Hadoop vs. MongoDB: Choosing the Right Database for Big Data

Read this guide on Hadoop vs. MongoDB to understand the comparison between Hadoop and MongoDB for big data management | ProjectPro

Hadoop vs. MongoDB: Choosing the Right Database for Big Data
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Hadoop and MongoDB are two of the most popular tools used for handling data in big data projects. While Hadoop is widely known for its ability to process massive amounts of data, MongoDB is a flexible NoSQL database that excels at storing and handling unstructured data. As the saying goes, "data is the new oil," and choosing the right tool to manage and analyze it can be the difference between success and failure in today's data-driven world. This blog from ProjectPro will explore the Hadoop vs MongoDB debate, and find out which one is the best fit for your big data project.


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Apache Hadoop vs. MongoDB: Difference Between Hadoop and MongoDB

MongoDB vs. Hadoop: Comparison between them

Before diving straight into their differences, let us first understand each of these programming frameworks: 

What is Hadoop? 

Hadoop is an open-source platform for storing and processing large volumes of data. Created by Doug Cutting, Hadoop is a Java-based application with various components for interfacing data, including resource management, distributed file systems, and data processing.

Hadoop's design and functionality make it a powerful tool for managing and analyzing big data. Its distributed architecture allows for processing large data sets across a network of computers, making it possible to process data that would be too large for a single machine. Additionally, Hadoop's open-source nature allows a large community of developers to contribute to its ongoing development and improvement.

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What is MongoDB?

MongoDB is one of the most popular NoSQL databases management framework first released in 2007. It is designed to be highly scalable and flexible and is built primarily for storing big data and data retrieval. Unlike a traditional relational database, MongoDB does not rely on creating tables with strict relationships between them. Instead, it stores data in collections of documents that can be easily retrieved, manipulated, and analyzed. 

MongoDB is based on C++ and belongs to the NoSQL family of databases. This means it is optimized for handling large volumes of unstructured or semi-structured data, which is typically difficult to manage using a traditional relational database. MongoDB is especially well-suited for use cases where data must be stored and retrieved quickly and efficiently, such as in web applications or analytics platforms.

Comparison of Hadoop vs. MongoDB 

Let us now understand the difference between MongoDB and Hadoop based on several parameters below: 

Hadoop vs. MongoDB Performance

MongoDB vs. Hadoop Performance

Hadoop is optimized for batch processing large data sets, making it ideal for tasks such as log processing, data warehousing, and data mining. Hadoop uses a distributed file system (HDFS) to store data across multiple nodes in a cluster, enabling parallel processing of large datasets. The MapReduce programming model used by Hadoop allows developers to write code that can be distributed across the cluster, enabling efficient processing of large datasets.

On the other hand, MongoDB is optimized for real-time data processing and querying, making it ideal for applications that require fast response times, such as e-commerce websites and mobile apps. MongoDB uses a document-based data model for flexible schema design and easy data scaling. MongoDB supports horizontal scaling, which means that data can be distributed across multiple nodes in a cluster, allowing for high availability and fault tolerance.

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Hadoop vs. MongoDB Scalability 

Hadoop vs. MongoDB Scalability

When it comes to scalability, Hadoop is designed for horizontal scalability, making it ideal for large-scale data processing across multiple machines. It can scale out to add more machines to handle the processing of large amounts of data. 

MongoDB, on the other hand, is a NoSQL database designed to scale vertically, increasing its processing power by adding more resources to a single machine. MongoDB achieves this by allowing users to add CPU, memory, and storage to a single machine to handle more data.

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MongoDB vs. Hadoop Use Cases 

Hadoop vs. MongoDB Use Cases

Hadoop is ideal for processing large volumes of structured and unstructured data. It is commonly used for batch processing, data analysis, and machine learning tasks. Hadoop is known for its ability to handle large-scale data sets and complex data processing tasks. While, MongoDB is ideal to store unstructured data, such as social media content, sensor data, and log files, text files, binary data, and more. 

Hadoop Big Data Example 

Netflix uses Hadoop to process large amounts of customer data to make personalized recommendations. Thus, by analyzing viewing history, ratings, and other data points, Netflix's recommendation engine can suggest new shows and movies to individual users.

