Introduction to Big Data (Types, Characteristics and Examples)

The data collection process is rapidly advancing. With the help of IoT devices, we are now able to keep track of our every movement, including our health, sleep, exercise, transactions, and other aspects of our online presence. On other side, companies are collecting large amounts of data to understand users' patterns and provide personalized recommendations.

To truly benefit from this data, companies must constantly analyze and process it to extract value. Otherwise, the collected data will simply be treated as digital garbage. As the volume and diversity of data increases, traditional data analysis and data science methods become inadequate. This has led to the emergence of a new field called Big Data, which is specifically designed to analyze and process this massive amount of collected data. In this article, we will provide a brief overview of the idea behind Big Data.

What is Big Data?

Big Data refers to a level of complexity in data that makes it difficult for traditional storage, processing, and analytical methods to handle. Big Data cannot be accessed using standard SQL approaches or stored in traditional relational databases. As a result, advanced distributed systems have been developed to support the processing of Big Data.

The emergence of Big Data is directly linked to the growth of companies like Google and Facebook, which collect and process large amounts of data daily. These companies and others like them generate and store petabytes of data, which includes various forms of data such as text, images, videos and social media interactions. This data is highly diverse, unstructured and dynamic, which makes it challenging to process and analyze.

What are the types of Big Data?

Big Data can be classified into three main categories based on their structure:

  • Structured Data: This type of data is organized in a specific format that allows for easy access, management and analysis. It is usually stored in relational database management systems (RDBMS) in a tabular format consisting of rows and columns. This data can be queried and accessed using structured query languages (SQL) which allows for predictable and consistent results. Note: We will cover SQL concepts in a separate blog.
  • Unstructured Data: This type of data does not have a specific format or structure. It typically includes text, images, videos, or audio data. Unstructured data is usually stored in non-relational databases such as MongoDB and accessed using NoSQL commands which do not require a predefined schema.
  • Semi-structured Data: This type of data contains some structure but cannot be represented in a tabular format with rows and columns, like in XML or JSON format datasets. It may have a specific format and contains fields, but it's structure may not be predefined. For example, personal data can be recorded as:
<rec><name>Ravish Raj</name><sex>Male</sex><age>25</age></rec>

JSON Data Example:

[
	{
		color: "red",
		value: "#f00"
	},
	{
		color: "green",
		value: "#0f0"
	},
	{
		color: "blue",
		value: "#00f"
    }
 ]

Examples of Big Data

Many companies collect and process large amounts of data, known as Big Data, for various purposes. Some examples of popular sources of Big Data include:

  1. Stock Exchange Data: Investment firms and banks analyze data from various stock exchanges, such as purchase and sell orders, profit, loss, and other financial information to make informed investment decisions.
  2. Social Media Data: Social media platforms like Facebook, Instagram, and Twitter collect a wide range of data on their users, such as their interests, preferences, and demographics. They then use this data to personalize user experiences by customizing their news feeds.
  3. Black box Data: Aviation companies use black boxes (which are actually orange in colour) in aeroplanes and helicopters to record a wide range of information about a flight, such as the pilot's actions, altitude, and speed. If there is an incident or accident, they analyze the data from the black box to understand what went wrong and take steps to prevent similar incidents in the future.
  4. Map and Transport Data: Companies like Google Maps, continuously collect data on the location and movement of devices that use the app. This data helps them provide the best routes to destinations, provide traffic updates and plan for transportation infrastructure.

Five Characteristics of Big Data to know which data is Big Data

The five most known characteristics of Big Data, also known as the Five V's of Big Data, are:

What are the five characteristics of Big Data, also known as Five V's of Big Data?

1. Velocity

Velocity refers to the rate at which data is generated, collected, and processed. For example, millions of users simultaneously watch videos on YouTube and the data about their activity, such as the videos they are watching, the time spent on the platform, and other information is stored every second, providing a real-time snapshot of usage on the platform.

Velocity is crucial for organizations to understand because it determines the resources needed for collecting, storing, and processing data. As the data is generated at a fast rate, the organization needs to have the capability to process and analyze this data in real time in order to provide meaningful insights.

