Immense growth of avaliable data generated from various fields of user interaction within technological area of domain has made the need for structured development of suitable analytics crucial. To be able to provide knowledge from vast amount od unstructured data it is essential to use data mining, extended to the specialized fast-growing field of machine learning. Furthermore, processing of continuously generated data has to be parallelized to make real-time processing possible, which is achieved through distributed data processing engines. This thesis provides description of conceptual model of distribuited data streaming processing engines as well as representation of specific engines available through Hadoop ecosystem: Apache Kafka, Apache Spark and Apache Flink. System which interconnects all of the specified engines is implemented in order to carrry out evaluation and comparison of their performance, while applying machine learning algorithms. Apache Kafka is used for securing persistence of data in both versions of experimental setup, while the use of Spark and Flink excludes one another. Selection of applied machine learning algorithms depended on their availability within libraries of chosen engines, it consisted of SVM, linear regression and k-means. Evaluation metric is based upon usual demands concerning distributed systems; it compares scalability of selected engines while performing data processing on same dataset which simulates data stream. Apache Spark outperformed Apache Flink through all of the conducted experiments, which does not diminish future prosperity of Apache Flink.