What Is the Hadoop Distributed File System?

Big data is typically defined by its volume, variety, and how quickly it’s produced. These qualities make it almost impossible for a typical data analytics system to store, maintain, and analyze successfully. However, businesses find a lot of value in these big data sets, as the insights they hold allow them to create actionable and informed strategic decisions. Apache Hadoop is an open-source software designed to specifically address the unique needs and challenges of big data through its modules and architecture, one of which is the Hadoop Distributed File System.

Read on to learn more about the Hadoop Distributed File System, including the benefits of an Apache Hadoop Distributed File System, and how to find a career that uses Hadoop Distributed File System (HDFS).

What is Hadoop?

Hadoop is an open-source framework that data professionals use to store, process, and analyze immense sets of data. It does this through a unique architecture of clustered computers that process the data in parallel to each other. Hadoop is easily scalable and can work across one cluster of computers to thousands of them. Each cluster processes data quickly and efficiently, protecting the information from loss or corruption by replicating it.

Hadoop works in part because it uses the Hadoop Distributed File System (HDFS) to spread data sets among the clusters. The HDFS is one module of four; the others include Hadoop Common, MapReduce, and Yet Another Resource Negotiator (YARN). In this article, we’ll focus on the HDFS and how it benefits the entire Hadoop system.

What is the Hadoop Distributed File System?

An Apache Hadoop Distributed File System is one of the modules used by the Hadoop software to successfully process, store, and analyze giant data sets. The Hadoop Distributed File System is the main storage used by the Hadoop clusters to distribute and monitor the data sets. While each cluster connects to the HDFS, they work independently so that the data processes in parallel.

The Hadoop Distributed File System manages, organizes, and stores large data sets by distributing them among the connected computer clusters. HDFS monitors the data and clusters for any issues or faults and then addresses those faults through the dissemination and replication of the compromised data set to ensure it’s not lost. The Hadoop Distributed File System works on standard hardware.

The Hadoop Distributed File System has the unique ability to write and read data on the server simultaneously instead of waiting for read/write actions that would make it more difficult to work with giant data sets.

What is the Hadoop Distributed File System used for?

The Hadoop Distributed File System’s use is mainly as the framework for the rest of the Hadoop ecosystem to operate from. The HDFS connects all of the different computer nodes and distributes the data sets among them for parallel processing. The Hadoop Distributed File System is best for batch processing. HDFS is uniquely fault-tolerant, ensuring that the data the nodes process stays protected from loss, corruption, or any other issues. When a node in a cluster becomes compromised, the HDFS immediately replicates the data across other nodes. It’s also designed to process immense data sets that might otherwise be too large for efficient processing and analysis.

Who uses the Hadoop Distributed File System?

Businesses with consumer bases that create immense data sets use Hadoop and the Hadoop Distributed File System. They use the insights generated from the data to make informed decisions concerning marketing, business operations, and processes. Data scientists working with marketers and other business professionals use the Hadoop Distributed File System to successfully aggregate, analyze, and store all of the giant data sets generated daily.

Pros and cons of the Hadoop Distributed File System

The Hadoop Distributed File System has pros and cons that are important to consider. Some of its pros include the fact that it’s capable of storing and processing many different types of data, both structured and unstructured. Its use of clusters means that data processing is efficient and fast. The HDFS’s ability to work on standard or low-cost hardware makes it accessible to many data professionals. Its ability to detect faults and replicate data makes it robust against data loss or corruption. It’s also scalable and flexible, making it ideal for different types of businesses and their needs or goals.

A potential drawback of the Hadoop Distributed File System is its struggle to efficiently process small files. If your business generates many small files instead of several immense ones, Hadoop might not be your best option. The HDFS also works best for batch processing and can’t support real-time analytics or processing.

How to get started with the Hadoop Distributed File System

If you’re interested in working with the Hadoop Distributed File System, first you’ll want to learn more about the different computer science skills that might be helpful when you start diving into Hadoop. Some of these foundational skills include SQL, programming languages, and big data concepts. Hadoop itself runs on Java, so it's helpful to understand this language. Once you feel comfortable with key skills, download Hadoop and practice with it independently to get hands-on experience.

One example of a career that uses Hadoop and the Hadoop Distributed File System is a data engineer. Data engineers create the infrastructures and systems that make the processing and analysis of big data possible. They tend to work closely with data scientists. Data engineers create, maintain, and upgrade these software frameworks. Data engineers typically have a bachelor’s degree in information technology or computer science and often obtain certificates in specific software. The average annual salary for a data engineer is $106,544, according to Glassdoor [1].

Getting started with Coursera

Sharpen your big data skills and learn about the foundational knowledge required for a career using Hadoop and Hadoop Distributed File Systems with courses and certificates on Coursera. With options like the IBM Data Engineering Professional Certificate, you’ll learn about the key skills and knowledge needed to start a career in big data.