What Is The Difference Between Big Data And Hadoop?

Table of Contents

• Hadoop Vs Big Data Definitions
• Benefits Of Big Data Vs Hadoop
• Applications Of Big Data Vs Hadoop
• Conclusion
• Bonus Points

Have you ever wondered why you get such specific ads when scrolling through Instagram or Facebook? In fact, not just ads but even your explore page is specially curated to hold your attention and keep you on the app for a long time. All of this is thanks to data. Instagram collects data from you, understands your preferences, and shows you content you will like. The data that these large companies collect is often categorized as Big Data, and to manage this big data technologies like Hadoop are implemented.

If you are new to the world of data, this article will help you understand the difference between Big Data and Hadoop. You will see how they work together and we will also share with you a few free resources to help and learn more about Data.

Hadoop Vs Big Data Definitions

What Is Big Data?

Big Data refers to all the huge amounts of data collected and stored by an organization. This is data that goes beyond a single database. It’s ever-growing and businesses have to use various software applications to manage and handle this data.

A lot of the decisions a company makes are made using big data. This type of data can help leaders and decision-makers read patterns and make predictions for the future of the company.

You can pursue a career in Big Data Analytics, by learning more about the subject. If you don’t know where to start, you can explore this Basics of Data Science free course on our website. You can learn the basic principles of Data Analytics and how raw data can be used in businesses.

What Is Hadoop?

Hadoop is an open-source software framework that helps handle, sort, and extract relevant data from big data. This framework is built on a cluster of machines and is used for distributed storage and processing of big data.

This application can be used on structured data, unstructured data, and even semi-structured data. Hadoop can be used to access any specific data from big data and process it quickly.

Benefits Of Big Data Vs Hadoop

Advantages Of Big Data

If you are planning on learning Big Data technologies, you can learn for free with UniAthena. Our Diploma in Data Analytics course can teach you about data analytics from scratch.

Here are the advantages of Big Data:

• Informed Decision-Making: 

Big Data provides you with customer information that helps you understand their preferences and behaviours. This makes it easier for your business to make decisions that align with your customer’s expectations.

• Cost-Effectiveness: 

Big Data also helps businesses reduce costs by identifying inefficiencies in work and optimizing operations. It also identifies tasks that can be automated and helps reduce costs of manual labour.

• Fraud Detection: 

Real-time fraud detection can be conducted with the help of Big Data technology. 

• Increase In Productivity: 

Automating tasks also improves productivity in the workplace. Employees can shift their focus to more strategic and creative tasks and leave the monotonous tasks to the machines.

• Better Customer Support: 

Big Data gives you accurate information on your target customer which can be used to improve customer service. You can even personalize the experience for your customers and reduce response time with automation.

Advantages Of Hadoop

Hadoop can be a very useful tool for data professionals working with big data. Here are some advantages of Hadoop:

• It’s Fast: 

Hadoop uses a distributed file system that helps break down large files into smaller file blocks for better storage. This helps store these files better, making working for the overall application faster. 

• It’s Scalable: 

This breaking down of large files and storing them in multiple clusters also makes it easier for the application to be scalable. For smaller organizations, you can reduce the number of nodes and increase them as the organization scales up.

• It Helps Save Cost: 

Being an open-source application, Hadoop is extremely cost-effective. You don’t need to worry about expensive hardware or processors for Big Data processing.

• It Can Manage A Variety Of Data: 

You can store structured data, unstructured data, and semi-structured data in Hadoop. You also do not require a predefined schema to access the data.

Applications Of Big Data Vs Hadoop

Another difference between Big Data and Hadoop is the way these two are used. If you are unfamiliar with the working of these two together, the free learning Executive Diploma in Data Analytics course can help you gain a better understanding. With this course, you will also learn more about data privacy and ethics.

• Big Data Applications

Big Data is utilized by a lot of social networking sites like Facebook, Instagram, and even Spotify to collect user data and help personalize experiences. It is also used in industries like transportation, healthcare, and agriculture to enhance operational efficiency and make better decisions.

• Hadoop Applications

Apache Hadoop is used to sort through Big Data and process all the data to get better results. It can also store datasets at a low cost and can help you access relevant data in a short time. You can use the Hadoop ecosystem to conduct marketing research and analyse user data. You can also use this application to train AI and machine learning models.

Conclusion

Hopefully, this article helped you understand Big Data Analytics and Hadoop and how these two differ. After learning some basic Data Analytics from one of the free courses mentioned above, you can dive deeper into Data Visualization with this Essentials of Data Visualization course. This course will help you learn the tools and techniques and give you technical knowledge. You will also be able to get your certificate from CIQ, UK once you complete the course.

Bonus Points:

• Apart from Hadoop, you can also invest your time in learning Apache Spark as it is a robust alternative to Hadoop.
• By learning Big Data you can pursue careers like Data Scientist, Data Analyst, Software Engineer, BI Developer, and more.