Data includes quantities, letters, or symbols on which a computer performs operations and which can be stored and communicated as electrical signals and recorded on magnetic, optical, or mechanical media. In the current digital era, businesses deal with huge volumes of data which is collectively referred to as Big Data.
In other words, the term “Big Data” refers to a large amount of data that cannot be processed or stored by processing equipment or conventional data storage. Big Data is generated on a massive scale, and it is being processed and analyzed by many global corporations in order to unearth insights and enhance their businesses.
Increasing Demand For Big Data Technology
Big Data is one of the most-used buzzwords in the Tech industry. Many Government agencies, businesses, Health care providers, and a slew of other organizations are currently focusing on Big Data to improve their operations and propel their growth. Experts believe that in the near future, the entire digital world will have grown to 44 zettabytes.
Big Data analytics enables businesses and organizations to make good use of massive amounts of data. It helps companies spot current market trends, patterns, and connections that would be difficult or impossible to detect with traditional data-processing methods. As a result, Big data professionals are in high demand. However, if you wish to work in this industry, you must first learn about the features and basics of Big Data.
What Are The Types Of Big Data?
Big data is often divided into three types:
- Structured Data- As the name suggests, It has a well-defined structure, follows a regular order, and is built in such a way that it can be easily accessible and used by either a person or a computer. Structured data is typically stored in Databases with well-defined columns.
- Semi-structured Data- It represents another form of structured data as It shares some of the characteristics of structured data. However, the majority of this type of data lacks a specific structure and does not follow the formal structure of data models like an RDBMS.
- Unstructured Data- This is a distinct type of data model, and as the name suggests, it lacks a structure and does not adhere to the formal structural norms of data models. It doesn’t even have a consistent format, as it is constantly changing. It may, however, contain data and time-related information on rare occasions.
Characteristics & Components Of Big Data
- Key Characteristics Of Big Data
The characteristics of Big Data can be explained by 5V’s as follows:
- Volume- The enormous amounts of data generated every second by social media, cell phones, autos, credit cards, M2M sensors, photos, video, and other sources are referred to as volume. Currently, we employ distributed systems to store data in many locations and then bring it all together with the help of a software framework such as Hadoop.
- Variety- Big Data is generated in a variety of ways, as previously discussed. In contrast to traditional data such as phone numbers and addresses, the most recent trend in data is in the form of images, videos, and audio, among other things, with around 80% of data being fully unstructured.
- Veracity- The term “veracity” refers to the data’s degree of trustworthiness. Because a large portion of the data is unstructured and unimportant, it must find another way to filter or translate it, as data is critical in corporate development.
- Value- The most important subject on which we must focus is value. It’s not only the amount of data we keep or process that’s a problem. It’s the amount of valuable, dependable, and trustworthy data that needs to be saved, processed and evaluated to uncover insights.
- Velocity- Velocity- Last but not least, velocity is one of the most critical characteristics of Big Data compared to the others. There is no purpose in investing so much in having to wait for data. As a result, one of the most important aspects of Big Data is the ability to give data on demand and at a faster rate.
- Crucial Components Of Big Data
Some of the main components of Big Data include:
The process of gathering and preparing data is referred to as ingestion. You need to use the ETL (extract, transform, and load) method to organize your data.
Identify your data sources, decide whether you’ll collect data in batches or stream it, and prepare it through cleansing, massaging, and organizing throughout this step. When acquiring data, you do the extract process, and when optimizing it, you perform the transformation process.
Businesses need to store the information once they’ve obtained it. The load procedure, the final phase of the ETL, will be completed here. Depending on your needs, you’d store your data in a data warehouse or a data lake. This is why, before beginning any Big data project, you must first understand your organization’s goals.
This step involves the evaluation of your Big data process to provide valuable insights for your company. Prescriptive, predictive, descriptive, and diagnostic analytics are the four types of Big data analytics. To examine the data, you’d employ Artificial Intelligence and Machine Learning algorithms.
This is the last step in the Big data process. You must share your findings with others when you have studied the data and discovered the insights. To effectively share your insights with a non-technical audience such as stakeholders and project managers, you’ll need to use data visualization and data storytelling.
Your Big data implementation would be incomplete if any of these components were missing.
Big Data is currently one of the most in-demand technologies. Businesses in various industries are seeking ways to use Big Data to improve their operations, attract more consumers, and stay ahead of the competition.
Volume, velocity, and variety are the first three characteristics of Big data, while Variability, authenticity, visibility, and value are further properties. However, understanding the properties and characteristics of Big Data is essential for companies in the current, informative world to effectively learn how to use and apply it to enhance their business.