What Is Hadoop Architecture?

Hadoop architecture operates as a framework built of multiple computers, known as clusters. It allows for the storage and analysis of large data sets.

 Big data is key for your business in making better-informed decisions and plans, and consumers today generate more data than ever. To process and analyze this information, your business needs frameworks designed to handle the immense volume and velocity of the created data. Apache Hadoop is an open-source framework that allows clusters of networked computers to break apart immense data sets and analyze them for further use.

Its unique architecture makes it key for processing immense, unstructured data sets quickly and efficiently, all while protecting your data from loss or corruption due to the design of the computer clusters. Additionally, you can combine the abilities of Hadoop with machine learning and AI to uncover patterns within large data sets while also formulating predictions. 

What is Hadoop?

Hadoop is an open-source framework that allows you to process immense data sets within clusters of networked computers. Data scientists and other professionals use Hadoop for data analysis, predictive analytics, machine learning, and data mining. Big data and Hadoop interact because Hadoop has the ability to process unstructured and unlabeled data of all different kinds. One of the things that makes Hadoop unique is its clusters of computers. Each cluster processes data in parallel, providing security and efficiency. Also, it’s easily scalable by adding more computers or clusters.

What is Hadoop architecture?

Hadoop architecture comprises four modules: the Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), Hadoop Common, and MapReduce. These components work across a cluster of computers set up on the same network. Giant sets of data break up across the cluster for parallel processing. HDFS takes these data sets and spreads them across the cluster, duplicating the sets at least twice. This way, if one of your data sets becomes corrupted, it’s not lost. Instead, the file system replicates the remaining copies of the set so that it’s always available in multiple places.

Once HDFS spreads the data set across the cluster, MapReduce processes and converts it, combining individual subsets into a more manageable data set. Finally, YARN monitors the cluster and assigns tasks as needed.

Hadoop architecture follows a leader/follower structure. One computer in the cluster is the NameNode, which directs processing, and the follower computers do the actual processing.

What is Hadoop architecture used for?

Hadoop architecture’s main function is to process immense sets of big data for use in data analytics and data management. It’s unique in that its architecture protects data from corruption or loss by replicating the sets across multiple computer clusters. It’s capable of processing different file types at once. These clusters allow your business to process giant data sets quickly and efficiently.

Some other uses of Hadoop architecture include:

• Data lakes: Hadoop’s ability to store data without needing to pre-process means you can partner this technology with a data lake, which stores huge amounts of unstructured data.

• Data storage: Hadoop allows you to store all different types of files, which allows your company to process data in ways that will support your business.

• Big data analytics: Hadoop offers robust data analytics through its efficient processing of immense data sets and parallel processing.

• AI and machine learning: Data professionals use the data processed by Hadoop to support machine learning tasks.

• Risk management: Industries with financial risks, such as banks or insurance companies, often use Hadoop as a risk management tool.

Who uses Hadoop architecture?

If you’re a data professional, you might use Hadoop architecture to process immense data sets that would otherwise be impossible to parse. You can use Hadoop for its storage, processing, and big data analytics tools. Hadoop architecture has applications across various industries, including security, finance, health care, and retail.

Pros and cons of Hadoop architecture

One benefit of Hadoop is the fact that it’s incredibly scalable. You can easily increase the amount of clusters in the ecosystem by adding new computers without the need for expensive infrastructure. Hadoop’s architecture is also designed to detect and address any failures without losing your data. For example, if a node—a computer server—within the cluster fails, the data on that node has not been lost because that same data has been stored on another node within the cluster. The analysis of your data can continue without interruption. Hadoop’s other key benefit is that its ability to handle different types of structured and unstructured data makes it very flexible, simplifying storage and analysis processes.

Some cons of Hadoop architecture include the fact that Hadoop struggles to process lots of small files versus several immense ones. If your business tends to generate small data files, Hadoop might not be your best option. In addition, Hadoop’s design is mainly for batch processing, and it cannot do any real-time, data-stream processing.

How to get started with Hadoop architecture

If you’re interested in working with Hadoop architecture, the first step is to consider your background and expertise with big data. It might be helpful to grow familiar with different computer programming languages, such as SQL. Then, checking out online courses, blogs/forums, or certification programs can allow you to learn the foundations of Hadoop and how Hadoop architecture interacts with big data. Finally, you can download Hadoop and practice it on your computer.

If you’re working in big data, you will most likely use Hadoop as part of your daily tasks and responsibilities. One example of a career in this field is a data engineer. As a data engineer, your responsibilities could include creating, utilizing, and managing different infrastructures to store and analyze data sets.

If you’re considering a career as a data engineer, you’ll typically need a bachelor’s degree in a field such as computer science or applied math. Many data engineers also have certifications in specific programming languages. If you choose to work as a data engineer, you can expect to earn an average annual salary of $106,574 [1].

Getting started with Coursera

Sharpen your big-data skills and learn more about the foundational knowledge required for a career in data engineering with courses and Certificates on Coursera. Coursera provides access to high-quality boot camps, college courses, and Certificates offered by the top universities and companies worldwide.

With options such as the University of California San Diego’s Introduction to Big Data and IBM’s IBM Data Engineering Professional Certificate, you’ll learn about the key aspects of big data processing, storage, and analysis and how those skills might fit into your future data career. Explore all the courses available on Coursera today and find your new passion.