Most modern businesses need continuous and real-time processing of unstructured data for their enterprise big data applications.

Real-time streaming implementations need to have the following characteristics:

• Minimize latency by using large in-memory
• Event processors are atomic and independent of each other and so are easily scalable
• Provide API for parsing the real-time information
• Independent deployable script for any node and no centralized master node implementation

The real-time streaming pattern suggests introducing an optimum number of event processing nodes to consume different input data from the various data sources and introducing listeners to process the generated events (from event processing nodes) in the event processing engine:

Event processing engines (event processors) have a sizeable in-memory capacity, and the event processors get triggered by a specific event. The trigger or alert is responsible for publishing the results of the in-memory big data analytics to the enterprise business process engines and, in turn, get redirected to various publishing channels (mobile, CIO dashboards, and so on).