Event streaming systems, such as Apache Kafka, are increasingly used for data-intensive applications due to the proliferation of streaming sources of data and the requirement of responding with low latency. These systems center their internal design and ...

Stream processing pipelines need to handle workload fluctuations (e.g., daily patterns, popularity spikes) by scaling up/down the resources contributed to running jobs. While there have been efforts proposing auto-scaling mechanisms for stream processing ...

The growing popularity of the data stream abstraction entails new challenging requirements when it comes to data ingestion and storage. Many organizations expect to retain data streams for extended periods of time and to store such stream data in a cost-...