Log aggregation is how you turn logs scattered across hundreds of machines into one searchable place.
Log aggregation is the practice of collecting log data from many sources, servers, containers, applications and services, and bringing it into a central system where it can be searched, filtered, correlated and analysed together, instead of sitting in isolation on each machine.
In a distributed system, logs are produced everywhere: dozens of services, hundreds of containers, ephemeral pods that vanish. You cannot log into each one to investigate an incident, and even if you could, the answer usually spans several of them. Aggregation solves this by shipping every log to one place, so you can search across the whole estate, correlate a log line with the trace it belongs to, and keep the evidence after the container that produced it is gone.
A collector or agent (the OpenTelemetry Collector's filelog receiver, Fluent Bit, and similar) reads logs from files, containers or syslog, a pipeline parses and enriches them (adding structure, service and trace identifiers), and they are stored and indexed in a backend for query. The valuable step is enrichment: attaching consistent fields and trace IDs so aggregated logs can be filtered precisely and joined to metrics and traces.
Kubernetes is where log aggregation stops being optional. Pods are ephemeral: when one crashes and restarts, its local logs can vanish, taking the evidence of the failure with them. Containers are many and short-lived, and no human can watch them individually. The standard pattern is a lightweight agent running as a DaemonSet on every node, Fluent Bit or an OpenTelemetry Collector, that tails container logs and ships them to a central store before the pod disappears. Without aggregation, a Kubernetes incident is often impossible to investigate after the fact, because the logs that would explain it are already gone.
Aggregating logs is easy to start and expensive to scale, because log volume grows with traffic and every gigabyte has to be shipped, stored and often indexed. The teams that do it well are deliberate about what they aggregate and how they keep it: dropping high-volume, low-value lines at the edge, sampling where appropriate, and tiering older logs into cheaper storage. Aggregation without a cost strategy is how observability bills spiral, which is why modern pipelines filter and shape logs on the way in rather than storing everything and paying to search it.
Ops Singularity's TelemetryOps pillar aggregates logs at petabyte scale over OpenTelemetry, correlated with metrics and traces, and Sentinel AI acts on what they reveal through governed Action Tickets, so aggregated logs lead to resolution, not just search.
Aggregation is the collecting-into-one-place part; log management is the whole lifecycle around it, including parsing, storage, retention, access and analysis.
Even a small distributed or containerised app benefits, because logs are ephemeral and spread across containers. For a single server it matters less.
Ops Singularity turns open telemetry into autonomous, governed resolution. See it on your own stack.