The Data Processing Battle: Hadoop vs MongoDB

Hadoop and MongoDB are both technologies that are used to store and process large amounts of data, but they are used for different purposes and are not directly comparable.

For storing and analysing massive volumes of data dispersed across a cluster of commodity machines, Hadoop is an open-source software platform. It is made to manage huge data demands that are too much for a single server or a conventional database to handle. 

The MapReduce programming architecture, on which Hadoop is built, enables programmers to create applications that can handle enormous volumes of data concurrently across numerous devices.

MongoDB is a popular, open-source NoSQL database that is designed for storing and querying large amounts of data in a flexible, JSON-like format. It is optimized for handling high volumes of data and is designed to scale horizontally across multiple servers.

MongoDB is known for its ability to handle unstructured data, which makes it well-suited for storing data that may have a complex or changing structure.

In general, Hadoop is used for batch processing and analysis of large datasets, while MongoDB is used for storing and querying large amounts of data in real-time. Both technologies can be used to store and process big data, but they are suited for different use cases and have different strengths and capabilities.

Because MongoDB is based on C++, it handles memory better than other databases. Hadoop is a set of Java-based programmes that provide a framework for data archiving, retrieval, and processing. Compared to MongoDB, Hadoop optimises space better.

All fields in a MongoDB table can be queried, indexed, aggregated, or replicated simultaneously. Data is stored in JSON, BSON, or binary in MongoDB. Additionally, data in MongoDB must be imported in JSON or CSV format. Since Hadoop can handle a variety of data formats, processing no longer requires data transformation.