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Rooted in even basic human everyday needs to even high-scale industrial requirements, technology has revolutionized our way of living. It has wormed itself in every aspect of our lives. With this shift in paradigm, the value of data as a resource has increased tenfold. Large datasets fall under the category of Big Data which requires numerous types of analytics in Big Data for use.
With 2.5 quintillion bytes of data being generated on a daily basis via cell phones, streaming videos, social networks, and most importantly, the Internet of Things. The significant growth of data in recent years has given way to numerous types of Big Data analytics. Collecting, processing, and analyzing Big Data requires the expertise of professionals in this field who can impart the necessary information that can aid an organization in growing. With Big Data training, you can secure yourself a high-profile job in this field.
Keep reading if you want to know more about Big Data, types of data in Big Data analytics, types of digital data in Big Data, which type of clustering could handle Big Data, 4 types of data analytics, and more.
Big Data can be defined as a high amount of data that cannot be processed or stored with the help of standard processing equipment and data storage. A massive amount of data is produced daily, and interpreting and manually processing complex and expansive datasets are next to impossible. It requires modern tools and expert skills to interpret large volumes of data and provide them to organizations with valuable insights to help businesses grow.
Big data types in Big Data are used to categorize the numerous kinds of data generated daily. Primarily there are 3 types of data in analytics. The following types of Big Data with examples are explained below:-
1. Structured Data: Any data that can be processed, is easily accessible, and can be stored in a fixed format is called structured data. In Big Data, structured data is the easiest to work with because it has highly coordinated measurements that are defined by setting parameters. Structured types of Big Data are:-
2. Unstructured Data: Unstructured data in Big Data is where the data format constitutes multitudes of unstructured files (images, audio, log, and video). This form of data is classified as intricate data because of its unfamiliar structure and relatively huge size. A stark example of unstructured data is an output returned by ‘Google Search’ or ‘Yahoo Search.’
3. Semi-structured Data: In Big Data, semi-structured data is a combination of both unstructured and structured types of data. This form of data constitutes the features of structured data but has unstructured information that does not adhere to any formal structure of data models or any relational database. Some semi-structured data examples include XML and JSON.
This form of data does not typically fall under the umbrella of Big Data. However, certain subtypes of data are relevant in the field of analytics. This type of data often refers to the origin of data. They are classified as follows:-
Data subtypes can fall under other access levels, such as:-
General characteristics of Big Data can be referred to as the five Vs: Volume, Velocity, Variety, Veracity, and Value. They have been elucidated below:-
Collecting, processing, analyzing, and storing Big Data has several perks that adhere to modern-day conglomerate needs. Some of the added benefits of Big Data are as follows:-
1. Predictive analysis: This holds a significant amount of benefit in Big Data because it directly enhances businesses' growth via forecasting, better decision-making, ensuring maximum operational efficiency, and mitigating risks.
2. Enhanced business growth: With data analysis tools, businesses across the globe have improved their digital marketing strategies with the help of data acquired from social media platforms.
3. Time and cost saving: Big Data collects and stores data from variegated sources for producing actionable insights. Companies can easily save money and time with the help of advanced analytics tools for filtering out unusable or irrelevant data.
4. Increase profit margin: With the help of different types of Big Data analytics, companies can increase revenue with more sales leads. With the help of Big Data analysis, companies can determine how their products and services are faring on the market and how customers are receiving them. This can help them make more informed decisions about the areas that require investing time and resources.
Although invisible, there is more to Big Data than what meets the eye. It is an integral part and parcel of our everyday lives. Some stark examples of using Big Data are described below:-
Big Data has been extensively used in recent years in every aspect of our lives and every possible sector of global industries. It is one of the most valuable resources in the market, used to optimize any operational process. As an aspirant of data science, it is imperative to have the basic skills and knowledge about fundamental aspects of data analysis and to learn about the different types of data in analytics. You can take your first step into this lucrative career field by pursuing a reliable course or undergoing professional training. A great way to start is by taking part in KnowledgeHut’s Big Data training.
Big Data can be categorized into three parts
The 4 main components of Big Data are
Big Data has the following 6 characteristics
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