Stream Data Model and Architecture

In recent era, Big Data Streams have significant impact owing the reality that there are many applications from where a big amount of data is continuously generated at a bang-up velocity. Because of integral dynamical features of big data, it is hard to apply existing working models directly on big data streams. The solution of this limitation is data streaming. A modern-day data streaming architecture allows taking up, operating and analyzing high mass of high-speed data from a collection of sources in real time to build more reactive and intelligent customer experiences. It can be designed as a batch of five logical layers; Source, Stream Storage, Stream Ingestion, Stream Processing and Destination. This chapter comprises of a brief assessment on the stream analysis of big data which engaged a thorough and organized way to looking at the inclination of technologies and tools used in the field of big data streaming along with their comparisons. We will provide study to cover issues like scalability, privacy and load balancing and their existing solutions. DGIM Algorithm which is used to count the number of ones in a window and FCM Clustering Algorithm and others are also in consideration to review in this chapter.