Event Processing

Real-Time Analytics: Streaming Data at Scale Real-time analytics help teams see what happens as it happens. Streaming data arrives continuously from apps, devices, and logs. The goal is to turn that flow into meaningful insights within seconds or minutes, not hours. This speed lets teams react quickly, adjust offers, prevent outages, and improve customer experiences. What real-time analytics means Data is collected and processed as it streams in. Results are updated frequently, often with rolling windows. Decisions are supported by current, not historical, information. Key building blocks ...

Real-Time Analytics: Streaming Data at Scale Real-time analytics means turning events into insights as they arrive. Streaming data comes from logs, sensors, apps, and transactions. When processed with low latency, teams can detect issues, guide decisions, and trigger timely actions. At scale, the goals include steady throughput, predictable response times, and resilient operation. This requires well designed pipelines, careful partitioning, and reliable state management. A typical pipeline starts with data sources feeding a streaming platform. The processor keeps state, applies calculations, and emits results to storage, dashboards, or alerts. The key is to balance speed with correctness, especially when events arrive out of order or late. Good design also helps you handle bursts and keep the system responsive. ...