Efficient stream data processing for Backend model training

This paper examines approaches to stream data processing (SDP) for training machine learning (ML) models on the backend. Architectural solutions, including Lambda and Kappa architectures as well as microservice approaches, are explored with a focus on their advantages and limitations under modern conditions. Tools such as Apache Kafka, Apache Flink, and Apache Spark Streaming are analyzed, emphasizing their applicability to various data processing tasks. Special attention is given to performance optimization methods, including the use of online learning and incremental learning algorithms, data compression, efficient serialization, and resource management. The article presents examples of technology implementation demonstrating their practical value.