Real-time Analytics

Dashboard refreshes should happen in milliseconds, not every morning at 9 AM.

Traditional Business Intelligence (BI) is built on Looker or Tableau reading from a Data Warehouse that updates once a night (Batch Processing). This is fine for monthly reports, but useless for operational monitoring. If a server is down or a fraud attack is happening, you need to know now. Real-time analytics shifts the paradigm from "Historical Reporting" to "Operational Intelligence".

The Streaming Stack

The heart of a real-time stack is the Event Bus, typically Apache Kafka or Redpanda. Applications emit events ("User Clicked", "Order Placed") into Kafka topics immediately. These are not stored like a database, but streamed like a log.

To process this stream, we use tools like Apache Flink or ksqlDB. These engines allow us to run SQL queries on moving data. We can calculate "average cart value in the last 5 minutes" continuously. The result is then pushed to a fast serving layer—often a time-series database (Redis, Druid, or ClickHouse), or directly to the frontend via WebSockets.

Push vs Pull

The final mile is the user interface. Old dashboards "Poll" the API every 30 seconds. Real-time dashboards use "Push" technology (WebSockets or Server-Sent Events). The server pushes the new number to the browser the millisecond it changes. The chart animates live.

Real-time is harder. It involves handling out-of-order data, duplicate events, and backpressure. But for use cases like Logistics tracking, Stock trading, or cybersecurity, it is the only viable option. The business moves in real-time; your data should too.