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.
Event-driven architecture is a design approach in which services communicate by publishing and reacting to events. Each event records that something happened, so services no longer call each other directly and wait for answers.
Apache Kafka is an open-source distributed event streaming platform. It stores streams of records in durable, ordered logs that many applications can publish to and read from independently.
Batch processing is handling data in scheduled groups, such as hourly or nightly runs, rather than processing each record the moment it arrives. Payroll runs, overnight warehouse loads and monthly billing are common examples, where completeness matters more than instant results.
Change data capture detects inserts, updates and deletes in a database and streams those changes to other systems. Targets stay in sync without repeated full reloads.
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.
WebSockets is a protocol that keeps a single, long-lived, two-way connection open between a client and server. Either side can send messages at any time, which suits real-time features.
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