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Real-Time Data Streaming: Kafka and Flink Architecture (2025)

Oct 26, 2025•
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Modern products depend on fresh data. This guide covers production streaming with Kafka and Flink.

Executive summary

  • Use schemas (Avro/Protobuf) and registries to evolve safely
  • Prefer idempotent producers, exactly-once sinks; measure end-to-end latency
  • Isolate hot partitions, scale consumers, monitor backpressure and lag

Reference architecture

  • Ingest: producers (apps, CDC via Debezium), schema registry
  • Processing: Flink jobs (stateful windows, joins), state backends
  • Storage/Sinks: OLAP (ClickHouse), OLTP, caches, search

Exactly-once

  • Kafka transactions + Flink checkpoints; dedupe keys; idempotent sinks

CDC

  • Debezium connectors; outbox pattern; schema evolution policies

Operations

  • Monitor consumer lag, rebalance churn, partition skew; size brokers, ISR

FAQ

Q: When to choose Flink vs Kafka Streams?
A: Flink for complex stateful processing and windowing at scale; Kafka Streams for simpler app-embedded topologies.

  • Event-Driven Architecture: /blog/event-driven-architecture-patterns-async-messaging
  • Data Pipeline Orchestration: /blog/data-pipeline-orchestration-airflow-prefect-dagster
  • ClickHouse Performance: /blog/clickhouse-analytics-database-performance-guide-2025
  • Database Sharding: /blog/database-sharding-partitioning-strategies-scale-2025
  • Caching Strategies: /blog/caching-strategies-redis-memcached-cdn-patterns-2025

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