Fast analysis of data is becoming increasingly important in many domains. To process data incrementally and in real time, many applications are leveraging stream processing systems. At the same time, new data sources become available and affordable, which means scalable solutions are required. Current stream processing systems can scale out individual analysis tasks to tens to hundreds of nodes, processing millions of events per second. Today these systems support the largest companies at peak hours, but they are severely limited in the number and dynamicity of analysis tasks making them inflexible and inefficient in dynamic setups. In our work, we create novel technologies and system architectures that enable highly efficient stream data processing for arbitrary tasks and setups.