What are Hadoop Clusters? Important Features, Key Roles and Advantages

According to recent statistics, approximately 2.5 quintillion bytes of data is created each day. Effectively managing and processing this massive influx is crucial for businesses and organizations.

Understanding the architecture of a Hadoop cluster is essential for handling big data efficiently. This guide provides insights into Hadoop clusters, their scalability, benefits, and limitations, equipping you with the knowledge of big data management.

Understanding Hadoop Clusters in Big Data: Key Features and Overview

Before understanding a Hadoop cluster in big data, you first need to know what a cluster is. A cluster is a group of connected systems working together to perform specific tasks. In computing, clusters consist of multiple machines linked through a local network, allowing them to share workloads and function as a unified system. 

Each machine, known as a node, plays a role in processing, storage, or resource management, making distributed computing efficient.

A Hadoop cluster in big data is specifically designed for handling vast amounts of structured and unstructured data. Instead of relying on a single system, Hadoop breaks down large datasets into smaller parts, distributes them across multiple nodes, and processes them simultaneously. 

To fully understand the architecture of a Hadoop cluster, you should first get familiar with its core components. These elements play a crucial role in ensuring smooth and efficient operations.

• Master node: This node manages the entire cluster by handling metadata, task scheduling, and resource allocation. It includes the NameNode, responsible for tracking stored files, and the JobTracker, which assigns processing tasks to worker nodes.
• Worker nodes: These nodes store and process data. Each contains a DataNode, which manages storage, and a TaskTracker, which executes assigned computational jobs.
• Secondary NameNode: This node periodically saves metadata snapshots, helping in recovery and maintaining data integrity.

Now that you understand how a Hadoop cluster is structured, it's important to explore its scalability and how it can adapt to growing data demands.

Scalability of Hadoop Clusters

When working with big data, scalability is one of the most critical factors to consider. As your data increases, your system must be able to handle additional workloads without affecting performance. Hadoop clusters support horizontal scaling, allowing you to add or remove nodes based on data processing requirements.

To understand this better, consider the following example in retail industry: 

A large e-commerce company processes 5PB of customer purchase data monthly using 20 nodes. During a holiday sale, traffic surges, and transaction records grow to 8PB. The company quickly adds 10 more nodes to handle the increased load, ensuring real-time order processing and inventory tracking.

The following characteristics highlight how scalability benefits Hadoop clusters:

• Elastic expansion: You can increase or decrease the number of nodes as needed. For instance, if your video streaming platform experiences a surge in user-generated content, additional nodes can be deployed to process and store the data efficiently.
• Load distribution: Workloads are evenly spread across nodes to prevent overloading any single machine. If a particular node reaches its limit, tasks are automatically reassigned to available nodes.
• Cost-efficient scaling: Instead of investing in expensive hardware replacements, you can expand a Hadoop cluster using low-cost commodity machines.
• Optimized processing speed: The system processes data where it is stored, reducing network congestion and improving efficiency. This is especially useful for organizations handling real-time analytics, such as stock market data analysis.

Also Read: Understanding Hadoop Ecosystem: Architecture, Components & Tools

Now that you understand how scalability allows Hadoop clusters to grow with increasing data demands, it’s equally important to examine the properties that make them efficient. These properties define how Hadoop maintains performance, reliability, and fault tolerance in large-scale data processing.

The following properties explain why Hadoop clusters are widely used for big data applications.

Properties of Hadoop Clusters

The following properties explain why Hadoop clusters are widely used for big data applications.

• Fault tolerance: If a node fails, the system automatically redirects tasks to other functioning nodes. For example, in an online banking system, customer transaction data is replicated across multiple nodes, ensuring that no critical information is lost due to a hardware failure.
• Data locality: Instead of transferring large datasets over the network, Hadoop processes data where it is stored. A cloud-based video service analyzes usage trends without transferring large media files between servers.
• Parallel processing: Multiple nodes execute different tasks simultaneously, significantly improving efficiency. A social media platform analyzing millions of user interactions can process text, images, and video data in parallel to generate recommendations faster.
• High throughput: Hadoop can process massive datasets at high speeds due to its distributed architecture. A logistics company tracking shipments across different regions uses Hadoop to analyze traffic patterns and optimize delivery routes in real time.
• Cost efficiency: Hadoop clusters run on affordable, commodity hardware rather than expensive, high-end servers. Startups that handle large datasets, such as ride-hailing services, can use Hadoop to process location data without investing in costly infrastructure.

