Working with large amounts of data requires developers to be aware of the various big-data handling tools. Edureify, the best AI Learning App has provided information on various data-handling tools like Matplotlib, NumPy, MongoDB, MySQL, and more.

In this article, Edureify presents information on Hadoop, another data handling open-source platform. Read on to know more about Hadoop and take Edureify’s best online coding Bootcamp to learn the skills.

What is Hadoop?

Apache Hadoop stands for High Availability Distributed Object Oriented Platform. It is a project developer platform. Hadoop is an open-source framework that can store and process large datasets that range from gigabytes to petabytes of data.

Hadoop is a Java-based software platform that distributes big data and object-oriented tasks parallelly for their higher availability. The platform breaks down the big tasks into smaller workloads for efficient working.

Hadoop Modules

There are four main modules of Hadoop. They are-

  • Hadoop Distributed File Systems (HDFS)- This module functions on standard or low-end hardware that works as a distributed file system. HDFS module provides better data along with support for native data and high fault tolerance.
  • Yet Another Resource Negotiator (YARN)- Helps manage and monitor resource usage and cluster nodes. It helps schedule jobs and tasks.
  • MapReduce- It enables parallel data computation. It takes data inputs and converts them into datasets for computation in key-value pairs.
  • Hadoop Common- It provides the common Java libraries for use across all modules.

Hadoop Language

Hadoop is built on Java. Hadoop is a very flexible tool that supports codes written in C, Python, and C++.

Uses of Hadoop

The following are the uses of Hadoop in various sectors-

  • Retail- Big organizations need to work with large amounts of customer data. It becomes difficult to connect between large amounts of seemingly unrelated data. The Hadoop-powered Cloudera Enterprise deployed by British retailer M&S generated impressive results. The implementation of the cloud-based platform helped the organization predict the analytics and gain more advantages than their competitors.
  • Finance- Hadoop is very suitable for the Finance sector. It works efficiently in handling the algorithms of risk management. Banks have begun using the Hadoop framework for managing risk, financial security, and ensuring the security of the customer portfolios.
  • Healthcare- The Healthcare department has to handle a lot of data daily. The Hadoop framework allows doctors, carers, and nurses to have access to information whenever they need it. The framework helps the officials by offering them insights into the data and gives the analysis of the next feasible steps to take.
  • Security and Law Enforcement- Hadoop offers high security that can improve the effectiveness of local and national security. The Hadoop framework can help connect isolated data and events. Using the Hadoop framework can enhance the process of streamlining these data connections. This help reduce the time required for such works and increases the efficiency of detecting crimes in areas. The National Security Agency (NSA) had said that their use of the open-source Hadoop framework has helped them reduce the cost of work and also enabled better security, detection of terrorism, cybercrime, and more.

Impacts of Hadoop

Hadoop is one of the most crucial developments in the big data space. Hadoop is also considered to be the foundation of the modern cloud data lake. With the use of Hadoop, companies have been able to analyze and query big datasets with its off-the-shelf hardware, open-source, and inexpensive software.

Difference between Hadoop and Spark

Compared to Hadoop, Spark is a newer project developed in 2012. Spark is an Apache project that processes data in a parallel cluster and works in memory. The following are the major differences between Spark and Hadoop-

S.No. Spark Hadoop
1. Has more computation types and extends the MapReduce model that works as a lightning-fast cluster computing technology. It is an open-source framework that functions with the MapReduce algorithm.
2. Helps reduce the number of read/write attempts to disk. It stores the intermediate data in-memory enabling faster processing speed. The MapReduce model reads from the disk which lengthens the process.
3. Handles real-time data efficiency. Handles batch processing.
4. Process data interactively for its low latency computing. Does not have an interactive mode and has a high latency computing framework.
5. Can process real-time data. Helps process data in batches.
6. Requires a lot of RAM for running in-memory which increases cluster and cost. Is cheaper in terms of cost.
7. GraphX, a graph computation library is used. Uses the PageRank algorithm.


Here was the tutorial on Hadoop.

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Some FAQs on Hadoop-

1. What does Hadoop stand for?

Hadoop stands for High Availability Distributed Object Oriented Platform.

2. What is Hadoop?

Hadoop is a project developer platform. Hadoop is an open-source framework that can store and process large datasets that range from gigabytes to petabytes of data.

3. Mention the names of the four modules of Hadoop.

The four modules of Hadoop are-

  • Hadoop Distributed File Systems (HDFS)
  • Yet Another Resource Negotiator (YARN)
  • MapReduce
  • Hadoop Common

4. What languages are supported by Hadoop?

Hadoop is based on Java but also supports codes written in C, Python, and C++

5. From where can I learn more about Hadoop?

Take the best web development coding Bootcamp with Edureify and learn more about Hadoop and other data-handling tools.

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