Grafana

Real-time Analytics: Streaming Data to Dashboards Real-time analytics helps teams observe events as they happen. With streaming data, dashboards refresh continuously, helping people spot trends and issues quickly. This guide shares practical ideas to build a simple streaming dashboard that you can reuse. How real-time streams work Data sources push events to a streaming platform (for example, Apache Kafka, AWS Kinesis, or Pulsar). A processor reads those events, aggregates them near real time, and writes results to storage. A dashboard or BI tool queries the latest numbers to render charts. Real-world example An online store streams events such as view, add_to_cart, and purchase into a topic. A small processor computes per-minute revenue and top products, then stores results in a time-series database. A Grafana dashboard shows revenue over time and a map of active users, updating as new events arrive. ...

Performance Monitoring for Cloud-Native Apps Modern cloud-native apps run across many services, containers, and regions. Performance data helps teams understand user experience, stay reliable, and move fast. A good monitoring setup shows what happens now and why something changes. What to monitor Latency: track P50, P95, and P99 for user requests. Slow tails often reveal hidden bottlenecks. Error rate: measure failed responses and exceptions per service. Throughput: requests per second and goodput per path. Resource saturation: CPU, memory, disk, and network limits, plus container restarts. Dependency health: databases, caches, queues, and external APIs. Availability and SLOs: align dashboards with agreed service levels. How to instrument and collect data Use OpenTelemetry for traces and context propagation across services. Capture metrics with a time-series database (for example Prometheus style metrics). Include basic logs with structured fields to join traces and metrics when needed. Keep sampling sane for traces to avoid overwhelming backends while still finding root causes. Visualization and alerts Build dashboards that show a service map, latency bands, error rates, and saturation in one view. Alert on SLO breaches, sudden latency spikes, or rising error rates. Correlate traces with metrics to identify the slowest span and its service. Use dashboards to compare deployed versions during canary periods. Practical steps you can start today Define clear SLOs and SLIs for critical user journeys. Instrument core services first, then expand to downstream components. Enable tracing with sampling that fits your traffic and costs. Review dashboards weekly and drill into high-lidelity traces when issues occur. Test alerts in a staging or canary release to avoid noise. A quick example Imagine a page request that slows down after a code change. The trace shows a longer database call in Service A. Metrics reveal higher latency and a growing queue in a cache. With this view, you can roll back the change or optimize the query, then re-check the metrics and traces to confirm improvement. ...