Streamingintegraatioon

Streamingintegraatioon is a term used to describe the process of integrating streaming data sources, streaming platforms, and delivery pipelines to enable real-time data processing, analytics, and content distribution. It encompasses the end-to-end flow from data producers through event streams to downstream consumers, often involving schemas and processing logic.

Core components typically include data producers (sensors, applications, logs), a streaming platform (for example Apache Kafka, AWS Kinesis, or Google Pub/Sub), a stream processing layer (such as Apache Flink or Spark Structured Streaming), and sinks such as data warehouses, dashboards, or content delivery networks.

Common patterns include event-driven architectures, Kappa or lambda architectures, and change data capture (CDC) for real-time replication. Data formats such as JSON, Avro, or Protobuf and schema registries support schema evolution across systems. The choice of storage options—real-time databases, data lakes, or data warehouses—also shapes the design of streaming integration.

Benefits include lower latency, scalability, and timely insights for operational intelligence or personalized experiences. Challenges cover data quality, latency guarantees, backpressure handling, schema evolution, governance, and security. Successful streaming integration relies on clear data contracts, robust monitoring, and appropriate fault-tolerance strategies.

See also real-time data integration and streaming analytics.