Big Data

What is Big Data? Introduction, Types, Characteristics, Examples

What exactly is data?
The quantities, characters, or symbols on which computer operations are performed, may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.

Let us now look at the definition of Big Data.

What is Big Data?

When it comes to big data, it refers to a collection of information that is both large in volume and growing exponentially over time. Because it is so large and complex, no traditional data management tools are capable of storing or processing it efficiently. Big data is data, but it is data that is extremely large.

What is an example of Big Data?

The following are some real-world Big Data applications:

It is estimated that the New York Stock Exchange generates approximately one terabyte of new trade data per day, which is an example of Big Data.

As an illustration of Big Data
Social Media Sites

Every day, more than 500 terabytes of new data are ingested into the databases of the social media website Facebook, according to a recent statistic. This information is primarily gathered through photo and video uploads, message exchanges, and the posting of comments, among other activities.

As an illustration of Big Data
In just 30 minutes of flight time, a single jet engine can generate more than 10 terabytes of data. With hundreds of thousands of flights per day, the amount of data generated can amount to several Petabytes.

As an illustration of Big Data
Big Data Comes in a Variety of Forms
The following are examples of different types of Big Data:

Structured \sUnstructured
Data that can be stored, accessed, and processed in the form of a fixed format is referred to as “structured information.” Over time, computer science talent has demonstrated greater success in developing techniques for working with this type of data (in which the format is known in advance) and also in deriving value from this type of data. In the current climate, however, we are anticipating problems when the amount of data collected grows to an enormous extent; typical sizes are in the range of multiple zettabytes.

An ‘Employee’ table in a database is an example of Structured Data

Employee_ID Employee_Name Gender Department Salary_In_lacs
2365 Rajesh Kulkarni Male Finance 650000
3398 Pratibha Joshi Female Admin 650000
7465 Shushil Roy Male Admin 500000
7500 Shubhojit Das Male Finance 500000
7699 Priya Sane Female Finance 550000

Do you have any idea? One zettabyte is equal to 1021 bytes, and one billion terabytes are equal to one billion zettabytes.

One can easily understand why the term “Big Data” was coined and imagine the difficulties involved in storing and processing all of this information by looking at these figures.

Do you have any idea? Data stored in a relational database management system is an example of what is referred to as structured information.

Types Of Big Data

Unstructured data is any data that does not have a known form or structure and is therefore classified as such. Additionally, unstructured data presents several challenges in terms of processing and deriving value from it, in addition to its sheer volume and unpredictability. As an illustration, a heterogeneous data source containing a mixture of simple text files, images, videos, and so on is an example of unstructured data in action. Organizations today have a plethora of data at their disposal, but because this data is in its raw form or unstructured format, they are unable to extract any value from it.

Data Sets With Unstructured Information

The results of a Google search are shown below.

Unstructured Data Is An Exemplification
Unstructured Data Is An Exemplification


Semi-structured data can include both of these types of information. Semi-structured data appears to be structured in appearance, but it is not defined in the same way as, for example, a table definition in a relational database management system. An XML file containing semi-structured data is an example of semi-structured data.

Semi-structured data examples are provided below.

Data Growth Over the Years Data Growth has increased over the years.

Please keep in mind that web application data is unstructured and consists of log files, transaction history files, and other similar files. OLTP systems are designed to work with structured data, which is data that has been organized into relationships (tables).

Characteristics Of Big Data

The following characteristics can be used to describe large amounts of data:

Volume \sVariety
Variability in Velocity I Volume – The term “Big Data” refers to an extremely large collection of data. When it comes to extracting value from data, the size of the data is extremely important. Aside from that, whether a particular data set can be considered Big Data or not is determined by the amount of data contained within it. As a result, when dealing with Big Data solutions, one of the characteristics that must be taken into consideration is ‘volume.’

Variety – The next aspect of Big Data to consider is its diversity.

Data that is heterogeneous in both source and nature (structured or unstructured) is referred to as “varieties.” Back in the day, spreadsheets and databases were the only data sources taken into consideration by the majority of software applications. In today’s world, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, and other types of files are taken into account when developing analysis applications. This variety of unstructured data poses several challenges in terms of data storage, mining, and analysis.

It is the speed at which data is generated that is referred to as its velocity. (iii) Velocity The real potential in data is determined by how quickly it is generated and processed to meet the needs of the organization.

Big Data Velocity is concerned with the rate at which data is ingested from various sources such as business processes, application logs, networks, social media sites, sensors, and mobile devices, among others. The flow of information is massive and never-ending.

When we talk about variability, we are referring to the inconsistency that can be displayed by data at times, which can make it difficult to handle and manage the data effectively.

Advantages Of Big Data Processing

The ability to process large amounts of data in a database management system has several advantages, including the following:

When making decisions, businesses can benefit from external intelligence sources.
Organizations can fine-tune their business strategies as a result of the availability of social data from search engines and social media sites such as Facebook and Twitter.

Customer service has been improved.
Traditional customer feedback systems are being phased out in favor of new systems built with Big Data technologies. Consumer responses are read and evaluated by these new systems, which make use of Big Data and natural language processing technologies.

Early detection of potential risks to the product or services, if any.
Increased operational effectiveness
Massive data processing techniques can be used for the creation of a staging area or landing zone for new data before determining which data should be transferred to a data warehouse. In addition, such integration of Big Data technologies and data warehouses helps an organization to offload infrequently accessed data.