In this article, we will discuss the characteristics of big data. What do the character and format of data in various resources in organizations mean, and how are they useful? Big data refers to the different types of data that are collected from different resources. Data will be structured, unstructured, and semi-structured, which is used in growing technology over time.
These types of data are large in quantity and complexity, with varying speeds and types. The data management system cannot store, process, or analyze them. Big data is the fastest-growing sector of technology globally. Big data refers to analyzing large amounts of data to create organizations that use it in different aspects. To understand big data deeply, you must be familiar with its core characteristics. Understanding the characteristics of big data will also help advance the concept of big data. In this article, we will discuss the definition, characteristics, types, components, and advantages of big data.
What is big data?
Big data is defined as the collection structured and unstructured data source from servers, customers information, order, and purchase data. All financial transactions, ledger history, and employee records. In big companies, these data is continuously increasing day by day.
large amount of data is not so important for companies; the most important thing is that how to manage and analyze this huge collection of data. To analyze these data, companies use different types of strategies and understand the patterns of the data that lead to better business decisions.
Types of Big Data
Companies collect different types of data from different resources, Raw data can be classified in three major categories:
Structured Data
These types of data is predefined of organizations. It presents things like tabular form and schema form, and it is easier to understand and analyze them. Structured data is valuable and it is collected from various resources in the database.
Unstructured data
It is not predefined conceptual definitions. These types of data is not easily understood and analyzed. In the form of big data, it can be audio file, video file, or mobile activity. There is no structured or tabular form.
Semi-structured data Semi-structure data is also called hybrid data. It is a combination of structured and unstructured data. There are some characteristics of structured data but there is no valuable information that can be defined structured data. It can be defined as relational database or normal structure of database. JSON and XML are the best example of semi-structured data.
Characteristics of Big Data
There are five v’s characteristics of big data, explanations.
5 V’s of Big Data
Volume
- As the name suggests, there is a lot of data
- There is huge amount of defined data.
- The size and value of data play a very crucial role. If the volume of data is very large, then it will be considered “big data.“Big Data”. Big data is dependent on the volume of data.
- The volume of big data is generated from different resources like business process, machines, social media, human interactions and networks. For example: social media sites like Facebook generate approximately million messages, which it upload each day. Big data handles large amount of data.
Variety
As we discussed earlier in this article, big data is defined in three major categories: structured, unstructured, and semi-structured. In this characteristic, data is collected from different resources. Data is collected in the form of arrays, PDF files, emails, audio files, photos, and videos.
Value
This is an essential part of big data. It is defined as the value of the data. The data is valuable and reliable because we can store, process, and analyze it.
Velocity
Data is created in real-time with the help of velocity. That means you can say that velocity provides the speed to create the data in real time. It links the incoming data speed sets to the rate of change.
Velocity in big data provides the flow of data from sources like logs of applications, business processes, network devices, social media sites, and mobile devices.
Veracity
Veracity provides information about data, that is, how much data is valuable. In big data, veracity is one of the main components that helps ensure the accuracy of the data. Veracity provides for the filtration of data in many ways. You can say that veracity is the process of handling and managing the data in an efficient and easy way. Example: Facebook and Twitter posts with hashtags.
Most Important components of Big Data
Ingestion
It refers to the process of collecting and preparing the data from resources. In this, component data will go through three main stages of preparation: extract, transform, and load (ETL). You will identify your data, which is what the sources of data are, and where to gather the data in batches or streams.
Storage
Storage is the final step of the ETL process; you need to store your data for the loading process. It depends on your requirements for where you store your data, like a data warehouse or a data lake.
Analysis
In these components, you will analyze your data; that is what the value is for your organization. In the analysis of big data, there are four stages: predictive, descriptive, and diagnostic. Artificial intelligence and machine learning algorithms use these steps to analyze the data.
Consumption
These are the final components needed to process the big data. After analyzing the data and finding the correct value of the data, you can share it with others.
Conclusion
Big Data is the most-demanding technology in current times. Various companies are utilizing big data for their operations, customer gathering, and for competitors. In big data, there are three main characteristics: volume, velocity, and variety. Other characteristics are variability, veracity, and value. I have already defined these characteristics earlier in this article.
follow me : Twitter, Facebook, LinkedIn, Instagram