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.
A NoSQL database stores and retrieves data without the fixed tables and relations of traditional SQL systems. It uses documents, key-value pairs, wide columns or graphs, and is often built to scale horizontally.
Stream processing is computing on data continuously as each event arrives, so results update within seconds. It is the engine behind real-time data streaming, replacing waits for scheduled batch runs.
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.
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