What is Hadoop and How Does It Work?

Summary: Hadoop is an open-source framework for managing big data, offering distributed storage and parallel processing. Its main components include HDFS, MapReduce, and YARN. Hadoop’s advantages, such as scalability, fault tolerance, and cost-effectiveness, make it a valuable tool for big data analytics.

Introduction

Hadoop has become a highly familiar term because of the advent of Big Data in the digital world and its successful establishment. Technological development through Big Data has been able to change the approach of data analysis vehemently. However, understanding Hadoop can be critical, and if you’re new to the field, you should opt for a Hadoop Tutorial for Beginners.

But what is Hadoop, and what is its importance in big data? Let’s find out from the blog!

Also Check Out: Unfolding the Details of Hive in Hadoop.

What is Hadoop?

Hadoop is a framework that uses distributed storage and parallel processing to store and manage big data. Data Analysts are the professionals who use the software to handle big data. 

There are three main features of Hadoop which includes:

• Hadoop HDFS – Hadoop Distributed File System is the unit of storage
• Hadoop MapReduce – Hadoop MapReduce is the processing unit
• Hadoop YARN – Yet Another Resource Navigator (YARN) is the resource of the management unit.

Advantages of Hadoop in Big Data

Hadoop is a widely used open-source framework for processing and storing large volumes of data in a distributed computing environment. Its advantages will help you expand your interest in the Big Data Hadoop Tutorial for beginners. 

It offers several benefits for handling big data effectively. Here are some of the key benefits of Hadoop in the context of big data:

Scalability

Hadoop provides a scalable solution for big data processing. It allows organisations to store and process massive amounts of data across a cluster of commodity hardware. 

Hadoop’s distributed file system (HDFS) breaks down data into smaller blocks and distributes them across multiple nodes. As the data volume grows, HDFS enables parallel processing and efficient resource utilisation.

Distributed Computing

Hadoop follows a distributed computing model, which means it can distribute the workload across multiple nodes in a cluster. This parallel processing capability enables faster data processing and analysis, as the tasks can be executed concurrently. 

Hadoop’s MapReduce framework efficiently manages the distribution of data and computation across the cluster, making it suitable for processing large datasets.

Explore: Edge Computing vs. Cloud Computing: Pros, Cons, and Future Trends.

Fault Tolerance

Hadoop is designed to be fault-tolerant. It can handle hardware failures gracefully without losing data or disrupting ongoing processes. When a node fails, Hadoop automatically redistributes the data and tasks to other healthy nodes in the cluster. 

This fault tolerance feature ensures high data availability and reliability, which is crucial when dealing with large-scale data processing.

Cost-Effectiveness

Hadoop is based on commodity hardware and is less expensive than specialised hardware or high-end servers. It leverages the power of distributed computing using cost-effective hardware components, making it an affordable option for organisations dealing with big data.

Additionally, Hadoop’s ability to scale horizontally by adding more nodes to the cluster allows organisations to expand their data processing capabilities without significant upfront investments.

Flexibility

Hadoop provides flexibility in terms of data types and sources it can handle. It can process structured, semi-structured, and unstructured data, allowing organisations to use diverse data formats. 

Hadoop’s schema-on-read approach enables users to store raw data without predefined schemas and structure it during analysis. This flexibility is beneficial when the data constantly evolves or when dealing with complex, heterogeneous data sources.

Further Read: 

Tableau Data Types: Definition, Usage, and Examples.

Data Types in NumPy: The Building Blocks of Powerful Arrays.

Data Processing Ecosystem

Hadoop has a rich ecosystem of tools and frameworks that complement its core functionalities. For example, Apache Hive provides an SQL-like interface to query and analyse data stored in Hadoop, while Apache Spark offers fast in-memory data processing and machine learning capabilities. 

These additional tools enhance Hadoop’s capabilities and provide a comprehensive platform for big data processing, analytics, and machine learning.

How does Hadoop work, and how is it used?

Hadoop runs on commodity servers and can scale up to support thousands of hardware nodes. The file system is designed to provide rapid data access across the nodes in a cluster along with fault-tolerant capabilities because applications can continue to run in case any individual nodes fail. 

These features helped Hadoop become a foundational platform for Data Management for using Big Data Analytics after it emerged in the mid-2000s.

