Real-Time Data Stream
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A Real-Time Data Stream is a data stream that delivers continuous data flow with minimal latency for immediate processing and time-sensitive responses within specified deadlines.
- AKA: Live Data Stream, Streaming Data, Real-Time Stream.
- Context:
- It can typically deliver Data Events with low latency.
- It can typically maintain Temporal Ordering through sequence guarantees.
- It can typically support Continuous Processing via stream processors.
- It can typically handle High Throughput through parallel processing.
- It can typically ensure Data Freshness via time constraints.
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- It can often provide Backpressure Handling for flow control.
- It can often support Stream Partitioning for scalability.
- It can often enable Stream Replay for recovery purposes.
- It can often implement Quality of Service for delivery guarantees.
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- It can range from being a Low-Volume Real-Time Data Stream to being a High-Volume Real-Time Data Stream, depending on its data throughput.
- It can range from being a Structured Real-Time Data Stream to being an Unstructured Real-Time Data Stream, depending on its data format.
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- It can be processed by Stream Processing Engines for real-time analytics.
- It can utilize Message Queue Systems for data transport.
- It can employ Event-Driven Architecture for reactive processing.
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- Example(s):
- System Real-Time Streams, such as:
- Application Real-Time Streams, such as:
- Infrastructure Real-Time Streams, such as:
- ...
- Counter-Example(s):
- Batch Data File, which contains static dataset rather than continuous flow.
- Historical Data Archive, which stores past records rather than real-time data.
- Periodic Data Update, which provides scheduled refresh rather than continuous stream.
- See: Stream Processing, Real-Time Computing, Event Streaming, Data Pipeline.