What Is Real-Time Analytics?

Real-time analytics is the capability to query and analyze data that is continuously updated from source systems, delivering insights from events that occurred seconds to minutes ago — rather than from batch-loaded data that may be hours or days old. Real-time analytics enables operational use cases that require current data: fraud detection dashboards, live inventory monitoring, customer journey analysis, operational SLA tracking, and real-time A/B test result monitoring.

In the data lakehouse, real-time analytics is achieved by combining a streaming ingestion pipeline (Kafka → Flink → Bronze Iceberg tables) with a fast analytical query engine (Dremio with Reflections). The result is a lakehouse that serves BI queries on data that may be only 1–5 minutes old — sufficient for the vast majority of operational analytics use cases.

The Near-Real-Time Lakehouse Stack

The near-real-time lakehouse architecture:

  1. Event sources: Operational databases (via Debezium CDC), application services (direct Kafka producers), and external APIs publish events to Kafka topics
  2. Flink ingestion: Flink reads Kafka topics and writes to Bronze Iceberg tables with Iceberg's streaming write mode — committing new snapshots every 1–5 minutes (configurable)
  3. Dremio Reflection refresh: Dremio's Reflection refresh schedule detects new Iceberg snapshots and refreshes relevant Aggregation Reflections — typically completing in 15–60 seconds for small incremental snapshots
  4. BI queries served: BI tools querying Dremio receive results from the refreshed Reflection — data is 2–7 minutes old end-to-end
Near Real Time Lakehouse Analytics Stack diagram
Figure 1: Near-real-time analytics stack — Kafka, Flink, Iceberg, Dremio Reflections, BI tools.

When the Lakehouse is Enough vs Specialized OLAP

The lakehouse near-real-time approach is sufficient for most operational analytics use cases. Specialized real-time OLAP databases (Apache Druid, ClickHouse, Apache Pinot) provide true real-time query on data updated at very high frequency (millions of events/second, second-level freshness requirements), but at the cost of a separate system with its own storage, management, and data duplication.

For most organizations, the near-real-time lakehouse (2–5 minute data freshness on Iceberg + Dremio) eliminates the need for a separate real-time OLAP tier — reducing architectural complexity while delivering freshness levels that are operationally effective for fraud, operations, and business monitoring use cases.

Real Time Analytics Decision Framework diagram
Figure 2: Real-time analytics decision — lakehouse near-real-time vs specialized OLAP database.

Summary

Real-time analytics in the data lakehouse is achievable through the combination of streaming ingestion (Flink writing to Iceberg every few minutes) and fast query execution (Dremio serving Reflection-accelerated queries on the most recent snapshots). For most operational analytics use cases, this near-real-time lakehouse approach delivers sufficient freshness while maintaining the governance, openness, and analytical richness of the full lakehouse platform — without requiring a separate real-time OLAP database and its associated operational complexity.