Big Data Analytics, Tools, and Tech
Big data is primarily defined by the volume of a data set. Big data sets are generally huge — measuring tens of terabytes — and sometimes crossing the threshold of petabytes. The term big data was preceded by very large databases (VLDBs) that were managed using database management systems (DBMS). Today, big data falls under three categories of data sets: structured, unstructured, and semi-structured.
Big data processing requires a particular setup of physical and virtual machines to get results. The processing is done simultaneously to achieve results as quickly as possible. These days, big data processing techniques also include cloud computing and artificial intelligence. These technologies help in reducing manual inputs and oversight by automating many processes and tasks.
The evolving nature of big data has made it difficult to give it a commonly accepted definition. Data sets are consigned the big data status based on technologies and tools required for their processing.
Big data analytics is the process of extracting useful information by analyzing different types of big data sets. Big data analytics is used to discover hidden patterns, market trends, and consumer preferences for the benefit of organizational decision-making. There are several steps and technologies involved in big data analytics.
Data acquisition has two components: identification and collection of big data. Identification of big data is done by analyzing the two natural formats of data: born digital and born analog.
Born digital data: Information captured through a digital medium, such as a computer, smartphone app, etc. This type of data has an ever-expanding range since systems to collect different kinds of information from users. This data is traceable and can provide both personal and demographic business insights. Examples include cookies, web analytics, and GPS tracking.
Born analog data: Information in the form of pictures, videos, and other formats related to physical elements. This data requires conversion into digital format by using sensors such as cameras, voice recordings, digital assistants, etc. The increasing reach of technology has also raised the rate at which analog data is being converted or captured through digital mediums.
The second step in the data acquisition process is the collection and storage of data sets identified as big data.
