Data stream
A data stream is an ordered sequence of data items or events made available incrementally for consumption or processing.
Data for All
A data stream is a continuous or successive flow of data generated by activities, observations, devices, or systems.
- How it is used
- Shows how ordinary activities and connected vehicles generate successive flows of data.
- How deep it goes
- Illustrates data streams through scenarios and named exchanges without explaining storage or processing internals.
- Examples
- Uses restaurant payments, hotel records, ticket scans, phone locations, and connected-car messages as concrete cases.
- What you can do after
- Evaluate/understand how repeated everyday actions create records that organizations may observe or share.
Kafka Streams in Action, Second Edition
An event stream is a continuous flow of independent records in which records sharing a key are not updates to one another.
- How it is used
- Supports real-time capture, transfer, storage, processing, and analysis of business and sensor activity.
- How deep it goes
- Defines event structure and independence, then relates records to table inserts without descending into implementation internals.
- Examples
- Uses purchases, web clicks, sensor transmissions, stock quotes, and package-custody scans as concrete stream records.
- What you can do after
- Evaluate event-driven data as timestamped independent records and distinguish streams from update-oriented records.
Read how Kafka Streams in Action, Second Edition explains data stream →
Apache Kafka in Action
A data stream is a continuous or unbounded sequence of data records processed as they become available.
- How it is used
- Supports near-real-time exchange, integration, and processing of records across distributed enterprise systems.
- How deep it goes
- Defines streams and explains partitions and offsets, but stops short of detailed stream-processing implementation.
- Examples
- Uses Kafka topics, database tables, and files as streams tracked through partitions, identifiers, timestamps, lines, or byte positions.
- What you can do after
- Evaluate how systems represent, partition, track, exchange, and process continuously arriving records.
Related concepts
- KStreamincludes · KStream is a Kafka Streams abstraction representing an unbounded stream of key-value records processed as independent events.
- sink nodeincludes · A sink node is a terminal or receiving component in a data or flow system that accepts incoming data, messages, or flow for consumption, processing, or storage.
- streamingused by · Streaming is the incremental handling of a sequence of data items, including their processing, transfer, or consumption as they become available.
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