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Short answer: an observability ontology—or, more precisely in the OpenTelemetry example, a set of semantic conventions—gives telemetry a shared meaning. A service catalog describes software entities, their relationships, and their organizational context. The first helps tools interpret and correlate signals; the second helps people discover what exists, who is responsible for it, and how it fits together.
They solve different problems, but they can work together. Telemetry can identify the service producing a trace, metric, log, or profile, and an engineer can use that identity to open the corresponding catalog record. That connection is an integration pattern inferred from the two models’ documented roles, not a feature that either model provides by itself.
The distinction at a glance
| Question | Observability semantics or ontology | Service catalog |
|---|---|---|
| Primary purpose | Make telemetry fields consistent enough for producers and consumers to interpret and correlate them. | Make software and organizational entities discoverable and show how they relate. |
| Main subject | Signals, operations, resources, attributes, names, units, meanings, and valid values. | Components, APIs, infrastructure resources, users, groups, and other catalog entities. |
| Typical users | Instrumentation authors, telemetry producers, platform engineers, and teams building observability tools. | Developers, service owners, platform teams, and responders looking for ownership or dependency context. |
| Typical question | What does this field mean, and can another tool consume it consistently? | What software exists, what does it depend on, and who is associated with it? |
| Primary risk when it drifts | A changed telemetry shape can make a consumer misread or reject data. | Stale entities or relationships can send people to the wrong owner, repository, API, or dependency. |
The distinction matters because neither model is a substitute for the other. A catalog can tell you that a payment component exists, but it does not define the meaning of every span attribute emitted by that component. Semantic conventions can make a span field understandable, but they do not provide a directory of teams, repositories, APIs, and operational contacts.
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What an observability ontology means in practice
Use the term carefully
Ontology is a broad term for a formal model of concepts and their relationships. The OpenTelemetry documentation reviewed for this topic defines semantic conventions, not one universal, all-purpose observability ontology. In that concrete standards example, the model is a shared vocabulary and a set of conventions for describing telemetry.
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OpenTelemetry states that semantic conventions define a common set of semantic attributes that provide meaning when data is collected, produced, and consumed. The benefit is a common naming scheme that can be standardized across a codebase, libraries, and platforms. In practical terms, a producer and a consumer can agree that an attribute has a particular name, type, meaning, and allowed values instead of negotiating a private interpretation for every integration.
What the conventions standardize
The conventions cover more than traces. OpenTelemetry documents conventions for traces, metrics, logs, profiles, and resources. Depending on the signal, a convention can describe items such as:
- Names and kinds for spans and other trace operations.
- Metric instrument choices, units, and the meaning of recorded measurements.
- Attribute names, data types, definitions, and valid values.
- Resource information that identifies the process, host, deployment, or service associated with a signal.
That shared vocabulary is what enables a collector, backend, dashboard, alert, or analysis tool to interpret data without a bespoke mapping for every application. It also makes correlation more reliable: a trace, metric, and log can refer to the same conceptual service and operation using compatible terms.
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Service identity inside telemetry
OpenTelemetry describes a service as a logical component that produces telemetry. Its service conventions distinguish a service namespace, a logical service, and an individual service instance. A namespace can group related services; the service identifies the logical component; and an instance identifies one running member of that component. The specification’s example treats a database and an HTTP server as distinct services within a blog-site system.
These distinctions prevent a common ambiguity. A deployment may run many copies of one service, and an organization may operate similarly named services in separate namespaces. A telemetry record that carries the appropriate identity can therefore support questions such as whether an error affects one instance, an entire logical service, or a group of services.
What semantic conventions do not provide
Semantic conventions do not automatically discover your repositories, assign ownership, or maintain a list of approved services. They describe how data should be named and interpreted. An implementation still has to emit the fields, preserve them through collection, and ensure that consumers understand the convention version in use.
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What a service catalog models
Entities and relationships
A service catalog is a directory and model of software and organizational entities. Backstage’s technical overview describes a catalog model based on entities. In that model, components are software pieces tracked in source control; APIs describe boundaries between components; resources are physical or virtual infrastructure needed to operate a component; and organizational entities include users.
The catalog’s value comes from the relationships among those entities. A component may provide an API, consume another API, depend on a database or queue, and be associated with a team or individual. A catalog can present those links so a developer or responder can move from a software name to its source, interfaces, dependencies, and organizational context.
The questions a catalog answers
- What components or services does the organization consider registered?
- Which APIs does a component provide or consume?
- Which infrastructure resources does it need to run?
- Which user or group is associated with the component?
- Where can someone find the component’s source and supporting documentation?
