Technology

What is Apache Hadoop and Where it is Used?

We are in the era of the data age, and there has been an abrupt increase in the size and scale of web data. Big data has distinct and complex unstructured formats.

This includes information from social media, websites, email and video presentations. Therefore, organisations need technologies to extract valuable intelligence from Big Data.

To process large data sets, there is a need to have technologies that can accurately and powerfully analyse the big data formats.

Google is one company that processes data using its Google File systems and MapReduce frameworks. These scalable frameworks motivated the Hadoop initiative. The Apache Hadoop allows the distributed processing of big data across many machines.

Apache Hadoop

Apache Hadoop is a powerful and scalable distributed computing platform that is widely used for storing, processing, and analyzing big data. Hadoop was created by Doug Cutting and Mike Cafarella in 2005, and it has since become an important tool for data scientists and analysts worldwide.

Apache Hadoop is a big data platform organisations use to manage and process large data sets. It helps perform various operations such as text analytics, structured and unstructured data analysis, machine learning, predictive modelling, and more. Apache Hadoop is popular for its high-performance processing capabilities for big data sets.

Hadoop is a programming language and framework that enables distributed data processing. It is written in Java and runs on the Linux system. It can process huge data sets and is designed to be simple, portable, and scalable.

Hadoop has several modules, like Hadoop Distributed File System (HDFS), MapReduce, etc. HDFS is the key element that connects the Hadoop framework with the operating system and provides the file system for storing the data. HDFS uses the block-level file system concept, which is very scalable.

Hadoop MapReduce is a framework that allows the execution of computations and provides fault tolerance. It works as the layer between the application programmes and the distributed storage mechanism. And finally, Hadoop’s Distributed Scheduler provides the job schedule, which plays an important role in the MapReduce framework.

Benefits of the Hadoop Framework

1. Scalability: Hadoop is a formidable tool for big data processing. Its distributed architecture allows it to handle enormous datasets with ease. Hadoop’s scalability is one of its strongest features, as it can smoothly transition from small-scale clusters to large-scale ones without compromising on efficiency. It’s a reliable solution for businesses that deal with a vast amount of unstructured data, such as social media platforms, e-commerce, and healthcare providers. With Hadoop, organizations can process, store, and analyze data in real-time, leading to better decision-making and improved business outcomes.

2. Fault tolerance: Have you ever experienced sleepless nights worrying about data loss? With Hadoop, you can say goodbye to those worries! This powerful tool offers fault tolerance, which means that it can handle hardware failures without any interruption to your data processing. Hadoop automatically replicates data across different nodes, ensuring that your information is safe and sound. You can rest easy knowing that your data is in good hands with Hadoop.

3. Flexibility: Hadoop’s ability to handle different data types is a game-changer in the world of data analytics. With its capability to manage structured, semi-structured, and unstructured data, businesses can now process and analyze data from various sources. This makes it possible to obtain a comprehensive view of the organization’s operations, customers, and market trends. As a result, businesses can make informed decisions and stay ahead of the competition.

4. Cost-effective: One of the great advantages of Hadoop is that it’s not only open-source, but it can also run on commodity hardware. This means you don’t need to invest in expensive hardware to use Hadoop, which can significantly reduce your costs.

Additionally, Hadoop is highly scalable, allowing you to add more nodes as your needs grow, making it a cost-effective option for businesses of all sizes. With Hadoop, you can save money without compromising on performance, making it a win-win situation for your business and your bottom line.

5. Speedy processing: Time is precious, and Hadoop understands that! This framework excels at parallel processing, which means it can perform tasks concurrently and deliver results quickly. With Hadoop, you can process large and complex data sets quickly and efficiently, without any long waiting times.

Whether you’re dealing with structured or unstructured data, Hadoop’s distributed computing system allows you to process data in parallel, making it easy to scale your processing power as your data grows. By using Hadoop, you can save time and resources, and make data-driven decisions faster than ever before.

Conclusion

The Hadoop framework has proven its efficiency in most organisations, but as much as it is fast and cost-efficient, legacy systems will be required to complement its use. The features of Hadoop will make the technology attractive.

Therefore, it is upon the organisations to take advantage of the framework. With Hadoop, organisations will make important predictions by sorting out and analysing large amounts of data. There is no doubt that Hadoop is the core platform for structuring big data sets.

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Raj Maurya

Raj Maurya is the founder of Digital Gyan. He is a technical content writer on Fiverr and freelancer.com. When not working, he plays Valorant.

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