With these properties in mind, let's explore the different configurations of Hadoop clusters designed to suit varying data processing requirements. 

Exploring the Different Types of Hadoop Clusters

Hadoop clusters can be classified based on their setup and operational requirements. Each type is designed to handle data differently, impacting storage, processing, and system architecture.

Understanding the types of Hadoop clusters helps you choose the right environment for your big data processing needs. The following sections explain the major types of Hadoop clusters and how they function.

Single Node Hadoop Cluster

A single-node Hadoop cluster runs all essential Hadoop services on one machine. This setup is mainly used for testing, learning, and small-scale development. Since everything operates on a single system, there is no real data distribution, making it unsuitable for large-scale applications. 

However, it allows you to understand the architecture of a Hadoop cluster before working on complex systems.

The following characteristics define how a single-node Hadoop cluster operates:

• All components on one machine: The NameNode, DataNode, JobTracker, and TaskTracker function on a single system. For example, if you are a student, you can set up a single node cluster to practice running a test MapReduce job and understanding file storage mechanisms.
• Best for learning and testing: You can use this setup to test new configurations before deploying them to a multi-node environment. For instance, Apache Pig and Hive queries are frequently tested on a single node before deployment on larger clusters.
• No actual distributed processing: Since all tasks execute on one system, processing speed is limited. If you are working on a machine learning project that requires analyzing terabytes of data, a single-node cluster will not be efficient.

Also Read: Hadoop vs MongoDB: Which is More Secure for Big Data?

Single-node clusters are useful for understanding the core components of Hadoop. However, real-world applications require multiple machines working together for efficient big data processing.

Multiple Node Hadoop Cluster

A multiple-node Hadoop cluster consists of two or more interconnected machines. Unlike a single-node setup, it supports distributed computing, enabling faster and more efficient big data processing. Businesses, research institutions, and cloud-based platforms rely on multiple node clusters to process petabytes of data daily.

The following aspects define how a multiple-node Hadoop cluster functions:

• Separate master and worker nodes: The master node handles task scheduling, while worker nodes store and process data. For example, a bank’s fraud detection system assigns real-time transaction analysis tasks to multiple worker nodes, ensuring fast risk assessment.
• Distributed data storage: Data is split into smaller blocks and stored across multiple nodes. This ensures fault tolerance, as data replication prevents loss. If a node fails in an e-commerce platform, product inventory remains accessible from another node.
• Parallel processing capabilities: Tasks are divided across nodes and executed simultaneously. Search engines use this method to index billions of web pages quickly, improving search efficiency.
• Scalability for handling big data: You can add nodes to meet growing demands. A healthcare analytics company may start with 10 nodes and expand to 100 as patient data increases.
• Inter-node communication management: Unlike single-node clusters, multiple-node setups require a robust network to prevent bottlenecks. If communication between nodes is slow, task execution gets delayed, impacting performance.

Also Read: How to Become a Hadoop Administrator: Everything You Need to Know

Note: The Secondary NameNode is not a backup for the NameNode. Instead, it periodically merges file system metadata and edit logs to reduce the workload on the primary NameNode. If the NameNode fails, the Secondary NameNode cannot replace it but helps in faster recovery by maintaining recent metadata snapshots.

A multiple-node setup forms the backbone of Hadoop’s real-world applications. To fully utilize its capabilities, you need to understand the architecture of Hadoop cluster and how different components interact. Read the following section. 

upGrad’s Exclusive Software Development Webinar for you –

SAAS Business – What is So Different?

The Architecture of Hadoop Clusters: A Comprehensive Overview

The architecture of a Hadoop cluster is designed for large-scale data storage and processing. It follows a distributed model that ensures efficiency, fault tolerance, and high availability. Understanding its core components helps you manage data effectively.

To build a solid foundation in Hadoop cluster in big data, you need to explore its essential components. Below is a breakdown of the primary elements that define its structure and functionality.

NameNode

The NameNode is the master of the cluster. It manages metadata and ensures the smooth operation of the Hadoop Distributed File System (HDFS). Every file stored in the system is tracked and organized by the NameNode.

The following aspects highlight its role in the architecture of a Hadoop cluster:

• Manages metadata – Stores information about file locations, permissions, and directory structures. For instance, when a new file is added, the NameNode records its details and assigns storage locations.
• Controls data access – Regulates which users can read, write, or modify files. In banking applications, this prevents unauthorized access to sensitive financial records.
• Tracks DataNodes – Monitors the status of all DataNodes and redirects requests if a node fails. This ensures continuous data availability in large-scale business applications.

