Streaming Data Pipelines: Architecture and Best Practices
Streaming Data Pipelines: Architecture and Best Practices Streaming data pipelines enable real-time insights, alerts, and timely actions. A good design is modular and scalable, with clear boundaries between data creation, transport, processing, and storage. When these parts fit together, teams can add new sources or swap processing engines with minimal risk. Architecture overview Ingest layer: producers publish events to a durable broker such as Kafka or Pulsar. Processing layer: stream engines (Flink, Spark Structured Streaming, or ksqlDB) read, transform, window, and enrich data. Storage and serving: results land in a data lake, a data warehouse, or a serving store for apps and dashboards. Observability and governance: schemas, metrics, traces, and alerting keep the system healthy and auditable. Design choices matter. Exactly-once semantics give strong guarantees but may add overhead. Often, idempotent sinks and careful offset management provide a practical balance for many use cases. ...