Apache Hadoop

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What is Apache Hadoop?

Apache Hadoop is an open source software framework for running distributed applications and storing large amounts of structured, semi-structured, and unstructured data on clusters of inexpensive commodity hardware.


Hadoop is credited with democratizing big data analytics. Before Hadoop, processing and storing large amounts of data was a challenging and expensive task that required high-end, proprietary hardware and software. Hadoop’s open-source framework and its ability to run on commodity hardware made big data analytics more accessible to a wider range of organizations.

Since Hadoop was first released in 2006, cloud computing, as well as containerization and microservice architectures, have significantly changed the way applications are developed, deployed, and scaled. While Hadoop is now considered a legacy technology for big data, the framework still has specific use cases.

Techopedia Explains the Hadoop Meaning

Techopedia Explains the Hadoop Meaning

The Hadoop software framework was created by Doug Cutting and Mike Cafarella and was inspired by how Google processed and stored large amounts of data across distributed computing environments.

The name “Hadoop” doesn’t stand for anything; Doug Cutting named the framework after his son’s toy elephant. The unique, playful name inspired an ecosystem of open-source tools for obtaining actionable insights from large, complex datasets.

While Hadoop’s role in projects today may be limited because of more advanced frameworks like Apache Spark, Hadoop still plays a role in scenarios where organizations have already invested in Hadoop infrastructure and still have specific use cases for using the Hadoop ecosystem.

Apache Hadoop vs. Apache Spark

Hadoop’s ability to scale horizontally and run data processing applications directly within the Hadoop framework made it a cost-effective solution for organizations that had large computational needs but limited budgets.

It’s important to remember, however, that Hadoop was designed for batch processing and not stream processing. It reads and writes data to disk between each processing stage. This means it works best with large datasets that can be processed in discrete chunks, rather than continuous data streams.

While this makes Hadoop ideal for large-scale, long-running operations where immediate results aren’t critical, it also means that the framework may not be the best choice for use cases that require real-time data processing and low-latency responses.

In contrast, Apache Spark prioritizes in-memory processing and keeps intermediate data in random access memory (RAM). This has made Spark a more useful tool for streaming analytics, real-time predictive analysis, and machine learning (ML) use cases.

Hadoop History

Hadoop’s ability to scale horizontally and run data processing applications directly within the Hadoop framework made it a cost-effective solution for organizations that had large computational needs but limited budgets. Here’s a brief overview of how Hadoop came to be:

Early 2000s20042005200620082012

Hadoop was inspired by Google’s publications on its MapReduce programming model and the Google File System (GFS). These papers described Google’s approach to processing and storing large amounts of data across distributed computing environments.

Doug Cutting and Mike Cafarella started the Nutch project, an open-source web search engine. The need to scale Nutch’s data processing capabilities was a primary motivator in the development of Hadoop.

The first implementation of what would become Hadoop was developed within the Nutch project. This included a distributed file system and the MapReduce programming model directly inspired by the Google papers.

Hadoop became a separate project under the Apache Software Foundation.

Hadoop 1.0 was released as a platform for distributed data processing, and the open source ecosystem around Hadoop began to expand rapidly as data scientists and data engineers began to use the software framework.

The Hadoop 2.0 release introduced significant improvements, including YARN (Yet Another Resource Negotiator). YARN extended Hadoop’s capabilities beyond just batch processing.

How Hadoop Works

Hadoop splits files into storage blocks and distributes them across commodity nodes in a computer cluster. Each block is replicated across multiple nodes for fault tolerance. Processing tasks are divided into small units of work (maps and reduces), which are then executed in parallel across the cluster. Parallel processing allows Hadoop to process large volumes of data efficiently and cost-effectively. 

Here are some of the key features of Hadoop that differentiate it from traditional distributed file systems:

  • Hadoop can scale out from a single server to thousands of machines with minimal human intervention.
  • Hadoop automatically replicates data blocks across multiple nodes. If one node fails, the system can access copies of those blocks from other nodes and automatically restart failed tasks.
  • Hadoop can be used with data lakes that store multiple types of data.

