Explained Big Data in Data Science: How It Works

Due to the digital age, people generate an enormous amount of data every day. As social media interactions and online transactions become more prevalent, as do sensor readings and mobile app use, the amount, velocity, and variety of data produced exceeds the capacity of traditional data processing methods. Data science and big data are closely related fields. “big data” refers to the ever-increasing amounts of varied information collected and stored. A data scientist uses scientific methods, processes, algorithms, and systems to make sense of data.

This article will explore what big data is in data science and how to work it, and also examine the benefits of big data. 

What is Big Data?

A big data set is a dataset that is so large and complex that traditional data processing methods cannot handle it. The “three Vs” of big data can be summarized as follows:

• Volume: Big data datasets are typically large, containing billions or trillions of records.
• Variety: There are various types of data in big data datasets, including structured, semi-structured, and unstructured data.
• Velocity: Datasets created by big data are often generated rapidly, making it challenging to keep up with them.

Examples of big data include:

• Web search logs
• Social media data
• Sensor data from IoT devices
• Financial transactions
• Customer data
• Medical records
• Genomic data

The History of Big Data

Big data is relatively new, but large data sets have existed since the 1960s and ’70s when the first data centers and relational databases emerged. In 2005, people realized how much data was generated by Facebook, YouTube, and other online services. That year, the Hadoop framework was explicitly developed for storing and analyzing big data sets. During this time, NoSQL also gained popularity.

Big data has grown because open-source frameworks like Hadoop (and Spark, more recently) have made it easier to work with and less expensive to store big data. The volume of big data has grown exponentially since then. There are still massive amounts of data users generate, but it’s not just humans.

The Internet of Things (IoT) connects many objects and devices, gathering data on product performance and customer usage patterns. As machine learning emerges, more data is being produced. 

Although big data has come a long way, its usefulness has just begun. Cloud computing has opened up a new dimension to big data. A cloud-based application provides truly elastic scalability, allowing developers to create ad-hoc clusters for testing a subset of data on the fly. A graph database is also becoming increasingly important due to its ability to display a large amount of data efficiently and comprehensively.

Types Of Big Data

The three main types of big data are as follows:

• Structured data is arranged in a specific format like a database table. Structured data is typically easy to store, process, and analyze. Data from sensors, financial transactions, and customer records are examples of structured data.
• Unstructured data is data without a predefined format. Many unstructured data types exist, including text, images, videos, audio files, and others. Unstructured data is often more complex to store, process, and analyze than structured data. Social media posts, customer reviews, and medical images are examples of unstructured data.
• Semistructured data is a type of data that falls between structured and unstructured. Although semistructured data has some structure, it is not as rigidly structured as structured data. Data in JSON and XML formats are examples of semi-structured data.

How Big Data Works

There are two types of big data: unstructured and structured. Structured data is information already stored in databases or spreadsheets by the organization. It is frequently numerical. It is necessary to organize and format unstructured data in a predetermined manner. Social media data helps institutions collect information about customers’ needs by gathering data from social media sources.

The data can be collected from publicly shared comments on social networks and websites, and it can also be ordered via surveys, product purchases, and electronic check-ins. Thanks to sensors and other inputs, data can be collected from smart devices across various situations and circumstances.

Databases are often used to store and analyze big data, allowing for the analysis of large, complex datasets. These difficult data types are usually managed by software-as-a-service companies (SaaS).

Data Science Vs. Big Data

Data science combines statistics, mathematics, computer science, and domain expertise to derive insights from data. Using various tools and techniques, data scientists prepare, analyze, and solve real-world problems based on the insights they gain.

Big data is a critical component of data science. Data scientists use big data tools and techniques to process and analyze large datasets. However, it is essential to note that data science is not just about big data. Data scientists can use many types of data, not just big data. They can also work with smaller datasets.

Listed below are the key differences between big data and data science:

 

Feature	Big data	Data Science
Definition	Datasets that are too large or complex to be processed or analyzed by traditional data processing methods.	Data mining combines statistics, mathematics, computer science, and domain knowledge to extract insights from the data.
Focus	Analyzing, storing, and processing large datasets.	Solving real-world problems by analyzing data.
Tools and techniques	Tools and techniques for big data include Hadoop, Spark, and Hive.	Machine learning, statistics, mathematics, and computer science are some of the tools and techniques used by machine learning.
Applications	Among the applications are business intelligence, fraud detection, and scientific research.	There are a variety of applications, such as product development, marketing, and healthcare.

Benefits Of Big Data

Big data analytics can potentially revolutionize many industries and aspects of our lives. The following are some of the key benefits of big data:

• Improved decision-making: By providing insights into customers, operations, and markets, big data analytics can help businesses and organizations make better decisions. In addition to identifying which products are most popular with customers, retailers can use big data analytics to optimize their supply chain and open new stores.
• Increased efficiency: Using big data analytics, businesses, and organizations can automate tasks and processes, resulting in significant efficiency gains. Manufacturing companies can use big data analytics to identify and eliminate production bottlenecks.
• Reduced costs: By identifying areas where businesses and organizations can save money or improve their operations, big data analytics can help them reduce costs. Using big data analytics, healthcare providers can reduce readmission rates or improve billing efficiency.
• New product and service development: Using data analytics, products, and services can be developed that meet customer needs. By analyzing big data, technology companies can identify new features users want in their products.
• Improved customer service: Businesses and organizations can better understand their customers’ needs and preferences through big data analytics. Using big data analytics, a telecommunications company can identify customers likely to leave and offer them incentives to remain.

Big data analytics can transform how businesses and organizations operate. In addition to helping them make better decisions, it can help them reduce costs, improve efficiency, and develop new products.

Conclusion

In conclusion, Big Data is revolutionizing data science. The data offers organizations a treasure trove of insights for making better decisions, gaining a competitive edge, and improving customer service. Although handling Big Data presents challenges, advancements in technology and data science methodologies continue to make it easier and more manageable. Understanding Big Data is an advantage in today’s data-driven world and necessary for success.