Real-Time Analytics delivers insights on data that is minutes or seconds old rather than hours or days old. The distinction matters enormously for time-sensitive decisions: fraud detection, supply chain disruption alerts, live customer behavior analysis, and operational dashboards monitoring infrastructure health all require data fresher than a nightly batch pipeline can provide.
The architecture for real-time analytics in a lakehouse combines a streaming ingestion layer with a query engine that can see newly committed data immediately. Apache Flink and Apache Kafka are the most widely deployed streaming components for this pattern: Kafka buffers the event stream, and Flink processes it and writes micro-batches to Apache Iceberg tables every 30 to 300 seconds. Because Iceberg's commit protocol makes each new batch of data atomically visible to readers, a query engine like Dremio can query data that is only a few minutes old without any additional pipeline steps.
Micro-Batch vs True Streaming
In practice, most "real-time" lakehouse analytics runs on micro-batch ingestion rather than true millisecond streaming. True streaming (where individual events are visible to queries the moment they are ingested) requires specialized systems like Apache Pinot, Apache Druid, or ClickHouse that maintain in-memory serving layers for recent data at the cost of higher infrastructure complexity and cost.
For most business use cases, micro-batch ingestion into Iceberg with a 60-second commit interval is sufficient. An operational dashboard showing "active orders in the last 5 minutes" with a 2-minute actual latency serves the business need without the overhead of a dedicated real-time serving system. When a use case genuinely requires sub-second latency on individual events (algorithmic trading, live fraud scoring at transaction time), it warrants the dedicated real-time system.
Real-Time Data for AI Agents
AI agents that monitor for operational anomalies or support real-time decision-making need access to current data. A micro-batch streaming architecture ensures that when an agent queries for the past hour's transaction volume or error rates, it is reading data that is at most a few minutes old. This freshness is what separates an agent that can detect and respond to an emerging issue from one that is working from data that is a day old and therefore already stale for any time-sensitive intervention.