Here is a brief about how Hadoop works:

HDFS

Hadoop maintains data in a distributed manner using the Hadoop Distributed File System (HDFS). The file is broken into smaller sections, which are typically 128MB or 256MB in size. These blocks are subsequently dispersed among the Hadoop cluster’s nodes. For fault tolerance, each record is replicated across multiple nodes, often with a replication factor of threefold.

Data Processing using MapReduce

Hadoop utilises the programming model MapReduce to process data. MapReduce separates processing into two phases: map and Reduce.

• Map: The input data is separated into sections and handled in parallel across the cluster nodes throughout the Map phase. Each node performs a map operation on its own data chunk, transforming it into pairs of keys and values.
• Shuffle and Sort: After the map phase, the intermediary key-value pairs generated by each node are organised and grouped across the cluster of nodes by key. This is known as shuffle and sort.
• Reduce: The sorted preliminary key-value pairs will be processed in the Reduce step to obtain the final result. Any node reduces a portion of the key-value pairs, combining or summarising the data as appropriate. The reduction phase output usually gets saved to a file in HDFS.

Job Submission and Cluster Management

To take advantage of Hadoop, you generally use the Hadoop API to generate code in Java, Python, or other compatible languages. The code has been compiled and saved as a JAR file. 

You next publish your Hadoop task to the Hadoop cluster, which involves the JAR file and input/output directories. The Hadoop cluster management spreads jobs among accessible nodes, organises execution, and manages error tolerance.

Challenges of Hadoop and How We Solve Them

Although Hadoop is one of the excellent technologies that has made big data environments feasible, it has limitations that have complicated its use. Following are some of the challenges that you might face as a user with Hadoop:

Performance Issue

Hadoop’s dependability on disc storage for data processing might result in lower performance than systems that use memory-intensive processing, such as Spark. MapReduce, Hadoop’s common processing engine, frequently requires lengthy disc reads and writes, which can add delay and lower overall processing speed. 

On the other hand, Spark’s ability to capitalise on stored in-memory data processing gives considerable speed enhancements for many industrial applications. Spark decreases disc I/O operations by retaining data in memories, resulting in shorter processing times.

High prices

Because Hadoop’s structure tightly combines storage and computing resources, it can increase prices. When processed or data storage needs increase, organisations frequently need to add more cluster nodes, which implies scaling both processing and storage at the same time. 

This strategy may result in excessive resource allocation, as expanding nodes to meet rising storage needs additionally results in excess computing capacity. On the other hand, separating computing and storage allows organisations to grow every resource individually, minimising costs based on particular needs.

Excess Capacity

One result of combining computing and storage resources in a cluster of Hadoop servers is the possibility of excess capacity. When extra nodes are installed primarily to boost the amount of storage, their processing resources remain unused. This mismatch between storage and computing power might result in additional expenditures and maintenance.

Management Difficulties

Organisations frequently confront difficulties while establishing and managing big Hadoop clusters. The complexity grows when other big data technologies are integrated into the Hadoop ecosystem. Aside from cluster management, responsibilities like data integration and data quality control can be challenging for organisations that use Hadoop systems.

On-site orientation

Hadoop was designed initially for on-premises installations. While all of its elements can now be stored in the cloud’s big data platforms, Hadoop remains largely an on-site solution.

Frequently Asked Questions

What is Hadoop and its main components?

Hadoop is an open-source framework that uses distributed storage and parallel processing for big data management. Its main components include Hadoop HDFS (storage), Hadoop MapReduce (processing), and Hadoop YARN (resource management).

How does Hadoop ensure fault tolerance?

Hadoop ensures fault tolerance by replicating data across multiple nodes. If a node fails, the system automatically redistributes data and tasks to other healthy nodes, ensuring continuous operation and data availability.

What are the advantages of using Hadoop for big data?

Hadoop offers scalability, distributed computing, fault tolerance, cost-effectiveness, and flexibility. It can handle diverse data types, allowing organisations to store and process large datasets efficiently across clusters of commodity hardware.

Conclusion

The above blog gives you a clear idea about the Hadoop tutorial for beginners and its uses. It certainly ignites your interest in Hadoop Learning for Beginners. Accordingly, you can expand your knowledge of Hadoop and learn about its application in real-world cases. 

You will find numerous online courses offering Big Data Hadoop training in India. While opting for Data Science courses, you will also find short-term courses offering Online Hadoop Certification Training.