Hadoop vs MongoDB

Differences Between Hadoop and MongoDB 

Hadoop is an open-source platform, which is used to store and process the huge volume of data. It is a Java-based application, which contains a distributed file system, resource management, data processing and other components for an interface.

MongoDB is mainly built for data storage and retrieval. It can also perform data processing and scalability. It is based on C++ and belongs to the NoSQL family. It does not rely on creating relational tables instead; it stores its records as documents.

Many companies use Hadoop and MongoDB platform to create their own Big Data application:

• MongoDB uses its platform for real-time operational process helping end-users and Business process.
• Hadoop, on the other hand, gets the data from MongoDB; blend the data from different sources to produce machine learning models, which MongoDB will use it for Real-Time Operational processes.

Head to Head Comparison Between Hadoop and MongoDB

Both Hadoop and MongoDB are excellent in data partitioning and consistency, but when compare to RDBMS it does not perform well in data availability. Below is the top 9 comparison between Hadoop and MongoDB:

Key Differences between Hadoop and MongoDB

The differences between Hadoop with MongoDB are explained in points presented below:

• Hadoop is based on Java whereas MongoDB has been written in C++ language.
• Hadoop is Suite of Products whereas MongoDB is a Stand-Alone Product.
• Hardware cost of Hadoop is more as it is a collection of different software. However, the hardware cost of MongoDB is less when compared to Hadoop.
• When compared to Hadoop, MongoDB is more flexible it can replace existing RDBMS. Hadoop, on the other hand, can also perform all the tasks but need to add other software.
• MongoDB has the ability of geospatial indexing which is useful in geospatial analysis. This feature is not readily available in Hadoop.
• Hadoop is best for Large-Scale processing application whereas MongoDB is best for Real-Time Mining of data and Processing.
• MongoDB belongs to the NoSQL family whereas Hadoop use of SQL for processing of data.
• Hadoop is flexible in the format data; it can be of any available format whereas MongoDB imports only CSV and JSON format data.
• Hadoop is a Framework that can have much software for processing whereas MongoDB is a Database type.

Hadoop and MongoDB Comparison Table

Following is the comparison table

BASIS FOR COMPARISON	MongoDB	Hadoop
RDBMS System	It is designed to replace or enhance the RDBMS system, providing it with a variety of use cases.	It is not meant to replace the RDBMS system, but it acts as a supplement helps in archiving data or provide important use cases.
Outline	It is actually a database and is written in C++.	Collection of different software that creates a data processing framework. It is a Java-based application.
Framework	Stores data in collections, each data fields can be queried at once. Data is stored as Binary JSON or BSON and is available for querying, aggregation, indexing, and replication.	Consists of different software, the important components are the Hadoop Distributed File System (HDFS) and MapReduce.
Strength	It provides a more robust solution, more flexible then Hadoop. Can replace existing RDBMS.	The biggest strength of Hadoop is that it is built to handle Big Data. It is excellent for handling batch processes and long-running ETL jobs.
Designed	Designed to process and analyze a huge volume of data.	It is a database, Primarily designed for data storage and retrieval.
Weakness	Major complaint regarding MongoDB is fault tolerance issue, which can lead to data loss.	It depends mainly on ‘NameNode’, which is the only point of failure
Data Format	Should be in CSV or JSON format in order to import the data.	Can be of any available formats, it can handle both structured and unstructured data.
Hardware Cost	Cost effective because it is a single product.	Cost is more as it is a collection of software.
Memory handling	Efficient in handling memory as it is written in C++	It has the ability to optimize space utilization, which MongoDB lacks.

Conclusion

The above differences conclude that Hadoop is the best choice for a huge volume of data that require large processing and structuring of data. MongoDB is best for data that requires real-time processing and high data availability.

• In any organization, the data is very important, data increases day by day it is impossible to handle this huge volume of data by a single application. It is highly suggested that for any organization handling Big Data should make use of both Hadoop and MongoDB together.
• With all the suggestions, it is very important to know that both Hadoop and MongoDB were not built to brag security. Both of these applications meant to manage a huge volume of data with their excellent features and few drawbacks.
• If your organizations have low latency real-time data or need to remove existing RDBMS completely and starting a new transactional system then you need to go to MongoDB.
• If your organization needs a batch solution, running analytics while still being able to make use of SQL and query the data then Hadoop is the best option.
• Since Hadoop is known to handle a huge volume of data providing large-scale solutions, it can be considered for flexibility and scalability. Either way, even MongoDB is excellent in its scalability for analyzing huge volume of complex data and more efficient than RDBMS.
• When both Hadoop and MongoDB are used then it addresses each other’s weaknesses and strengths.
• Both platforms can be used as a Big Data Solution but it is very important to know if these solutions can be used and combined with your business environment. When the configuration is not done correctly, it would cause catastrophe for either of these platforms and their data.

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