What is big data concept? Many individuals do not know the answer to this question. In order to fully grasp what is big data concept one must first understand what is big data. There has been much debate over what is big data. Some people use the term to describe large sets of unprocessed information that is very unmanageable. Others prefer to use the phrase “big data” to describe any potentially unlimited amount of data that can be processed quickly and easily by machines.
During the late 1990s and the early 2000s, before the term became popular, a more literal definition would have defined it as any collection or set of data exceeding a terabyte (1 TB) in size. This definition was devised by a group of computer scientists who were working on advanced machine learning systems. Their goal was to create a machine learning system that could process massive amounts of data in a short period of time. Machine learning is the science of analyzing large amounts of data with the hopes of discovering patterns and trends.
The biggest difference between unstructured and structured data sources is the method by which the information is stored. Large unstructured data sources can be processed quickly and efficiently by a single machine while structured data sources must be processed using multiple machines. Data is stored in a structured manner in order to provide a better performance rating to the system. This results in better quality results because the machines are able to make good inferences from the structured data. It also leads to an increase in the number of successful algorithm executions.
Humans are the ones who collect and analyze a large amount of structured data sets. This may come in the form of social media sites, customer and industry surveys, blog entries, and other forms of structured data. Human interaction is the primary reason behind the creation and maintenance of large amounts of structured data sets. However, the big data analytics examples highlighted above show that the processing speed of machines can be utilized to improve the results from human interaction. Machines are able to gather and organize the necessary information much faster than humans, and the result is better quality results.
The big data analytics examples mentioned earlier using social media to collect and analyze information. Many companies today use data management and social media to gather customer and employee information for business purposes. The data management and social media combination have resulted to a more efficient workflow system for managing work processes.
The question “What is Big Data?” continues to gain importance as businesses and organizations discover more uses for big data analytics. However, the conceptualization of this concept was actually completed over forty years ago by John McCarthy. In his article “A Pattern Language”, Mr. McCarthy identified seven main conceptualizations of big data analytics. He further defined these concepts in terms of logical process, statistical method, fuzzy logic, cognitive process, algorithm, and artificial intelligence.
Today, these conceptualizations have been refined to include more specific descriptions. Each of these is important to the improvement of big data analytics, however, because each of them is necessary for the development and maintenance of big database management systems. For instance, the first phase of the process, analytical processing, is concerned with gathering relevant information. Therefore, these processes must deal with the extraction, identification, and analysis of relevant data. Next, the organizational structure of the data, including queries, categories, and relationships, is dealt with. Lastly, the relevance of the data to the requirements of the organization and the requirements of the users is evaluated.
The development of big databases is a complex project. Because of this, many companies will engage the services of an outside organization to develop analytical process and data sets, as well as data management tools to manage these large sets of data. These companies will typically rely on an external developer or software firm to create, design, and maintain the analytics example required by their organization. Big data analytics examples include Hadoop, Spark, and Hive. These applications are based on the Map-Reduce framework and use data modeling, parallelism, and some form of the implicit programming language, DSL. These programs leverage large scale distributed processing technologies such as Hadoop and Apache Hadoop