In this article, you going to see discussion related MongoDB Vs Hadoop and What is Best for Better Handling Big Data.
If you are looking for best comparison of MongoDB Vs Hadoop, then you are a right place.
Let’s start with the topic of MongoDB Vs Hadoop.
MongoDB:
MongoDB is a NoSQL database, Whereas Hadoop is one type of framework which is an opensource.
It consist of set of programs with help of which storing & processing Big Data is done in a distributed environment.
The main point regarding MongoDB, it is document oriented database(NoSQL).
In MongoDB, all type type data is going to be stored in JSON which is in well document format.
It is also called as document-oriented database management system, because stores data in a collection.
High Flexibility and Scalable Database Management System.
It is Document Based which includes different type of data models and data is stores in key value sets.
MongoDB is completely free and opensource.
Below are the Key Feature of MongoDB are as follow as:
1)Rich Query Langauge and supports aggregation, text search, and CRUD functionality.
2)Due to embedded data models, unlike relational databases requires lesser input and output operations.
3)Indexes support for faster queries.
4)Using replica datasets fault tolerance is easily get provided.
5)With the help of replication gets complete ensurity regarding data is stored on multiple servers, creating redundancy, and ensuring high availability.
6)Horizontal scalability possible is possible only due to features called sharding.It help in increasing data which is is lower than vertical methods of handling system growth.
7)Consist of Multiple store Engine which ensures right engine is used for the right workload and also increase performance.
Storage Engines Include:
Frist is WiredTiger : It is also called as default engine used in case of new deployments for versions 3.2 or higher versions.
It can handle easily workload.
Features include compression, check pointing, and document-level concurrency for write operations.
Second is In-Memory Storage Engine :
The Engine is used for storing documents in-memory instead of on-disk.
Which increases the predictability of data latencies.
Third is MMAPv1 Storage Engine :
It is one of earliest storage for MongoDB and works on Version 3 or earlier.
Works well in all condition of work load which include bulk in-place updates, reads, and inserts.
Reason to Use MongoDB?
1)Query language used by MongoDB supports dynamic querying.
2)Using relational databases, with the help of several tables for a construct and Mongo’s document-based model, you can represent a construct in a single entity.
3)It is easy to scale due Horizontal storage.
4)Schema in MongoDB is implicit and it is easier to represent inheritance in the database in addition to improving polymorphism data storage.
What is the Limitations of MongoDB?
What is the Limitations of MongoDB?
1)Document sizes cannot be bigger than 16MB.
2)MongoDB requires a lot of memory due to Lack of joins.
3)To do manually add code, we have to use joins, due to which slower execution and less-than-optimum performance
4)Functionality nesting is limited and which cannot exceed 100 levels.
Hadoop
It is completely free and consist of open source set of programs which helps in modifying for your big data processes.
The important point is consist of four module and each perform a specific task related to big data analytics.
It include are as follow as:
Distributed File-System
It is very important because of following point given below:
1)Data get easily stored with the help of shared.
It accessed across an extensive network of linked servers.
2)Beacasue of this, Work with data became easy as though you were working from local storage.
3)Shared disk file system limits data access for offline users and you can access data even when offline.
4)Limited to the host computer’s OS. You can access it using any computer or supported OS.
MapReduce
It is the second most important module. It help you to work with data within Hadoop.
Important point is it Perform two tasks which are as follow as:
1)Mapping: Include transforming a set of data that get converted into a format and it can be easily analyzed.
It fulfills this by filtering and sorting.
2)Reducing :Follow mapping. Mathematical operations get performs (e.g., counting the number of employee over the age of 24).
Hadoop Common
It is a collection of tools which support the other three Hadoop modules.
Include scripts and modules required to start Hadoop
Hadoop YARN
It is mainly use for enabling resource management and job scheduling.
YARN provides for Hadoop developers an efficient way for writing applications and manipulating large sets of data.
And also it makes possible simultaneous interactive, streaming, and batch processing
Reason to Use Hadoop?
1)Large amounts of data can be quickly stored and proceed.
2)Always increase in data generated from the internet and social media which makes its capabilities a key resource for dealing with high volume data sources.
3)Due to Distributed File System, Hadoop get high computing power which helps for fast data processing.
4)Important point regarding this is that it protects against hardware failure by redirecting jobs.
5)Store a wide variety of structured or unstructured data which include images and videos.
6) Due to open-source framework, it runs on commodity servers.
And which are more cost-effective than dedicated storage.
What is Limitations of Hadoop?
It have set of limitations which are as follow as:
1)Beacasue of Programming, MapReduce is suitable for simple requests.
2)Requires entry-level programmers which have the java skills necessary to work with MapReduce.
3)It is a is a complex application and due to which it requires a complex level of knowledge to enable functions such as security protocols.
4)It does not provide a full suite of tools necessary for handling metadata or for managing, cleansing, and ensuring data quality.
Conclsion on : What Should We Use For Big Data? MongoDB or Hadoop?
Both Hadoop vs MongoDB are popular choices for handling big data. However, although they have many similarities.
Example of similarities is open-source, NoSQL, schema-free, and Map-reduce.
Their approach to data processing and storage is different.
It is exactly the difference that finally helps us to determine the best choice between Hadoop vs. MongoDB.
I hope you like this article and if you have any query, please comment below.
