Apache Flink — Stream Processing for Real-Time Analytics

Batch processing with daily latency isn’t enough. The client wants to see conversions, revenue, and anomalies in real time. Apache Flink with Kafka enabled us to build a streaming analytics pipeline.

Why Flink (and Not Spark Streaming)¶

Spark Streaming is micro-batch — latency in seconds. Flink is true streaming — event-by-event processing with millisecond latency. For real-time dashboards and alerting, the difference is fundamental.

Flink offers exactly-once semantics, event time processing (not just processing time), and sophisticated windowing.

Pipeline Architecture¶

E-commerce events (page view, add to cart, purchase) → Kafka topics → Flink jobs → output to Elasticsearch (dashboards) + Kafka (alerting) + S3 (archive).

Flink Jobs¶

• Real-time aggregation: revenue per minute, conversion funnel, active users
• Anomaly detection: sliding window, comparison with historical average
• Sessionization: grouping events into user sessions based on activity gaps
• Enrichment: join with reference data (product catalog, user segments)

Operational Experience¶

Flink on Kubernetes runs in HA mode with checkpointing to S3. Savepoints for planned upgrades — stop the job, upgrade, restart from the savepoint. No data loss.

Monitoring: Flink provides hundreds of metrics to Prometheus. Key ones: checkpoint duration, backpressure, throughput, consumer lag.

Streaming Is the New Batch¶

Real-time analytics isn’t a luxury — it’s a competitive advantage. Flink with Kafka provides a stream processing platform that handles everything from simple aggregations to complex CEP.