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What is Big Data? What are the types of Big Data?

what is big data
what is big data

What is Big Data?

Big Data is a collection of data that is huge in volume, yet continues to grow rapidly over time. And, It is data that is so large in size and complexity that no traditional data management tool can store or process it effectively. And, Big Data is also data but very large in size.

The History of Big Data:

Although the concept of Big Data itself is relatively new, the beginnings of big data set back to the 1960s and 70s when the data world was just beginning with the development of earlier data centers and relative databases. Around 2005, people began to realize how much data users generated through Facebook, YouTube, and other online services. Hadoop was developed that year. In fact, Meanwhile, NoSQL also began to gain popularity. The development of open-source frameworks, such as HDOP, has been essential to the growth of big data because they make big data easier to work with and cheaper to store. In the years that followed, the volume of big data skyrocketed. Consumers are still generating huge amounts of data but it’s not just humans who are doing it.

Types of Big Data:

Furthermore, The following types are:

  • Structured
  • Unstructured
  • Semi-structured

Structured:

Any data that can be stored, accessed, and processed in a fixed format is called ‘Structured data’. Over time, skills in computer science have become more and more successful in developing techniques for working with such data. Nowadays, however, we are looking for problems when such data increases to a large extent, the normal size is the range of several zettabytes.

Unstructured:

Any data with an unknown format or structure is classified as unstructured data. In addition to being large in size, processing unprocessed data to take advantage of it poses a number of challenges to its processing. A great example of unstructured data is a heterogeneous data source consisting of plain text files, images, videos, etc.

Semi-structured:

Semi-structured data can contain both types of data. We can see semi-structured data formatted, but in reality, it is not explained as an example. Definition of a table in relative DBMS. An example of semi-structured data is the data presented in an XML file.

Characteristics of Big Data:

However, The following characteristics are:

  • Volume
  • Variety
  • Velocity
  • Variability

Volume:

The name of the big data itself is related to a size that is too large.  The size of the data plays a very important role in determining the value beyond the data. Also, whether or not a particular statistic can actually be considered big data depends on the amount of data. Therefore, volume is a feature that needs to be considered when dealing with big data.

Variety:

The next aspect of Big Data is its variety. Different types mean different sources and the nature of the data, both structured and unstructured. In recent days, spreadsheets and databases have been the only data sources that most applications consider. Nowadays, data requests in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered for analysis. This type of unstructured data involves certain issues for storing, mining, and analyzing data.

Velocity:

The term velocity refers to the speed of data preparation. How fast the data is generated and processed to meet the demands determines the real potential of the data. Big Data velocity is related to the speed at which data flows through business processes, application logs, networks, and social media sites, sensors, and mobile devices.

Variability:

This is inconsistent with what can be shown by the data at times, thus hampering the process of handling and managing the data effectively.

How does Big Data work?

Big data to provide new insights that open new opportunities and business models. Getting started involves three key actions:

Integrate:

Big Data collects data from many different resources and applications. Traditional data integration mechanisms, such as ETL do not usually depend on this task. It needs new strategies and technology to analyze large data sets on a terabyte, or even a petabyte, scale.

Manage:

Big data needs storage. Your storage solution can be in the cloud, on campus, or in both. You can store your data in any format and bring your desired processing needs and necessary process engines to these datasets based on demand. Many people choose their storage solution according to which their data is currently stored.

Analyze:

When you analyze and act on your data, your investment in big data pays off. Get a new explanation with a visual analysis of your diverse set of data. And, Explore the data further to make new discoveries. Share your searches with others. Build data models with machine learning and artificial intelligence. To make your data work.

Example of Big Data:

The best example of Big Data can be found in both public and also private sectors. And, From targeted advertising, education, and already mentioned large-scale industries to real-life scenarios in guest service or entertainment. By 2020, 1.7 megabytes of data per second will be generated for every person on the planet, and the potential for data-driven organizational growth in the field of hospitality is enormous. Big data can help deliver benefits in some amazing areas.

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