What Is the Hadoop Distributed File System?

Key takeaways

The Hadoop Distributed File System (HDFS) is an open-source module used to store, organize, and monitor large data sets across clustered computers called nodes.

• The Hadoop Distributed File System is the main storage module used by Hadoop clusters to distribute data sets and process them in parallel.

• This system is uniquely fault-tolerant and protects information from loss or corruption by replicating compromised data sets across other nodes.

• You can learn foundational skills like SQL, Java, and other programming languages to prepare for a career that uses this technology.

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 HDFS. Build job-ready skills for an in-demand career by enrolling in the IBM Data Engineering Professional Certificate.

Big data is typically defined by its volume, variety, and how quickly it’s produced, and these large data sets often take a long time to parse, or process. 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, finding actionable and informed strategic decisions from them. Apache Hadoop was 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.

What is Hadoop?

Hadoop is used to process, and analyze immense sets of data. It does this through a unique architecture of clusters that process the data in parallel to each other. Each cluster processes data quickly, 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).

What is the Hadoop Distributed File System used for & how does it work?

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 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.

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

HDFS is designed for high-throughput access to large data sets where each file's metadata must be stored in the clusters’ memory. When a system generates millions of small files, the metadata overhead can exhaust the memory capacity and degrade the efficiency of the entire cluster.

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 you work with many small files instead of several immense ones, Hadoop might not be your best option.

Where is Hadoop used in real life?

One example of a career that uses Hadoop and the Hadoop Distributed File System is a data engineer. Data engineers create the infrastructure 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.

Hadoop in action

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.

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

Keep learning with these free resources

Sharpen your big data skills and continue learning with these free resources:

• Review our Data Science Learning Roadmap: Beginner to Expert (2026)

• Watch our video on YouTube: What is Data Science? - Advice for New Data Scientists by IBM

• Subscribe to our newsletter: Career chat

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