Real-time Analytics

Real-time Analytics is about turning fresh events into decisions while they still matter. It connects streaming data, fast processing, OLAP serving, dashboards, alerts, and user-facing product experiences.

Why This Matters

Many systems cannot wait for the next batch job. Fraud detection, operational dashboards, experimentation, observability, personalization, and logistics all need low-latency signals.

Data Flow

flowchart LR
  Events[Application events] --> Kafka[Event stream]
  Kafka --> Stream[Stream processing]
  Stream --> OLAP[Real-time OLAP store]
  OLAP --> Dashboards[Dashboards]
  OLAP --> Alerts[Alerts]
  OLAP --> Products[User-facing product features]

Common Design Questions

• What latency does the business actually need?
• What happens when events arrive late?
• How are duplicates handled?
• Is the serving layer optimized for filters, aggregations, or joins?
• What needs batch correction later?

Takeaways

• Real-time does not always mean instant; define the latency target.
• Streaming systems need correctness stories, not just fast ingestion.
• OLAP serving systems are often where product expectations meet infrastructure limits.