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Data plane

As explained in Cloud Observability in Action

The data plane is the set of worker resources that execute application workloads and carry operational traffic in a distributed system.

The data plane is the set of worker resources that execute application workloads and carry operational traffic. In Kubernetes, its components launch, health-check, report, and terminate containers running in pods, while the control plane manages cluster resource state. section 3.2.3

The boundary is practical rather than uniform: load balancers, networked storage, APIs, and formats can differ across cloud providers. Operating the data plane also exposes logs and metrics that require deliberate collection, routing, storage, and querying. section 3.2.3

Kubernetes workload signals

A deployed frontend runs in the data plane while its container logs are streamed with kubectl logs. Operators can inspect application output alongside Kubernetes metrics such as API-server latency. section 3.2.3

How Cloud Observability in Action covers it

How deep it goes
Explains workload responsibilities and observability implications, but stops short of detailing individual worker components.
Examples
Uses frontend container logs, kubectl logs, API-server metrics, and a Kubernetes signal-source architecture figure.
What it assumes
Assumes familiarity with Kubernetes workloads and containers while distinguishing the control plane from the data plane.
What you can do after
Operate/configure it by identifying workload signals and planning their collection, routing, storage, and querying.
How it connects
Connects Kubernetes workloads with the control plane, containers, pods, logs, metrics, traces, load balancers, and networked storage.

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This explainer was generated by Manning's knowledge graph from the text of Cloud Observability in Action. The section links point to the parts of the book it draws on.