Different Types of Big Data
Types of Big Data:
Thus, Big Data includes huge volume, hig velocity and extensible variety of data. The data in it will be of three types:
- Structured Data
- Semi-Structured Data
- Unstructured Data
Structured Data:
It concerns all data which can be stored in database SQL in table with rows and columns. They have relational key and can be easily mapped into pre-designed fields. Today, those data are the most processed in development and the simplest way to manage information.
Structured data is often managed using Structured Query Language (SQL) – a programming language created for managing and querying data in relational database management systems. Originally developed by IBM in the early 1970s and later developed commercially by Relational Software, Inc. (now Oracle Corporation).
Structured data was a huge improvement over strictly paper-based unstructured systems, but life doesn’t always fit into neat little boxes. As a result, the structured data always had to be supplemented by paper or microfilm storage. As technology performance has continued to improve and prices have dropped, it was possible to bring into computing systems unstructured and semi-structured data.
Semi-Structured Data:
Semi-structured data is information that doesn’t reside in a relational database but that does have some organizational properties that make it easier to analyze. With some process you can store them in relation database, but the semi-structure exist to ease space, clarity or compute.
Example: CSV, XML and JSON documents are semi-structured documents. NoSQL databases are considered as semi-structured.
Unstructured Data:
Unstructured Data refers to information that either does not have a pre-defined data model. It is typically text-heavy, but may contain data such as dates, numbers and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated in documents.
