Hadoop vs MongoDB: Which Tool Is Better For Harnessing Big Data
According to a research report, the Hadoop big data analytics market is forecasted to grow at a CAGR of 40% over the next four years.  Given the current state where enterprises are dealing with a vast amount of structured and unstructured data, cost-effective Hadoop big data solutions are widely deployed to analyse data better. Relational databases cannot manage unstructured data. That’s where Hadoop and MongoDB big data solutions come into the picture, to deal with large and unstructured data. Although both the platforms have some similarities, for example, they are compatible with Spark and both perform parallel processing, there are also certain differences. Apache Hadoop is a framework which is used for distributed processing in a large amount of data while MongoDB is a NoSQL database. While Hadoop is used to process data for analytical purposes where larger volumes of data is involved, MongoDB is basically used for real-time processing for usually a smaller subset of data. In this article, we list down the differences between the two popular Big Data tools. Understanding The Basics Apache Hadoop is a framework where large datasets can be stored in a distributed environment and can be parallely processed using simple programming models. The main components of Hadoop include as mentioned below: Hadoop Common: The common utilities that support the other Hadoop modules. Hadoop Distributed File System: A distributed file system that provides high-throughput access to application data. Hadoop YARN: A framework for job scheduling and cluster resource management. Hadoop MapReduce: A YARN-based system for parallel processing of large data sets. MongoDB is a general-purpose, document-based, distributed database built for modern application developers and for the cloud era. It is a scalable NoSQL database management platform which was developed to work with huge volumes of the distributed dataset which can be evaluated in a relational database. The main components of MongoDB include as mentioned below: mongod: The core database process mongos: The controller and query router for sharded clusters mongo: The interactive MongoDB Shell Features The features of Hadoop are described below: Distributed File System: As the data is  stored in a distributed manner, this allows the data to be stored, accessed and shared parallely across a cluster of nodes. Open Source: Apache Hadoop is an open-source project and its code can be modified according to the user’s requirements. Fault Tolerance: In this framework, failures of nodes or tasks can be recovered automatically. Highly Available Data: In Apache Hadoop, data is highly available due to the replicas of data of each block. The features of MongoDB are mentioned below: Sharing Data Is Flexible: MongoDB stores data in flexible, JSON-like documents which means that the fields can vary from document to document and data structure can be changed over time. Maps To The Objects: The document model maps to the objects in the application code, making data easy to work with. Distributed Database: MongoDB is a distributed database at its core, so high availability, horizontal scaling, and geographic distribution are built-in and easy to use. Open-sourced: MongoDB is free to use. Real-Time Processing In Hadoop, the processing time is measured in minutes and hours. This open-source implementation of MapReduce technology is not meant to be used for real-time processing. On the other hand, MongoDB is a document-oriented database and is designed for real-time processing. The processing time in MongoDB is measured in milliseconds. Limitations Some of the limitations of Hadoop are mentioned below: Apache Hadoop lacks in providing a complete set of tools which is required for handling metadata, ensuring data quality, etc. The architecture of Hadoop is designed in a complex manner which makes it harder for handling smaller amounts of data. Some of the limitations of MongoDB are mentioned below: Sometimes the executions in this framework are slower due to the use of joins. In this framework, the maximum document size is 16 megabytes. Operations In Organisations Organisations are using Hadoop in order to generate complex analytics models or high volume data storage applications such as machine learning and pattern matching, customer segmentation and churn analysis, risk modeling, retrospective, and predictive analytics, etc. On the other hand, organisations are using MongoDB with Hadoop in order to make analytic outputs from Hadoop available to their online, operational applications which include random access to indexed subsets of data, updating fast-changing data in real-time as users interact with online applications, millisecond latency query responsiveness, etc. Performance Of Network Hadoop as an online analytical processing system and MongoDB as an online transaction processing system. Hadoop is designed for high-latency and high-throughput as data can be managed and processed in a distributed and parallel way across several servers, while MongoDB is designed for low-latency and low-throughput as it has the ability to deal with the need to execute immediate real-time outcomes in the quickest way possible.
According to a research report, the Hadoop big data analytics market is forecasted to grow at a CAGR of 40% over the next four years.  Given the current state where enterprises are dealing with a vast amount of structured and unstructured data, cost-effective Hadoop big data solutions are widely deployed to analyse data better.  Relational […]
According to a research report, the Hadoop big data analytics market is forecasted to grow at a CAGR of 40% over the next four years.  Given the current
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.