Case Studies in Amalgamation of Deep Learning and Big Data

2. Overview of Big Data

Big data defined as datasets size is away from the capacity of the usual database, capture by software tools, store, manage, and analyze. Handling the data is not easy, and analysis in the standard database likes SQL. The data is too outsized, moves very quick, or it is not related to the structure of database architectures (Parimala & Lopez, 2016).

The key fact of V-based characterization is to focus the big data’s maximum thoughtful challenges are capture, cleaning, curation, integration, storage, processing, indexing, search, sharing, transfer, mining, analysis and visualization of huge sizes of rapid moving high complex data (Manogaran et al., 2016). Big data can be categorized as 10 V’s (Figure 1) are Volume, Variety, Velocity, Veracity, Validity, Value, Variability, Venue, Vocabulary, and Vagueness.

Figure 1.

10 V’s of big data