Transformatiestromen

Transformatiestromen are a concept in data processing and software architecture that describe streams of data items moving through a sequence of transformation steps from a source to a destination. In a transformatiestroom, each data item is processed by one or more operations, and the output of one operation becomes the input to the next. The term is used especially in Dutch-language literature on data pipelines and stream processing, but the concept is widespread in modern data processing.

Typical transformations include map (or select), filter, enrich, and aggregation. Transformatiestromen can be stateless, where each item is processed independently, or stateful, where the transformation depends on previously seen items. They often support windowing to group data over a time interval and may incorporate joins with external streams or datasets. The pipeline structure can be represented as a directed graph of transformation nodes, enabling complex processing topologies.

Implementations emphasize low latency and scalability, with considerations such as backpressure, fault tolerance, and correctness under out-of-order or late-arriving data. Transformatiestromen are central to streaming ETL, real-time analytics, and event-driven architectures, and they are related to the broader concepts of data pipelines and reactive programming. Common platforms and libraries provide building blocks for composing transformation steps in a pipeline and managing the flow of data between stages.