MongoDB Big Data Example 

The weather forecasting company AccuWeather uses the MongoDB database to store and process data from thousands of weather sensors worldwide. This allows AccuWeather to provide millions of users with up-to-date and accurate weather forecasts. MongoDB's flexibility and scalability suit this real-time, big-data application well.

Which is Better for your Big Data Project - Hadoop or MongoDB? 

Which is better? - Hadoop or MongoDB

Hadoop and MongoDB are both popular choices for big data analytics. Companies such as eBay, Adobe, LinkedIn, SAP, McAfee, and Foursquare use MongoDB, while  Amazon, Microsoft, IBM, Cloudera, Intel, Teradata, and Map R Technologies are notable Hadoop users.

While both Hadoop and MongoDB offer benefits over traditional databases, MongoDB is the better choice for those looking to replace a traditional database. Its flexible schema allows for easy storage of information without the need for many transformations beforehand, and its query language allows for efficient access and processing of data on the fly.

However, Hadoop can be useful when dealing with massive objects due to its distributed file system. In these cases, Hadoop can be used with MongoDB to create a single cohesive architecture that leverages the strengths of both platforms. 

Hadoop vs. MongoDB vs. Cassandra

Check out the below table to better understand the comparison of Cassandra vs. Hadoop vs. MongoDB based on several features: 

Features 

Hadoop 

MongoDB 

Cassandra 

Built 

Hadoop is written in Java. 

MongoDB is written in C++ 

Cassandra is written in Java. 

Performance 

Hadoop has high latency due to disk access

MongoDB has low latency due to in-memory data access

Cassandra has low latency due to in-memory data access

Database type 

Distributed File System 

Document-oriented 

Column-oriented 

Data Management 

Batch Processing and Real-time

Real-time data analysis 

Real-time 

Data Format 

Structured and unstructured data 

BSON and JSON data 

Structured data 

Use Cases 

ideal for batch processing and is commonly used for big data analytics, ETL (Extract, Transform, Load), and data warehousing.

commonly used for content management, IoT applications, and mobile apps.

Used for time series data, data analytics, and fraud detection.

 

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Hadoop and MongoDB - A Perfect Match for Data Processing

Hadoop and MongoDB: A perfect match for Data Processing

Traditional relational databases ruled the roost until datasets were reckoned in megabytes and gigabytes. However, as organizations worldwide grew, a tsunami called "Big Data" rendered the old technologies unfeasible.

When it came to data storage and retrieval, these technologies crumbled under the burden of such colossal amounts of data. Thanks to Hadoop, Hive, and HBase, these popular technologies can now handle large sets of raw unstructured data efficiently and economically.

Another aftermath of the above problems was the parallel advent of "Not Only SQL" or NoSQL databases. The primary advantage of the NoSQL databases is their mechanism that facilitates the storage and retrieval of data in the loser consistency model, along with added benefits like horizontal scaling, better availability, and quicker access.

With its implementation in over five hundred top-notch organizations across the globe, MongoDB certainly has emerged as the most popular NoSQL database among all. Without a concrete survey, it might be challenging to assess the percentage of adoption and penetration of MongoDB. However, various metrics like Google searches and the number of employment opportunities for Hadoop and MongoDB professionals give a good idea of the popularity of these technologies.

Based on its Google search volume, it was found that MongoDB ranked first and was three times more popular than the next prevailing technology. When compared with the least prevailing database, MongoDB fared ten times better.

A survey of profiles of IT professionals on LinkedIn revealed that the percentage of professionals skilled in MongoDB was almost 50% compared to other NoSQL-skilled professionals. Regarding acceptance levels, MongoDB equals the sum of the next 3 NoSQL databases put together. Rackspace, one of the pioneers to adopt MongoDB for their cloud solutions, affirms, "MongoDB is the de facto choice for NoSQL applications."

Google Trends graph showing the popularity of MongoDB over other NoSql Technologies

Google Trends graph showing the popularity of MongoDB over other NoSql Technologies.

The reasons that MongoDB is being widely adopted by developers follow:

  • MongoDB enhances productivity and is easy to get started and use.

  • Owing to removing schema barriers, developers can now concentrate on developing applications rather than databases.

  • MongoDB offers extensive support for languages like C#, C, C++, Node.js, Scala, Javascript, and Objective-C. These languages are pertinent to the future of the web.

