content may have changed unexpectedly
  • you need to compare known history with the current state

History and older versions

Older-version information is helpful when teams need to understand:

  • whether a regression appeared after a change
  • how many historical versions exist
  • whether an artifact has changed recently

Common mistakes

  • reading deploy status without also checking version and package
  • using metadata too early when the main table already answers the question
  • skipping where-used analysis before productive changes