adatfolyamarchitektúra

Adatfolyamarchitektúra refers to the design and organization of systems that process continuous streams of data. It outlines how data is ingested, transformed, stored, and analyzed in real-time or near real-time. Key components often include data sources, message queues or brokers, processing engines, and data sinks. The architecture prioritizes low latency, high throughput, and fault tolerance to handle dynamic and often unpredictable data volumes. It's crucial for applications requiring immediate insights, such as fraud detection, real-time analytics, IoT data processing, and online recommendation systems. Common technologies employed include Apache Kafka, Apache Flink, Apache Spark Streaming, and cloud-native streaming services. The design choices within an adatfolyamarchitektúra depend heavily on the specific use case, considering factors like data volume, velocity, variety, and the required consistency guarantees. It enables organizations to react quickly to events and derive value from data as it is generated, rather than in batch processing cycles.