What Is a Hadoop Cluster?

Successful businesses regularly look for ways to improve their processes and increase customer value; data is a key part of meeting that goal. The amount of data generated daily is unlike anything we’ve seen before. This volume comes with unique challenges concerning the cost, time, and effort involved in storing, processing, managing, and analyzing these immense data sets. One solution to this challenge is Apache Hadoop, an open-source software library that’s designed for the processing and analysis of big data sets. Learn more about Hadoop, what a Hadoop cluster is, the Hadoop cluster architecture, and how to build a Hadoop cluster.

What is Hadoop?

Hadoop is an open-source processing framework that handles immense sets of data through the use of clusters of computer servers. It’s designed to support data scientists using data for deep learning and predictive analytics. Hadoop does this by taking massive processing or analytics tasks and breaking them down into smaller tasks that occur at the same time.

Hadoop is highly scalable, meaning you can continually add new computers to the framework to increase storage and processing without the need for expensive or complex infrastructure. Due to a specific structure known as a Hadoop cluster, Hadoop has the capability to handle these giant data sets.

What is a Hadoop cluster?

A Hadoop cluster is a group of computers, called nodes, all linked together to perform parallel tasks. Hadoop clusters are unique in that they are designed specifically for the processing, storage, and analysis of big data sets. The clusters accomplish this through a master-worker setup, in which one node directs the storing and processing of data within the worker nodes. The master node divides data into smaller chunks to distribute among the nodes for processing. Because the distributed data replicates across different nodes, it's protected from software failure and allows the clusters to protect and recover data if any computer has an issue.

Hadoop cluster architecture comprises four parts:

• Hadoop Distributed File System spreads the data among the clusters and allows each computer to natively support the large sets.

• Yet Another Resource Negotiator manages the clusters and assigns tasks.

• Hadoop Common provides Java libraries.

• MapReduce provides the computers with the framework necessary to complete parallel computation on the same data set.

What is a Hadoop cluster used for?

Hadoop clusters distribute immense data sets and work on them simultaneously. What makes them especially unique is that their design enables them to address any issues or failures at the computer level, thereby protecting the data from corruption or loss. The clusters can handle structured, unstructured, and semi-structured data, which makes Hadoop ideal for handling the different types of data generated by a business.

To build a Hadoop cluster, you need to configure a set of computers to all work together on the same network. The first step is installing Hadoop and then assigning particular roles to each computer. You’ll assign one machine as the NameNode and one machine as the ResourceManager, both of which are the masters of the cluster. The remaining machines are the workers and act as DataNode and NodeManagers.

Who uses a Hadoop cluster?

Professionals who work in big data environments use Hadoop and Hadoop clusters to analyze data. It’s especially useful across industries that also use cloud data processing services since Hadoop’s design means the immense data has protection from loss or failure and is analyzed quickly. Hadoop clusters find use in fields where data professionals employ big data analytics, machine learning, log analysis, data warehousing, and recommendation systems.

Pros and cons of a Hadoop cluster

Hadoop clusters have many benefits, including their ability to increase the speed of big data processing tasks. They are easily scalable; simply add more computers to create larger clusters. The clusters themselves are relatively inexpensive to set up and maintain. Additionally, they can work with a wide variety of data types, and because the data replicates across clusters, there’s a low risk of data loss or corruption.

Some cons of Hadoop include that it tends to struggle with lots of small files rather than several big files. It is also vulnerable to security breaches. Hadoop’s clusters cannot handle real-time processing but only support batch processing. Plus, because Hadoop’s clusters read and write data from disks, the processing overhead required tends to become quite expensive.

How to get started with a Hadoop cluster

If you’re interested in learning more about using Hadoop and Hadoop clusters in your career or job, first you’d want to learn more about Hadoop and develop your skills in it. This is possible through installing Hadoop and practicing with the software. You might also want to look into bootcamps, webinars, or tutorials to learn more foundational knowledge about Hadoop and programming in general.

Careers that use Hadoop include data engineer roles. These professionals are responsible for designing, installing, and maintaining the infrastructures data scientists use for analysis, processing, and storage. Most data engineers have a bachelor’s degree in fields such as information technology or applied science. They also usually have certifications specializing in different types of software or programming. The average annual salary for a data engineer is $106,514 [1].

Getting started with Coursera

Sharpen your data science skills and learn more about Hadoop and Hadoop clusters with courses and Professional Certificates on Coursera. With options like the University of California San Diego’s Introduction to Big Data or IBM’s IBM Data Engineering Professional Certificate, you’ll learn about the foundational skills and knowledge needed to successfully implement Apache Hadoop in your business or career. Explore the different facets of data science and how it benefits businesses in improving processes and customer satisfaction