Big Data

Hadoop vs MongoDB: Which is better?

The surge in the amount of data being produced in the modern world is nothing short of phenomenal. Due to the rate at which data is being generated worldwide, the volume doubles every two years. As a result, Statista predicts that by 2024, there will be 159 zettabytes, or 159 trillion gigabytes, of data available.

Data scientists all over the world are utilizing big data analytics techniques to handle and make use of the enormous amounts of data produced. Among those tools are Hadoop and MongoDB.


The old ways of storing and processing data will no longer be appropriate in the future due to the massive amount of data being produced. Big Data Analytics is the name given to the conventional approach, which has been quite well-liked in recent years. It has been in existence for more than ten years.

Several Big Data technologies have been developed to store and process this enormous amount of data, which may aid in structuring the data in the future. There are currently 150 NoSQL solutions as a result of this. These are platforms that are frequently linked to Big Data and are not powered by non-relational databases.

History of the Platforms

1.    MongoDB

The organization now known as MongoDB, formerly known as 10gen, created the MongoDB database solution in 2007. It was created as a cloud-based app engine to run numerous services and pieces of software.

Babble and MongoDB were the two components that the firm created. As a result of the product’s failure to make an impact, the application was abandoned and MongoDB was made available as an open-source project.

MongoDB took momentum after being released as open-source software and acquired the backing of a sizable community. Numerous improvements were made with the goal of enhancing and integrating the platform.

2.    Hadoop

Hadoop, in contrast to MongoDB, had always been an open-source project. It was developed by Doug Cutting and derived from the open-source web crawler Nutch, which was first developed in 2002.

After its debut, Nutch spent a number of years imitating Google. For instance, Nutch developed their own Distributed File System, or NDFS, at the same time that Google released its Distributed File System, or GFS.

Similar to how Google announced the use of MapReduce in 2005, Nutch likewise came up with the idea in 2004. Hadoop was then formally published in 2007. The idea from Nutch was carried over into Hadoop, which was used as a platform to handle massive volumes of data in parallel across clusters of inexpensive hardware.

Functionality of MongoDB & Hadoop

The tables and schemas that aid in the organization and structuring of data into columns and rows are the foundation of classic relational database management systems, or RDBMS.

RDBMSs make up the majority of the database systems in use today and will do so for a sizable portion of the foreseeable future. Recognizes the distinction between data lakes and data warehouses and databases.

1.    MongoDB

Data is stored in collections since MongoDB is a document-oriented database management system. As opposed to the many searches the RDBMS requires, these data columns can be queried once.

Data is stored in MongoDB as Binary JSON or BSON. Ad-hoc queries, replication, indexing, and even MapReduce aggregation of this data are all simply possible.

MongoDB was created in the C++ programming language and can be installed on Linux and Windows systems.

Linux workstations should be the best option for MongoDB, nevertheless, since it is thought to be best for real-time low-latency applications.

2.    Hadoop

Hadoop is a framework made up of an ecosystem of software. The two main parts of Hadoop are the Java-written MapReduce and Hadoop Distributed File System (HDFS).

The Hadoop secondary components are made up of a variety of other Apache products. Hive, Pig, HBase, Oozie, Sqoop, and Flume are some of these products.

Pig is used to analyze massive data volumes, whereas Hive is used to query data. Columnar databases HBase and Oozie aid with Hadoop task scheduling, and Sqoop is used to build interfaces with other systems, such as RDBMS, BI, and analytics systems. (Read more about the best BI tools and methods.)

Limitations of MongoDB & Hadoop

1.    MongoDB

A user must manually enter codes in order to use joins. It may result in less effective performance and slower execution.

If a user chooses to proceed without joins, MongoDB will need extra memory because all files will need to be transferred from the disc to memory.

2.    Hadoop

The majority of entry-level programmers are unable to use Hadoop because of the high level of java expertise required for MapReduce operation. As a result, SQL is preferred over Hadoop because it is simple for beginning programmers to utilize.

Hadoop is a sophisticated technology that calls for sophisticated understanding to provide features like security protocols.

Final Thoughts

Hadoop is the most reliable and appealing tool in big data, according to the research. It gathers a sizable group of data in a designated system and processes the data concurrently on a number of nodes.

MongoDB, on the other hand, is renowned for its rapid scaling, industry-leading availability, and snappy performance or implementation.

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