The exact entity kinds, fields, approval workflows, and ownership rules vary by implementation. Backstage is a concrete example, not a universal service-catalog schema. A different catalog may use different names or add entities for domains, products, environments, teams, or compliance controls.
What a catalog does not standardize
A catalog does not, by itself, standardize the meaning of every telemetry attribute. It may record that a component emits telemetry or link to an observability view, but it does not replace the conventions that define metric units, span kinds, attribute types, or valid values. Those remain concerns of the telemetry model and the systems that implement it.
Why the models are complementary, not competing
Think of the observability model as a language for machine-produced evidence and the catalog as a map of the software organization. The language makes a signal interpretable; the map makes the producer and its context findable.
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| Dimension | Observability semantics | Service catalog |
|---|---|---|
| Data flow | Travels with telemetry from instrumentation through collectors and backends. | Is retrieved and displayed as a directory of registered entities. |
| Granularity | Can describe individual spans, measurements, events, resources, and instances. | Usually describes durable software and organizational entities and their relationships. |
| Authority | Convention documents define meanings and valid shapes; instrumentation must apply them. | Catalog ownership and ingestion processes determine which entities and relationships are current. |
| Change mechanism | Schema or convention revisions can alter field names, types, meanings, or values. | Entity metadata and relationship updates change the directory view. |
How they meet in a practical integration
The following pattern connects the two models without confusing their responsibilities. It is an implementation approach derived from the documented roles of telemetry semantics and catalogs, not a prescribed OpenTelemetry or Backstage integration.
- Instrument the workload with consistent identity. Configure the telemetry resource so signals identify the logical service and, where relevant, its namespace and running instance. Use the semantic-convention definitions applicable to the signals you emit.
- Register the software entity. Create a catalog component representing the same logical service. Record its source location, provided or consumed APIs, infrastructure resources, and responsible group according to your catalog’s model.
- Choose a stable correspondence key. Decide which identity fields map a telemetry service to a catalog component. The key should distinguish similarly named services and remain stable when instances are replaced.
- Expose the catalog context from the incident workflow. A dashboard, trace view, alert, or runbook can use the correspondence key to present a link or lookup for the component record. The exact user interface is an integration choice, not part of the semantic convention.
- Test both directions. Start with a telemetry record and verify that it resolves to one intended catalog entity. Then start with a catalog component and verify that its identity finds the expected telemetry. Investigate duplicates, missing namespaces, and inconsistent environment labels before relying on the link during incidents.
A mapping example
| Concept | Telemetry side | Catalog side | Purpose of the mapping |
|---|---|---|---|
| Logical service | Service identity in the resource data | Catalog component | Moves from a signal to the software record. |
| Namespace or grouping | Service namespace where used | Catalog domain, system, or organizational grouping, if the implementation has one | Disambiguates similarly named services; the correspondence must be defined locally. |
| Running instance | Service-instance identity | Usually not a separate durable component; may be represented as deployment or runtime metadata | Preserves troubleshooting detail without creating a catalog record for every replica. |
| Dependency | Resource or operation references in telemetry | API or resource relationship | Combines observed behavior with declared architecture. |
The table illustrates a design choice: not every telemetry concept deserves a catalog entity. A replica that changes every few minutes may be valuable in a trace but too transient for a human-maintained directory. Conversely, an API contract may be important in the catalog even when no single telemetry event mentions it.
Implementing the connection without creating a second source of truth
Define ownership for each fact
Decide which system is authoritative for each field. Telemetry should be authoritative for what was emitted at runtime: operation timing, status, measurements, events, and the resource identity attached to that signal. The catalog should be authoritative for registered software context: component metadata, declared APIs, infrastructure relationships, and organizational associations.
Do not copy every catalog field into every telemetry record. Carry the minimum stable identity needed for correlation, then retrieve richer context from the catalog when a person needs it. This reduces payload size and avoids freezing organizational metadata into historical telemetry.
Normalize environment and naming rules
Many failed links are naming problems rather than technical failures. Establish conventions for case, separators, namespaces, and environment labels. Decide whether a development and production deployment share one logical component with environment metadata or are separate catalog entities. Document the rule so instrumentation and catalog ingestion make the same decision.
Handle missing and ambiguous identities
- Missing identity: reject the record from automatic linking or route it to an instrumentation-quality report; do not guess a component.
- Duplicate identity: add a namespace, environment, or another documented discriminator and repair the catalog entries.
- Renamed service: retain an alias or migration mapping long enough to cover historical telemetry and links in runbooks.
- Ephemeral instance: keep the instance detail in telemetry while mapping it to the stable logical component in the catalog.
