Stream On with Pravega
On stage: Pravega
Stream processing is at the spotlight in the space of data analytics, and the reason is rather evident: there is high value in producing insights over continuously generated data shortly rather than wait for it to accumulate and process in a batch. Low latency from ingestion to result is one of the key advantages that stream processing technology offers to its various application domains. In some scenarios, low latency is not only desirable but strictly necessary to guarantee a correct behavior; it makes little sense to wait hours to process a fire alarm or a gas leak event in the Internet of Things.
A typical application to process stream data has a few core components. It has a source that produces events, messages, data samples, which are typically small in size, from hundreds of bytes to a few kilobytes. The source does not have to be a single element, and it can be multiple sensors, mobile terminals, users or servers. . This data is ultimately fed into a stream processor that crunches it and produces intermediate or final output. The final output can have various forms, often being persisted in some data store for consumption (e.g., queries against a data set). Continue Reading