Real-Time Analytics

Real-time analytics processes and analyses data as events occur, providing instant or near-instant insights. It enables organisations to respond to situations as they unfold rather than discovering them hours or days later in batch reports.

Real-time analytics spectrum:

• True real-time: Sub-second latency (fraud detection, algorithmic trading)
• Near-real-time: Seconds to minutes latency (operational dashboards, alerts)
• Micro-batch: Minutes to hours latency (frequently updated reports)

Common real-time analytics use cases:

• Fraud detection: Identifying suspicious transactions as they occur
• Website/app monitoring: Tracking user behaviour and performance in real time
• IoT monitoring: Processing sensor data for equipment and environment monitoring
• Supply chain visibility: Real-time tracking of shipments and inventory
• Social media monitoring: Tracking brand mentions and sentiment as they happen
• Operational dashboards: Live views of business process performance
• Alerting and notification: Triggering alerts when conditions meet defined thresholds

Real-time analytics architecture:

• Event streaming: Apache Kafka, AWS Kinesis, Google Pub/Sub for data ingestion
• Stream processing: Apache Flink, Apache Spark Streaming for data transformation
• Real-time databases: ClickHouse, Apache Druid, Pinot for fast querying
• Real-time dashboards: Grafana, custom WebSocket dashboards
• Alert engines: PagerDuty, Opsgenie for notification management

Considerations for real-time analytics:

• Not all analytics needs to be real time - match latency to decision cadence
• Real-time infrastructure is more complex and costly than batch
• Data quality must be ensured in real time (harder than in batch)
• Consider the "right-time" approach - fast enough for the decision at hand