Real-time processing
Real-time processing handles incoming data or events with sufficiently low delay to produce results or responses as they occur or soon afterward.
Kafka for Architects
Real-time event processing is the handling of events with sufficiently low latency to support immediate or near-immediate reactions.
- How it is used
- Enables prompt reactions to events through low-latency delivery to independent processing applications.
- How deep it goes
- Explains the broker–application boundary and batch trade-offs, but defers detailed Kafka Streams processing design.
- Examples
- Uses independent order consumers, a fraud-detection transaction flow, and daily data-warehouse uploads to contrast processing modes.
- What you can do after
- Evaluate/understand when low-latency streaming fits a workflow and where processing logic and batch-related costs reside.
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Apache Kafka in Action
Real-time data processing handles data with sufficiently low latency for systems to react soon after events occur.
- How it is used
- Supports timely updates and reactions to enterprise events instead of waiting for scheduled batch runs.
- How deep it goes
- Explains the batch-versus-stream distinction and the trade-off between prompt, changeable results and definitive batch results; does not specify latency targets.
- Examples
- Uses delayed account balances and parcel tracking, alongside online-store price changes, stock updates, and overnight transaction reports.
- What you can do after
- Evaluate/understand when prompt event handling is preferable to scheduled processing and when final batch results remain useful.
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Kafka Streams in Action, Second Edition
Real-time processing is the processing of data with sufficiently low delay to support decisions while events are occurring.
- How it is used
- Supports time-sensitive decisions by processing business, device, and customer events as they occur.
- How deep it goes
- Explains low-delay processing, ordered storage, analysis, and replay, but does not detail implementation APIs or deployment mechanics.
- Examples
- Uses credit-card purchases, manufacturing temperature sensors, business routing, and an e-commerce sale surge.
- What you can do after
- Evaluate incoming events promptly and use stored event histories to investigate outcomes and improve future handling.
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Financial AI in Practice
Near-real-time processing handles incoming data with short delays rather than waiting for a scheduled batch.
- How it is used
- Supports instant financial decisions by merging selected incoming data with short delays while retaining scheduled processing for analytics.
- How deep it goes
- Contrasts nightly or weekly batches with partial real-time merges, but does not detail stream-processing implementation.
- Examples
- Uses BNPL or card approvals streamed through Kafka and bureau checks merged for instant lending decisions.
- What you can do after
- Evaluate when to use partial near-real-time merges rather than relying only on daily or weekly pipelines.
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Related concepts
- batchingcontrasts with · Batching is the grouping of multiple data items, requests, or operations so they can be processed together as a unit.
- Apache Kafkaused by · Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
- microservice architectureuses · Microservice architecture is a software architectural style that organizes an application as independently deployable services with focused responsibilities that communicate through defined interfaces.
- customer data platformimplemented by · A customer data platform is a platform that unifies customer data from multiple sources into persistent customer profiles for activation and analysis.
- streamingused by · Streaming is the incremental handling of a sequence of data items, including their processing, transfer, or consumption as they become available.
- Azure Functionsused by · Azure Functions is Microsoft's managed serverless compute platform for running event-driven or scheduled application code without managing the underlying servers.
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