A large data set can be analyzed computationally. If it reveals patterns, trends, and associations, then it comes under big data. Organizations are finding it difficult to collect and store data at an ever-increasing flow. Business needs real-time analysis. This requires applications that can display real-time changes and more illustrative graphics. This is a combination of tools, techniques, methods and frameworks. These statistics have both positive and negative values.
Big data can come from almost anything that generates data. It includes search engines and social media. This data can be categorized into three types:
Structured data – This data resides in a database or spreadsheet, so that its elements are available for effective processing and analysis.
Semi-structured data – It is neither raw nor structured data of a database. It contains tags to enforce hierarchies of records and fields within the data.
Unstructured data – It is very valuable and very much available in business interactions. It is also available in web logs, multimedia content, email, customer service interactions, sales automation, and social media data.
Big data is normally collected and analyzed at predefined intervals, however, the collection and analysis with the real-time big data analysis is continuous, giving the business latest information.
The handling of real-time big data analytics is done with Twitter-owned Storm system. It works with any programming language and can be changed in size or scale. Whereas GridGain also handles the big data and it is compatible with Hadoop DFS – a substitute for Hadoop’ MapReduce.
Real-time big data analytics is an important tool for business, but the organization must check all the situations first before implementing a new technology.