R
Real-Time Data
Data that is delivered and processed immediately or with minimal delay as it is generated.
In-Depth Explanation
Real-time data refers to information that is available for use immediately (or nearly immediately) after generation. Real-time processing enables instant responses and decisions.
Real-time characteristics:
- Minimal latency (milliseconds to seconds)
- Continuous flow of data
- Processing as data arrives
- Immediate availability for use
- Often event-driven architecture
Real-time vs batch:
- Real-time: Process immediately, respond instantly
- Near real-time: Small delays (seconds to minutes)
- Batch: Process accumulated data periodically
Real-time use cases:
- Fraud detection
- Stock trading
- IoT monitoring
- Live recommendations
- Operational dashboards
- Alert systems
Technologies:
- Apache Kafka (streaming)
- Apache Flink, Spark Streaming
- AWS Kinesis
- Real-time databases (Redis)
- WebSockets for delivery
Business Context
Real-time data enables immediate action - critical for time-sensitive decisions like fraud prevention or dynamic pricing.
How Clever Ops Uses This
We implement real-time AI solutions for Australian businesses needing instant insights, from live monitoring to immediate recommendations.
Example Use Case
"Real-time fraud detection: analysing each transaction as it happens, scoring risk, and blocking suspicious activity within milliseconds."