Facets of Data

 Facets of Data

It is used to represent the various forms in which the data could be represented inside Big Data. The following are the various forms in which the data could be represented.

• Structured
• Unstructured
• Natural Language
• Machine Generated
• Graph Based
• Audio, Video & Image
• Streaming Data

Structured

Structured data is data that depends on a data model and resides in a fixed field within a record. As such, it’s often easy to store structured data in tables within databases or Excel files. SQL , or Structured Query Language, is the preferred way to manage and query data that resides in databases.    

Unstructured

Unstructured data is data that isn’t easy to fit into a data model because the content is context-specific or varying.

Natural Language

Natural language is a special type of unstructured data; it’s challenging to process because it requires knowledge of specific data science techniques and linguistics.

The natural language processing community has had success in entity recognition, topic recognition, summarization, text completion, and sentiment analysis, but models trained in one domain don’t generalise well to other domains.

Example: Emails, mails, comprehensions, essays, articles etc..

Machine Generated

Machine-generated data is information that’s automatically created by a computer, process, application, or other machine without human intervention. Machine-generated data is becoming a major data resource and will continue to do so.

Graph Based

“Graph data” can be a confusing term because any data can be shown in a graph. “Graph” in this case points to mathematical graph theory. In graph theory, a graph is a mathematical structure to model pair-wise relationships between objects. Graph or network data is, in short, data that focuses on the relationship or adjacency of objects. The graph structures use nodes, edges, and properties to represent and store graphical data. Graph-based data is a natural way to represent social networks, and its structure allows you to calculate specific metrics such as the influence of a person and the shortest path between two people.

Audio, Video & Image

Audio, image, and video are data types that pose specific challenges to a data scientist. Tasks that are trivial for humans, such as recognizing objects in pictures, turn out to be challenging for computers.

Examples: Youtube videos, podcast, music and lots more to add up to.

Streaming Data

While streaming data can take almost any of the previous forms, it has an extra property. The data flows into the system when an event happens instead of being loaded into a data store in a batch. Although this isn’t really a different type of data, we treat it here as such because you need to adapt your process to deal with this type of information.

Examples: Video conferences and live telecasts all work on this basics.