Time-series systems such as TimescaleDB, InfluxDB, Prometheus with VictoriaMetrics, and QuestDB treat time as a primary axis. They partition metrics and sensor data into time ranges, index label sets, and apply compression tuned for ordered values. These design choices directly shape ingest capacity, cardinality limits, and query latency.

Go, Flink, and Spark represent different stages of real-time processing. Go handles ingestion and validation at millisecond latency, Flink maintains continuous event-time computation with state and recovery, and Spark performs large-scale analytical aggregation or feature building over recent and historical data. The choice depends on latency targets, consistency requirements, and the scope of computation.