What Is Hadoop: Use Cases, Challenges, and Benefits Explained

Hadoop is a well-known term that has gained prominence in the digital age. The Hadoop framework is essential when anyone can create massive amounts of data in a matter of seconds. You can learn all about Big Data and Hadoop with the Big Data Course. You may have wondered what Hadoop is or what the fuss about it is. This article will provide the answers.

What is Hadoop?

Hadoop is a framework that makes use of parallel processing and distributed storage to store and manage large amounts of data. It is the software most used by data analysts to handle big data, and its market size continues to grow. There are three components of Hadoop:

1. Hadoop HDFS is Hadoop Distributed File System (HDFS). It’s the storage unit.
2. Hadoop MapReduce- Hadoop MapReduce, is the processing unit.
3. Hadoop YARN — Hadoop YARN can be used as a resource management unit.

Hadoop Use Case

This case study will show you how Hadoop can be used to combat fraud. Let us look at the case of Zions Bancorporation. Their biggest challenge was how to utilize the Zions security team’s methods to stop fraudulent activities. They used an RDBMS database, which was not able to store or analyze large amounts of data.

They could only analyze very small amounts of data. With so many customers flooding in, they were unable to keep track of everything. This made them vulnerable to fraud.

They started to use parallel processing. The data was not structured and analysis was impossible. They had a lot of data, which they could not access in their databases.

The Zions team was able to use Hadoop to bring together all the data and keep it all in one place. They were also able to analyze and process the unstructured data they had. Hadoop made it easier to analyze different data formats and save time. Zions’ team was able to detect all types of malware, spears, and phishing attempts, as well as account takeovers.

Hadoop: The challenges

Although Hadoop is a marvelous tool, it’s not all roses and butterflies. Hadoop has its problems, like:

• It is a difficult process. MapReduce functions are written with Java if you want to run queries in Hadoop’s file system. This is a non-intuitive process. The ecosystem has many components.
• Each dataset is different. Hadoop is not able to give you an “all-purpose” advantage. Different components work differently and you will need to learn how to deal with them.
• MapReduce’s capabilities are limited. MapReduce is a great programming model. However, MapReduce is file-intensive and not ideal for data analytics or real-time interactive iterative tasks.
• Security is a concern. There are a lot of data available, and many of them are sensitive. Hadoop must still include proper authentication, encryption of data, provisioning, regular auditing, and other security practices.

https://medium.com/r/?url=https%3A%2F%2Fyoutu.be%2Fn3qnsVFNEIU

Five Benefits of Hadoop in Big Data

Hadoop was designed to handle big data. It’s not surprising that Hadoop offers so many benefits. These are the five most important benefits of Hadoop:

• Speed. Complex queries can be run in a matter of seconds with Hadoop’s concurrency processing, MapReduce model, and HDFS.
• Diversity. HDFS in Hadoop can store diverse data formats such as structured, semi-structured, and unstructured.
• It is cost-effective. Hadoop is an open-source data framework.
• Resilient. Resilient. Data in a node is replicated in other cluster nodes, which ensures fault tolerance.
• It is easily scalable. You can add servers easily because Hadoop works in a distributed environment.

Conclusion

Hadoop is a popular Big Data technology that can store, process, and analyze large data sets. This article will explain what Hadoop is and how it evolved. You understood the basics of Hadoop, its components, and how they work.