2. Volume

Volume refers to the quantity of data being uploaded. Recently the world population crossed the 8 Billion mark and most of these people are connected to the internet. It produces tons of data regularly, requiring enormous databases to store and process them. Some estimations show that approximately 3 Quintillion bytes of data are recorded daily. 1 Quintillion is equivalent to 1 Billion Gigabytes (GBs), or 1 Million Terabytes (TBs). One can pause to digest this number. 

3. Variety

Data collected can be present in multiple formats like text data, video data, audio data, health data, film data, and many more. Let's take an example of Facebook/Meta; it contains textual posts, video reels, short films, profile pictures, and many more. With this wider variety, challenges grow exponentially. Variety also refers to the various sources of the dataset. Data comes from devices, people, the internet, processes, and sometimes from nature.

4. Veracity

The biggest problem with today's data is verifying its quality and authenticity. Data is collected from millions of different sources, making traceability extremely difficult. With the ease in availability of technology and ignorance about fact-checking, it becomes critical to know whether the information in the form of data is true or false.

5. Value

Value refers to the techniques using which we make data useful. Value need not always be monetary; sometimes, it can help verify the critical hypothesis, for example, in the medical or defence domains. Engineers and Scientists are investing a humongous amount of time in Big Data techniques to extract value from the data.


What are the technologies involved in Big Data?

Big Data is becoming increasingly important for businesses, requiring advanced technologies to store and process large amounts of information. To meet this demand, a variety of scalable databases and techniques have emerged, including distributed computing systems. These systems allow companies to effectively handle the vast amount of data they collect. The technologies used for Big Data can be divided into two categories: operational technologies and analytical technologies.

Operational technologies, such as MongoDB, Apache Cassandra, and CouchDB, support real-time operations on large datasets.

Analytical technologies, such as MapReduce, Hive, Apache Spark, and Massive parallel processing (MPP), provide the ability to perform complex analytical computations.

Among these solutions, Hadoop stands out as the leading framework that supports both operational and analytical technologies. As an open-source platform, Hadoop can scale from a single server to thousands of machines and provides distributed computing through simple programming. In our next blog post, we will dive deeper into the introduction of Hadoop.

What are the advantages of Big Data processing?

Large companies such as Google, Apple, Amazon and many others are expanding their presence in India due to the country's significant population. This population provides businesses with a valuable resource: data. With more data, companies can make more personalized recommendations, resulting in increased customer retention and even more data. This benefits not only businesses but also consumers through:

Personal Benefits:

  • Cost optimization: Investment management companies, like Moneycontrol, analyze vast amounts of data in real-time to provide detailed analysis of the stock market, helping us make informed investment decisions.
  • Time optimization: Google Maps utilizes big data to analyze traffic patterns and suggest the best routes to our destinations.
  • Risk management: Companies like Truecaller use big data to automatically block spam callers and protect us from potential risks.
  • Health management: The monitoring system on Apple Watches utilizes data to continuously analyze heartbeat patterns and alert us to any discrepancies.

Business Benefits:

  • Customer retention: Utilizing big data allows companies to personalize advertisements, increasing customer retention and likelihood of product purchases.
  • Risk management: Companies analyze big data to identify and prevent potential cyber attacks.
  • Real-time problem-solving: Advances in big data analysis allow companies to quickly diagnose and resolve issues in real-time.

What are the challenges involved with Big Data?

Big Data engineers face several significant challenges, including:

  • Growth: The speed at which data is recorded can be too fast for traditional database management systems to process accurately and efficiently.
  • Storage: The sheer volume and variety of data collected poses a significant challenge, even with advanced databases.
  • Authenticity: Ensuring the authenticity of data sources is a major concern as the number of data collection points is vast and it becomes challenging to trace the origin of the data. Spammers use this to spread misinformation.
  • Security: With data being stored from millions or billions of devices, people, or processes, the risk of data leakage is high, which can expose sensitive information and lead to misuse. Ensuring the security of data systems is crucial.

Conclusion

In this article, we explored the growing field of Big Data, which is addressing the challenge of managing vast amounts of complex and diverse data. We discussed what constitutes Big Data, the types of datasets involved, the various technologies used in Big Data, and the significant challenges faced by engineers working with Big Data. Additionally, we highlighted the personal and professional benefits of Big Data. In our next blog post, we will delve further into the Hadoop framework, which is a key technical support for Big Data. We hope you found the article informative and enjoyable.

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