Data stream processing is witnessing its prime time due to efforts by database researchers and numerous worldwide open-source communities. While stream processing in the early 2000s was used for computing simple windowed aggregates and joins, stream processors nowadays are used for real-time traffic predictions, fraud detection, event-driven microservices, and even serverless functions. In this talk, I will first outline how streaming systems have changed since the early days of stream processing, and pinpoint the main advances that have led to the widespread adoption of modern stream processors. I will then turn to the ability of stream processors to be “misused” in modern applications based on an important observation: multiple families of data-intensive applications can be modeled as stateful dataflow graphs, which can be automatically scaled and deployed in the Cloud. Are streaming dataflow engines the answer to the quest for a "universal execution engine” for the Cloud?
Bio: Asterios Katsifodimos is an Assistant Professor at the Delft University of Technology, and a Visiting Academic at Amazon Web Services (AWS) - AI. Before that, Asterios worked at the SAP Innovation Center (Berlin), and at the Technical University (TU) of Berlin. He obtained his PhD from INRIA Saclay/University Paris 11. His research spans the areas of parallel data processing, Cloud computing, and data integration. Asterios has received the ACM SIGMOD Research Highlights Award in 2016, the EDBT best paper in 2019, EDBT best demo award in 2023, and the ACM SIGMOD Systems Award in 2023.