Schema evolution and compatibility
OpenTelemetry’s telemetry-schema documentation warns that a source and a consumer depending on a particular telemetry shape can break when that shape changes without corresponding changes. A shape can change through renamed attributes, altered types, changed meanings, or other convention updates. Schema URLs identify the schema version associated with telemetry.
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Why this affects the catalog link
If the identity field used for correlation changes name or meaning, a dashboard may stop finding the catalog component even though both systems are healthy. A more subtle failure occurs when the field remains present but its scope changes—for example, a value that once identified a logical service is repurposed to identify an instance. The resulting links can be syntactically valid and operationally wrong.
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- Record which semantic-convention or schema version current producers and consumers expect.
- Review the schema change for renamed fields, type changes, valid-value changes, and altered definitions.
- Update instrumentation, collectors, storage queries, dashboards, alerts, and the catalog-lookup logic as one compatibility change.
- Run dual-read or dual-write handling when a transition requires old and new shapes to coexist. Keep the overlap period explicit.
- Validate historical queries separately; old telemetry may retain the previous schema and should not be silently reinterpreted.
- Remove compatibility code only after all producers and consumers have moved and the retirement date is recorded.
Choosing what to build first
Start with semantic conventions when interpretation is the problem
Prioritize an observability semantic model if teams cannot agree on metric units, attribute names, span meanings, or resource identity; if dashboards require repeated per-service transformations; or if traces, metrics, and logs cannot be correlated reliably. The first deliverable is a documented vocabulary applied by instrumentation and telemetry pipelines.
Start with a catalog when discovery is the problem
Prioritize a service catalog if engineers cannot identify who owns a component, where its source lives, which APIs it provides, or what infrastructure it depends on. The first deliverable is a maintained entity and relationship directory with an ingestion and ownership process.
Build the bridge when incident response is the problem
If both kinds of information exist but responders switch manually among tools, implement the identity mapping. Begin with a small set of high-value services, verify one-to-one resolution, and expand after measuring false matches and missing matches. Treat the mapping as integration logic with tests and change management, not as an informal convention known only to a few engineers.
Common mistakes and their fixes
Calling a field standard an ontology
Using ontology as a broad label can imply that one specification covers every operational concept. State which concrete model is in use—such as OpenTelemetry semantic conventions—and explain its scope. This keeps readers from assuming that the convention defines ownership, topology, or an organization-wide service inventory.
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A component page can document that a service emits a metric, but it should not be the only place where the metric’s unit and attribute semantics are defined. Keep signal meaning in the telemetry convention and link to the catalog for software context.
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Creating catalog records for every runtime object
Containers, pods, jobs, and replicas can be useful telemetry resources yet too volatile for durable catalog entries. Model them at the level that supports discovery and ownership, while retaining runtime detail in telemetry.
Assuming declared and observed relationships are identical
A catalog relationship is usually a declared or maintained model of architecture. Telemetry shows what a workload actually did during a time window. A missing catalog edge does not prove that no call occurred, and an old catalog edge does not prove current traffic. Present the two as complementary evidence.
Ignoring version metadata
When a convention or schema changes, a consumer that assumes the old shape can fail silently or produce incorrect links. Track schema URLs or equivalent version information and test consumers during upgrades.
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Imagine an engineer sees elevated latency in a trace. The telemetry identifies a logical service, its namespace, and an individual instance. The engineer first uses the semantic meaning of the span and timing fields to understand the operation and compare it with related signals. Next, the service identity resolves to a catalog component. The catalog supplies the owning group, source location, exposed API, and declared database dependency.
The two views answer different parts of the investigation. Telemetry can show whether latency is isolated to one instance, a particular operation, or a downstream call. The catalog can show who can change the component, which interface may be affected, and what other software is expected to depend on it. If the lookup returns multiple components, that is an identity-governance defect to fix rather than a reason to weaken the matching rule.
Governance checklist
- Document the semantic conventions and schema versions used by each telemetry pipeline.
- Define required service identity fields and how namespaces and instances are represented.
- Assign an owner for catalog entity accuracy and a process for updating relationships.
- Specify the stable key that maps telemetry services to catalog components.
- Test missing, duplicate, renamed, and retired identities.
- Separate durable catalog entities from short-lived runtime instances.
- Review declared catalog relationships against observed telemetry without treating either as the complete truth.
- Plan compatibility work before changing a field used by dashboards, alerts, or catalog lookups.
Bottom line: use each model for its own job
Use observability semantic conventions to give signals a consistent, machine-interpretable meaning. Use a service catalog to organize software entities and their organizational context. Connect them through stable service identity when a responder needs to move from runtime evidence to ownership and architecture. Keeping those boundaries explicit produces better telemetry, a more trustworthy catalog, and integrations that remain understandable as both models evolve.
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