Big Data is a term used to describe enormous data sets that cannot be analyzed, processed, or stored using conventional tools. There are many different kinds of big data sources available today that generate data quickly. These information sources are accessible on a global scale. Social media networks and platforms are by far the largest sources of data. Facebook should be used as an example. It consistently generates more than 500 terabytes of data. This information consists of messages, videos, images, and more. Big data is characterized by three "V" s volume, velocity, and variety. If you want to learn more about Big Data check out ProjectPro Solved End-to-End Big Data Projects.

And Below are 5 types of big data.

Structured Data

Structured data is any data that can be processed, accessed, and kept in a set format. Over time, software engineering expertise has made more notable strides in developing methods for using this type of data and extrapolating a benefit from it. However, given that average sizes are now approaching multiple zetta bytes, we are now anticipating problems as the size of this data grows to enormous proportions. The easiest type of big data to work with is structured data. Big data that is profoundly coordinated with measurements described by setting parameters is known as structured data.

Unstructured Data

This is one of the types of big data that incorporates the data format of a sizable number of unstructured files, such as image, audio, log, and video files. Unstructured data is any data that has an unfamiliar model or structure. Unstructured data in big data faces a variety of challenges when it comes to setting it up for valuation because of its enormous size. An intricate data source with a mixture of pictures, videos, and text files serves as an example of this. A small number of associations have access to a vast amount of data. Due to the raw nature of the data, these associations are unable to deduce an incentive from it.

Semi-Structured Data

One of the categories of big data that includes both the previously mentioned formats of unstructured and structured data is semi-structured data. To be more precise, it refers to data that, despite not having been organized into a particular database, contains crucial tags or information that isolates individual data components. In this manner, we reach the conclusion of big data type categories.

Subtypes of Data

Despite not being formally classified as big data, some subtypes of data are somewhat pertinent to the analytics industry. These frequently make reference to the origin of the data, such as social media, machine learning, geospatial data, or event-triggered data. These subtypes linked, lost/dark, or open can also make references to levels.

Interacting With Data through Programming

When working with the data, different programming languages will accomplish a variety of tasks. Three key players are readily available:

Scala: 

A Java-based language that is gaining popularity It was used to develop a number of Apache products, including Spark, a key player in the big data stages market.

R: 

R is the language of choice for explicit structure and more contemporary analysis. It is one of the best coding languages for data control currently available, and it can be used at every stage of an investigation cycle all the way up to perception.

Python: 

Python is an open-source language that is regarded as being among the easiest to learn. Compact abstraction and syntax are used.

Conclusion

Structured data, unstructured data, and semi-structured data are the three categories into which big data is divided. Big data makes practically any understanding a business might need possible, whether the analytics are predictive, diagnostic, descriptive, or prescriptive. The field of big data analytics is built on the backs of giants; the ability to analyze and collect data has been around for decades, if not centuries.