Streamingzahlen

StreamingZahlen is a term used in German-language data engineering to describe the practice of processing and analyzing streaming numerical data in real time. The name combines streaming (from English) with zahlen, the German word for numbers. It is not a formal standard, but a descriptive label for real-time numeric analytics pipelines.

Core concepts include continuous data ingestion, windowed aggregations, and low-latency delivery of results. Distinctions are made between event-time processing, based on when data occurred, and processing-time, based on when data is processed. Typical operations are count, sum, average, min, max, and rate calculations, often implemented with tumbling, sliding, or session windows.

Typical architecture involves data sources feeding a streaming platform such as Apache Kafka or AWS Kinesis, a stream-processing engine like Apache Flink, Spark Structured Streaming, or Beam, and downstream sinks such as dashboards or alerting systems. StreamingZahlen is presented as a methodological approach rather than a single product.

Use cases include real-time dashboards for operational metrics, fraud detection, IoT telemetry, and live clickstream analysis. Key performance indicators are latency, throughput, accuracy in handling late data, and fault tolerance. Outputs commonly include rolling counts, moving averages, percentiles, and anomaly alerts.

Challenges encompass data quality, out-of-order events, watermark management, late-arriving data, backpressure, and scaling to high volumes. Governance and observability are important to validate correctness and maintain reliability in production environments.