MongoDB vs Hadoop Handling for Big Data


Welcome to MongoDB vs Hadoop. The approximately 80 to 90 percent of data in the different format in the modern era.

The new day and new year approximately created 2.5 quintillion bytes of data come from different formant sources like transaction social media image and video.

There are many solutions are available in context to manage Big Data. The approximately 120 solutions are available.


“It will good to cover my previous articles before this. Its help to learn more about MongoDB and Hadoop also Big data. Click here”

You will learn in this section.

  • Background
  • MongoDB
  • Hadoop
  • Current scenario
  • Vertically and horizontally
  • CAP classification and consistency

Alt tag MongoDB vs Hadoop

Current scenario

  • “The amount of data increasing exponentially and is currently doubling in size every two years. The data will reach 44 zeta bytes (44 trillion gigabytes) in the year 2025”.
  • To choose the best database to contain the flexible data model. The relation database predefines schema hard to change.
  • The Relation database is in the market at 1970 but the recent year the goal was not achieved due to nature of the data.
  • The data warehouse is the core part of business intelligence. The past 20-year data warehouse tool is used for deep mining. Some new database technologies Like HBase, Cassandra, and MongoDB commonly referred to as NoSQL Database handling a large amount of data.
  • The Relation database consistently to use normalization. There is serval challenge rising in data and schema.

MongoDB vs Hadoop

The big data consists of a huge amount of information which consist of volume, variety, velocity, veracity. The Hadoop vs MongoDB both of these solutions has many similarities NoSQL Open source MapReduce schema-less.

  • The using a single database fit for all situations is a problem.
  • The data upload one day in Facebook approximately 100 TB and approximately transaction processed 24 million and 175 million twits on twitter.
  • How the data manipulation in the relational database.
  • In order to increase or grow data the difference, big data tools are used.
  • Over the increase of automation number of the system generated a huge amount of the data. In IT industries generated a huge amount of data. The amount of the last 5 year is more than the data generated last 20 year.
  • The Data is growing too fast have complex data in volume terabyte to petabyte and variety of format hybrid structure or unstructured. This is called the big data phenomenon.
  • The conventional database does not handle the massive amount of data.
  • The Relation database consistency to use normalization. There is serval challenge rising in data warehouse works load transactional schema data aggregation. The number of the problem is rising in RDBMS problem in data modeling and constraints also horizontally scalability.

In early 2000 two, the most popular company suffer the scalability issue which is Google and Amazon. These companies are decided to build thereon solutions such as Google with Big table and Amazon later with Dynamo DB.

As 2017 there more than 225 solutions of NoSQL database.NoSQL data model are schema-less and the result is more fixable data model. The schema changes at any time because NoSQL database has a dynamic schema.

Vertically and horizontally

The majority of NoSQL database is open source. These databases are horizontally scalable and distributed shared nothing architecture. The node is working through network do not share any memory or disk.

Vertically scalable means increased the power of hardware like CPU RAM Hard disk. Horizontally scalable increased capacity by adding more machine or database server. MongoDB supports the horizontally.


  • HDFS(Hadoop distributed file system) run on commodity hardware.
  • The main difference between a distributed file system and Hadoop distributed file system it provides facility to fault tolerance and design to developed for low-cost hardware.
  • The Hadoop is suitable for the application that managed a large amount of data.  The Apache Hadoop is 100 % open source provides the new way of storing and processing data.
  • its developed by Apache Nutch web search engine project. but currently, HDFS is part of Apache Hadoop subproject.

It is open source product and used Google Map-reduce framework. The Hadoop enables distributed parallel processing for the huge amount of data. The processing data is no big if use Hadoop.


  • The MongoDB is leading NoSQL database also store data in form of the document. They have forty-three thousand customers and thirty million downloads.
  • The first version of Mongo dB was developed in 2007 New York-based organization it’s also called Mongo DB Inc. Its first release developed in PAAS (platform as serveries).
  • Later on, MongoDB comes in a market as open source. In 2013 changed its name 10 gen to MongoDB Inc.
  • They store data in the form of collection instead of the table in the case of a relational database. The document in the MongoDB supports any data types such as array numbers string.
  • A good example of horizontal scaling is Cassandra, Mongo DB. The Mongo DB handle the dynamic schema and consists of a server dynamic field. The Mongo DB like schema-less support array data types.

CAP classification and consistency

The cap theorem is used to understating the capabilities of MongoDB better. Master and slave functionality use Mongo DB for replication.

All the read and write request on the master node. Slave node also has all the set of data. The system is working on a master node.

MongoDB provides the excellent function of following

  1. consistency
  2. availability
  3. partition tolerance


Your data is consistency what you write is what you read. And all the replication contain the same.


Your read and write data is available all the time. Basically, data is available all the time.

Partition tolerance

Multiple entry points. If one o node fails and system are still work and become consistency. System split and operational.

The MongoDB best when the data structure is large and structure of data change continuously this strangle example Mongo DB handle the dynamic schema and consists of server dynamic field. the MongoDB also provides very rich functionality

This a high-level architecture of MongoDB vs Hadoop. The multiple mappers read the input via Hadoop MongoDB.  The output is saved in one reducer which writes back the result to MongoDB or Hadoop.

Alt tag MongoDB vs Hadoop

1 MongoDB Hadoop
Data Analysis MongoDB is the best choice is the case of aggregation operation.
it uses real-time data processing.
Using programming language models it provides facility to process a large amount of is a framework that allows distribution processing.
MongoDB NoSQL database has utilized a part of huge
information one thing in one time huge data sets. while Hadoop is used for processing.
It is designed as a single server of thousand of’s offering local computation and storage.
MongoDB supports the sharding architecture which supports
big data set store multiple nodes.
Its core part is map-reduce consists of two function map and reduce.
MongoDB provides the facility of horizontal scalability to add
more server to increase the storage space.
Hadoop process single or multiple jobs take data from MongoDB. basically, MongoDB is the back-end database.
AS soon as MongoDB is available for Hadoop query the data. Hadoop has also used the data warehouse.


2 Characteristics MongoDB Hadoop
Writing  Global write lock Cached locally than send to the data node
Storage Schema-less distributed database Distribution file system
Reading B tree index random plus sequence Sequence block access
Reliability Replication Replication
MongoDB is NoSQL database Hadoop is Framework


Use Case MongoDB Hadoop
Both platforms have the same strength
related to (RDBMS) like architecture, handling
large amounts of aggregated data Map Reduce
MongoDB is best for real-time data analysis also, have geo-spacial indexing ability. Hadoop is best for ETL and batch is built for big data and the excellent use case of Hadoop
is processing log file.
Strength MongoDB is more flexible than Haddop it has
the capability to replace the existing relational database.
Hadoop can manage data in any format.its map reduce
is better than’s support multiple databases such as for SQL query run with Hive in Hadoop.
Weakness Security is the main concern. not reliable in the transaction like ACID. Security is the main concern.
The choice to use New transaction system or (RDBMS) are replacing to use MongoDB. Depending on your data and its size. for log run, data analytics Hadoop is the best choice

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