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Understanding How MongoDB Teams Up with Hadoop and Big Data Technologies

Of late, Technologists at MongoDB have successfully developed a MongoDB connector for Hadoop that facilitates enhanced integration combined with ease in the execution of various tasks as below:

Integration of real-time data created in MongoDB with Hadoop for in-depth, offline analytics

  • The MongoDB-Hadoop connector uses the authority of Hadoop's MapReduce to live application data in MongoDB by extracting values from Big Data – speedily and efficiently.

  • The MongoDB-Hadoop connector projects it as a 'Hadoop compatible file system,' MapReduce jobs can now be read directly from MongoDB without being copied to the HDFS. Thus, doing away with the necessity of transferring terabytes of data across the network.

  • The "necessity" of scanning entire collections has been eliminated. MapReduce jobs can pass queries using filters and harness MongoDB's indexing abilities like text search, compound, array, Geo-spatial, and sparse indexes.

  • Reading and writing back results from Hadoop jobs back to MongoDB to support queries and real-time operational processes.

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Scope of Application - Hadoop and MongoDB

In context to Big Data stacks, MongoDB and Hadoop have the following scopes of application:

  • MongoDB is used for the operational part – as a real-time data store.

  • Hadoop is mainly used for offline analysis and processing of batch data. 

Scope of Usage in Batch Aggregation

MongoDB and Hadoop Batch Integration

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When it comes to analyzing relational data, the inbuilt aggregation features incorporated in  MongoDB hold good in the majority of situations. However, some cases require a higher degree of data aggregation. Under such circumstances, Hadoop provides powerful support for complex analytics.

  • Using single or multiple MapReduce jobs, Hadoop processes all the data extracted from MongoDB. Pulling data from other locations in these MapReduce jobs can also formulate a multi-data solution.

  • The results received from MapReduce jobs can be written back to MongoDB and used for analysis and queries as and when required.

  • MongoDB applications can thus use the data from batch analytics to hand over to the end-user or to facilitate other features down the line.

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Scope of Usage in Data Warehousing 

In a usual production environment, application data with specific functionality and language may exist in multiple data stores. Under such complex situations, Hadoop is used as an integrated source of data and a data warehouse.

  • MapReduce jobs transfer MongoDB data to Hadoop.

  • As soon as the data from MongoDB and other sources are available in Hadoop, the datasets can be queried. 

  • At this stage, data analysts can use Pig or MapReduce for querying large datasets that include data from MongoDB.

Owing to the above, MongoDB has emerged as the most preferred choice of developers. From the perspective of NoSQL databases, data engineers at MongoDB have successfully integrated it with Hadoop. The MongoDB Hadoop permutation effectively solves several architectural problems in data warehousing, processing, retrieval, and aggregating. 

Master Big Data Management with Hands-on Hadoop and MongoDB Projects on ProjectPro

Master Big Data Management with Hands-on Hadoop and MongoDB Projects on ProjectPro

Hadoop and MongoDB are powerful big data technologies with their strengths and weaknesses. Hadoop is great for processing large amounts of structured and unstructured data, while MongoDB is a NoSQL database that provides flexibility and scalability for handling unstructured data. Choosing between the two technologies largely depends on your project's specific needs and requirements.

However, it is essential to note that proficiency in these technologies is highly sought after in the current job market. Thus, to enhance your big data knowledge and gain hands-on experience, ProjectPro offers over 270+ solved end-to-end projects on big data and data science. These projects come with source codes, guided video explanations, and one-to-one mentor support sessions, making them an excellent resource for beginners and advanced learners to expand their skill sets and advance their careers. So, why wait? Subscribe to ProjectPro Repository today to get started and take your big data career to the next level. 

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FAQs on Hadoop vs. MongoDB 

Yes, MongoDB is ideal for handling big data, especially when dealing with semi-structured and unstructured data. It is a scalable NoSQL database that can handle large volumes of data and dynamic querying.

Hadoop is not a NoSQL database. It is a distributed data processing open-source framework that can handle both structured and unstructured data, but it is not a database management system. However, Hadoop can be used with NoSQL databases like MongoDB for querying and storing data.

Yes, MongoDB is designed to handle large amounts of data and can scale horizontally to support sharding, which allows for distributing data across multiple servers.

 

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