"streaming data" can generally be thought of as a sequence of events in time. Think measurements from sensors, mouse click events from uses on a webpage, or access logs from a server. These events are often fed into a message bus type system like Apache Kafka or AWS Kinesis.
Stream processing is a programming paradigm for working with these types of timestamped events and a "stream processing engine" like Denormalized seeks to actually execute stream processing compute jobs against streaming data.
The goal of the engine is to abstract away as much of the low level complexities needed to effectively work with this data at scale and provide an ergonomic way for developers to write and operate streaming applications.
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"streaming data" can generally be thought of as a sequence of events in time. Think measurements from sensors, mouse click events from uses on a webpage, or access logs from a server. These events are often fed into a message bus type system like Apache Kafka or AWS Kinesis.
Stream processing is a programming paradigm for working with these types of timestamped events and a "stream processing engine" like Denormalized seeks to actually execute stream processing compute jobs against streaming data.
The goal of the engine is to abstract away as much of the low level complexities needed to effectively work with this data at scale and provide an ergonomic way for developers to write and operate streaming applications.