Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data" refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered.