Big data is still data, but it is large or massive. It is a collection of data that is complex, massive in size and continues to increase with time. It is challenging to process big data if a person or institution uses traditional methods. Due to its size and complexity, traditional methods of managing data become inapplicable for storage and processing. Some common examples of big data include social media, NY stock exchange, jet engines, and satellites.
There are three critical types of big data.
- Structured data – It is called structured because it is processed, kept, or retrieved in an organized format. Big data can be stored in names files, memory cards, hard drives, or hard disks. Nevertheless, data continue to grow exponentially, and this is posing a considerable challenge to its storage, processing, and retrieval. Fundamentally, data needs to be appropriately structured for it to be processed with ease. Data can also be quickly retrieved if it is structured appropriately. In addition, structured data can take a format that includes rows and columns.
- Unstructured – This is data that does not have a fixed format. It is the opposite of structured data. Unstructured data can contain various text files, videos, images or audios that are just mixed up. If big data is stored in an unstructured format, it poses significant challenges, especially during retrieval.
- Semi-structured big data – This kind of data has both a structured and unstructured format. The best example of semi-structured data is the one in an XML file. This structure is somewhat organized, is less complicated than unstructured big data that, in most cases, poses serious challenges, especially during retrieval.
Nonetheless, several characteristics can help one recognize particular data as big. These characteristics are:
- Voluminous
- Variety (heterogeneous source of data, its nature, and format)
- Velocity, the speed at which data can be generated
- Availability, inconsistent at some point.
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