Hadoop Components

Here’s a breakdown of the three key Hadoop components, along with their roles in the big data ecosystem:

Hadoop Distributed File System (HDFS)

It is Hadoop’s storage backbone. HDFS is designed to store large files across multiple machines in a large cluster. It breaks down files into blocks, distributes them across the cluster, and provides high throughput and fault tolerance by replicating each block across several nodes.

Yet Another Resource Negotiator (YARN)

Can be thought of as Hadoop’s operating system (OS). YARN is responsible for managing and allocating resources across the cluster. It also handles job scheduling and enables multiple data processing engines to handle data stored in HDFS efficiently. Essentially, YARN separates the resource management and job scheduling/monitoring functions into separate daemons to provide more flexible and scalable cluster management.


MapReduce, which was the original programming model for Hadoop, supports massively parallel processing (MPP) for large sets of data.

The process involves two steps: the Map step, which processes and transforms the input data into a set of intermediate key-value pairs, and the Reduce step, which merges the output from the Map step to produce the final result. MapReduce is known for its simplicity and scalability, although newer processing models (like those provided by Apache Spark) offer better speed and more flexibility for many use cases.

The following tools work alongside Hadoop’s core components to create an ecosystem that expands the framework’s capabilities:


Providesstructured query language -like interface for querying and managing data stored in HDFS.


Providesscripting language for data analysis that can be used with extract, transform, load (ETL) pipelines.


Is used to transfer data between Hadoop and traditional relational databases.


Is a fast, in-memory processing engine that can be used on top of Hadoop with YARN. Today, Spark is often used instead of MapReduce to improve throughput and support a wider range of workloads.


Is a NoSQL columnar database that can provide Google Bigtable-like capabilities on top of Hadoop and HDFS.


Is a workflow and coordination system for managing Hadoop jobs.


Processes data in real time. Storm and Hadoop can be used together in a big data architecture. While Storm handles real-time data processing, Hadoop can be used for batch processing, storage, and analyzing large datasets that don’t require immediate processing.

Hadoop Challenges

While expanding the Hadoop ecosystem to support diverse workloads beyond batch processing greatly increased the framework’s capabilities, it also made deployments and ongoing management more complex.

Today, cloud-based services like Amazon EMR, Google Cloud Dataproc, and Microsoft Azure HDInsight provide managed service environments that abstract away some of Hadoop’s underlying complexity.

This evolution reflects broader trends in technology, where cloud-based solutions are increasingly providing more managed and integrated services to simplify complex infrastructure and software management tasks in hybrid cloud or multi-cloud environments where data needs to be moved between different storage systems and platforms.

Pros and Cons of Hadoop

Organizations that use Hadoop can achieve significant insights and value from files stored in data lakes as long as the organization’s information technology (IT) team has the skills required to navigate the framework’s intricacies – and the time to optimize and maintain the Hadoop ecosystem effectively.

Here’s a comparison table that outlines some of the pros and cons of using Hadoop for big data processing:


  • Can easily scale up by adding more nodes to the system
  • Uses commodity hardware, which reduces both storage and processing costs
  • Can handle various types of structured, unstructured and semi-structured data
  • Automatically replicates data blocks to ensure data is not lost if a node fails
  • Ensures services are available even in the event of hardware failure
  • Includes mechanisms for data protection
  • Distributes the workload across multiple nodes to improve processing throughput


  • Complex to set up, manage, and maintain
  • Can introduce overhead for simple tasks
  • Requires additional security measures for sensitive data
  • Can be challenging to manage cluster resources efficiently
  • The learning curve for understanding and effectively using the ecosystem is steep
  • Moving large volumes of data in and out can be complex and time-consuming
  • Latency may not be suitable for real-time processing

The Bottom Line

Apache Hadoop is a legacy open source framework for processing and storing large volumes of data across distributed computing environments. Hadoop is still useful for batch processing big data, but the framework’s complexity, steep learning curve, and performance limitations for real-time processing can be challenging.


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Margaret Rouse
Senior Editor
Margaret Rouse
Senior Editor

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.