Streaming analytics (also called real-time data analytics) are big data technolo
ID: 3754646 • Letter: S
Question
Streaming analytics (also called real-time data analytics) are big data technologies often used by companies seeking to make better use of datasets collected in real-time. Streaming analytics focus on data streams (a continuous, rapid record of data) and the processing of this information into new business intelligence, such as marketing knowledge and business insights.
Chose 3 streaming analytics platform and Discuss three major features of streaming analytics (based on your chosen streaming analytics platform). THEN From these three features, choose one feature on which to elaborate. Illustrate a hypothetical example that technically explains how streaming data is received, processed and finally presented to its audience.
Explanation / Answer
3 streaming analytics platform are:-
Striim;
Hadoop;
SQL Stream;
Major features of streaming analytics platforrm (Hadoop) :-
Highly scalable: additional nodes can be added or removed very easily
Data processing flexibility:- Whether data is structured or unstructured, irrespective of the data type, good decision making is done.
Fault tolerance:- data is stored at 3 different locations, so fault tolerance is good, say, 1 node fails in one location, so processing happens from another location.
message broker like RabbitMQ is used to pass events in a topics, and later publish the results. Such a code is called actor. Using stream processor, we can send events to the processor or via the broker. event stream processor is responsible for fetching data and sending to each actor, which is finally presented to the audience.