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Axiom helps you leverage the power of timestamped machine data. A common use case of machine data is observability (o11y) in the field of software engineering. Observability is the ability to explain what’s happening inside a software system by observing it from the outside. It allows you to understand the behavior of systems based on their outputs such as telemetry data, which is a type of machine data. Software engineers most often work with timestamped machine data in the form of logs, metrics, and traces. However, Axiom believes that machine data reflects a much broader range of interactions, crossing boundaries from engineering to product management, security, and beyond.

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Types of machine data in observability

Traditionally, observability has been associated with three pillars, each effectively a specialized view of machine data:
  • Logs: Logs record discrete events, such as error messages or access requests, typically associated with engineering or security.
  • Traces: Traces track the path of requests through a system, capturing each step’s duration. By linking related spans within a trace, developers can identify bottlenecks and dependencies.
  • Metrics: Metrics quantify state over time, recording data like CPU usage or user count at intervals. Product or engineering teams can then monitor and aggregate these values for performance insights.
Axiom supports all three types of data, allowing for fine-grained, efficient tracking across the three pillars.

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Logs, traces, and events

Axiom excels at collecting, storing, and analyzing timestamped event data. For logs and traces, Axiom offers unparalleled efficiency and query performance. You can send logs and traces to Axiom from a wide range of popular sources. For more information, see Send data to Axiom.

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Metrics

For metrics, Axiom offers a dedicated metrics datastore with query performance optimized for high-cardinality time-series data. You can send OpenTelemetry metrics to Axiom and query them using MPL (Metrics Processing Language). For more information, see Metrics.