Since the NameNode is crucial to system performance, a backup mechanism is required to protect metadata. The Secondary NameNode serves this purpose, let’s look at how. 

Secondary NameNode

The Secondary NameNode does not replace the NameNode but supports it by maintaining metadata snapshots. This prevents data loss and helps in system recovery.

Below are its primary functions:

• Creates regular metadata checkpoints – Periodically saves file system metadata to reduce recovery time in case of failure. If the NameNode crashes, the latest snapshot helps restore file locations.
• Improves performance – Frees up resources by clearing the outdated metadata. This is useful in environments where large datasets require frequent updates.
• Prepares backup for recovery – Assists in restarting the system quickly after an unexpected shutdown. Businesses handling customer transactions rely on this feature to prevent service disruptions.

While the NameNode and Secondary NameNode manage metadata, actual data storage and retrieval are handled by the DataNodes. Let’s explore that in detail. 

DataNodes

DataNodes store file blocks and handle user requests for reading and writing data. They are responsible for maintaining multiple copies of data to prevent loss.

The following points explain the role of DataNodes in a Hadoop cluster in big data:

• Store and manage data blocks – Break large files into smaller blocks and distribute them across nodes. For example, an online retailer processing sales records stores transaction data across multiple DataNodes.
• Communicate with NameNode – Send regular status updates to ensure system health. If a node stops responding, the NameNode replicates its data elsewhere.
• Prevent data loss through replication – Maintain multiple copies of each block. If one node fails, another has the same data, ensuring reliability.

Data storage is a key function, but processing large datasets requires a system for task execution. JobTracker and TaskTrackers handle this efficiently, which you’ll learn about in the next section. 

JobTracker and TaskTrackers

JobTracker and TaskTrackers work together to process data across the cluster. The JobTracker assigns tasks, while TaskTrackers execute them on individual nodes.

The following points explain their importance in the architecture of a Hadoop cluster:

• JobTracker distributes processing tasks – Assigns jobs across multiple nodes. For example, when analyzing customer sentiment from reviews, the JobTracker divides the task among TaskTrackers.
• TaskTrackers execute assigned tasks – Perform computations and return results. In weather forecasting systems, TaskTrackers analyze climate patterns in real-time.
• Handles task failures – If a TaskTracker fails, the JobTracker assigns the task to another node. This ensures uninterrupted processing in industries requiring real-time insights, such as stock market analysis.

Also Read: Hadoop YARN Architecture: Comprehensive Guide to YARN Components and Functionality

With a clear understanding of the cluster’s components, the next step is to examine the key benefits and challenges of using Hadoop for data management.

Key Benefits and Limitations of Hadoop Clusters

Hadoop clusters offer efficient data processing, scalability, and fault tolerance, but they also come with certain challenges. Understanding these aspects helps you decide when and where to use them effectively.

The following table highlights the key benefits and limitations of using a Hadoop cluster in big data. Each factor plays a crucial role in determining the system’s performance and suitability.

Also Read: Top 10 Hadoop Tools to Make Your Big Data Journey Easy

While Hadoop offers scalability, efficiency, and reliability, it also demands a solid understanding of its architecture. If you want to master the architecture of a Hadoop cluster and its real-world applications, structured learning can help you build expertise. The next section will show you how. 

How upGrad Can Help You Gain Expertise in Hadoop Clusters for Big Data?

If you want to build a career in data science, big data, or cloud computing, you need a platform that provides structured learning. upGrad is a leading online learning platform with over 10 million learners and 200+ courses. It offers industry-aligned programs that help you gain practical skills, real-world experience, and career support to excel in competitive job markets.

The following courses help you develop expertise in data-driven technologies:

• Master’s Degree in Artificial Intelligence and Data Science
• Post Graduate Diploma in Data Science & AI
• Post Graduate Certificate in Data Science and AI
• Free Data Science Course From upGrad

If you need guidance on which program suits your career goals, you can take advantage of upGrad’s free one-on-one career counseling session to get expert advice and make informed decisions!

Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!

Explore our Popular Data Science Courses

Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!

Top Data Science Skills to Learn

Stay informed and inspired  with our popular Data Science articles, offering expert insights, trends, and practical tips for aspiring data professionals!

Read our popular Data Science Articles

Reference Link:

https://www.demandsage.com/big-data-statistics/