Observability is the ability to understand what a running system is doing, and why, from the data it emits. That data is mainly logs, metrics and traces, which let engineers answer new questions without shipping new code.
A service level objective (SLO) is an internal reliability target for a service, such as the share of requests that must succeed quickly. It defines what good enough looks like for users over a rolling window.
Site Reliability Engineering (SRE) is an approach to running production systems that applies software engineering to operations. It sets measurable reliability targets and replaces repetitive manual work with automation.
A time-series database is built to store and query measurements indexed by time, such as metrics, sensor readings or prices, at very high write volume.
An audit trail is a chronological, tamper-resistant record of who did what, when and to which data or system. It lets organisations reconstruct events, prove compliance and investigate errors, fraud or security incidents.
A canary release ships a new version to a small share of users or servers first. The rollout widens only if error rates, latency and other health metrics stay within agreed limits.
Data observability is continuous monitoring of data pipelines and datasets so that problems are caught before users notice. It watches freshness, volume, schema, distribution and lineage.
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