Stream processing systems are pivotal to modern data-driven environments, enabling the continual ingestion, processing and analysis of unbounded data streams across distributed computing resources.

Stream processing decouples the data from bulky system-wide databases so it can be directed and redirected as necessary. Why transition to stream processing?

As AI shifts from experimental phases to mission-critical roles—such as fraud detection, live recommendation engines, and ...

Science & Technology Computing professor designs stream processing system for IoT applications Recent National Science Foundation CAREER recipient is building a digital architecture to rapidly process ...

Stream processing is the processing of data in motion, or in other words, computing on data directly as it is produced at the source or received by the stream processing system. Before stream ...

Event sourcing and CQRS are two patterns that has emerged in the Domain-Driven Design (DDD) community. Stream processing builds on similar ideas but has emerged in a different community, Martin ...

This Lambda architecture, as it would later become known, would combine a speed layer (consisting of Storm or a similar stream processing engine), a batch layer (MapReduce on Hadoop), and a server ...

Data systems in the modern world aren't islands that stand on their own; data often flows between databases, offline data stores and search systems, as well as to stream processing systems.

One problem is that stream processing systems often require users to learn a set of platform-specific programming interfaces to be able to manipulate streaming data.