Hadoop and MongoDB, both provide incredible value to developers and organizations alike. Due to both having their respective advantages and benefits, it proves to be quite hard for budding developers to decide what they will specialize in or focus upon. However, both have their individual fields where they will be more effective or tasks which only Hadoop or MongoDB can tackle. It is quite challenging for developers to decide upon a MongoDB or Hadoop course, but both open up more opportunities and prospects for their careers.
What is Hadoop?
Hadoop by Apache is a set of openly sourced software tools and utilities which are highly effective to process immense amounts of data and/or in computing. Hadoop is one of the systems which are known for providing software frameworks which work with distributed storage with the help of a programming model known as Ma3pRuduce.
Hadoop was created with the idea of creating a system that does not rely on the stability of computers and runs taking the future failures into account and is not harshly affected by it. Hadoop was initially built to run on weaker hardware and mostly still, is run on networks of basic or older systems. A Hadoop course is highly recommended if one wishes to delve into how Hadoop truly works.
Here are a few reasons why Hadoop is used by developers around the world:
- With Hadoop, one is able to process and store massive amounts of data from various sources.
- Hadoop is powered by the distributed file system which gives an edge in providing the computing power required for rapid computation of data.
- Hadoop expects and provides protection against hardware failure by commissioning other nodes (systems or computers) to store multiple backups of the data.
- It helps Hadoop developers to skip pre-processing it and store data in structured and unstructured formats.
What is MongoDB?
MongoDB was developed by MongoDB Incorporated with the common goal of creating a query language which can support text searches, aggregation features while being rich during create, read, update and delete operations. MongoDB indexes are built to promote rapid queries. MongoDB is a document-centric NoSQL database program that utilizes documents similar to JSON file formats and implements JSON schemas. JSON can be classified as an open standard file format. MongoDB is a source-available program which does not necessarily mean it is open-source.
Similar to Hadoop, MongoDB is also used to sustain faults and failures. MongoDB provides this with the creation of datasets that are replicas of the originals. The creation of replica datasets makes sure that the data is stored on multiple servers which creates redundancy and ensures that the data is always available even during failures. This decreases the chances of data loss or damage to the process.
MongoDB is also known to organizations and individuals for saving a lot of cost and expenses by using horizontal scaling instead of vertical scaling methods. MongoDB commissions a multiple number of storage engines which ensures that the most efficient engine is used for specific workloads. This also contributes to ensuring enhanced performance.
Corporations and firms today have the immense requirement for rapid and versatile access to data to extract valuable insights out of it which help them in making effective business decisions. MongoDB offers a few fundamental benefits which help in meeting the unceasing need to tackle these challenges that are related to storing and processing data. Here are a few of those benefits:
- MongoDB is a document-centric model which represents constructs as a single entity, unlike relational databases which require multiple tables for a single construct. This is especially advantageous when working with data which is immutable.
- MongoDB uses a query language that can support dynamic querying.
- MongoDB helps in representing inheritance in databases easily while helping in the improvement of polymorphism data storage.
- Increases scalability due to it following the horizontal scaling methodology
- It is getting more powerful with time; the latest Version 4.4 comes with new aggregation stages, expressions, and other improved features.
Hadoop vs MongoDB
Hadoop is an application based on Java and a set of software and tools which creates a data processing framework, while MongoDB is a database that is written using C++. Here are some of the differences between the two which can help you determine if you want to pursue a Hadoop course or modules on MongoDB.
Hadoop vs. MongoDB and their relation with relational database management systems
Hadoop is one of the complimentary supplements of relational database management systems which help in storing and archiving data or providing support during failures. MongoDB was designed with the intention of replacing or at least enhancing the relational database management system.
Stability and strength
Hadoop provides a very strong framework that helps in handling Big Data and to tackle batch processes or extract, transform and load (ETL) tasks that usually go on for longer durations. MongoDB is more versatile and helps in providing faster solutions as compared to Hadoop. MongoDB can also completely replace the existing relational database management system.
Hadoop helps in optimizing space usage, unlike MongoDB which cannot utilize space as efficiently. MongoDB is built using C++, hence it can handle memory very well.
Hadoop is a database that was initially designed for storing data and the retrieval of data in case of crashes or failures. MongoDB was created with the intention of processing and analyzing large amounts of data.
Data format support
Hadoop can process data from various formats, regardless of the data being structured or unstructured. MongoDB can only work with CSV or JSON format and is unable to import the data if it is not of these two formats.
The main reason for the failure when using Hadoop is NameNode, which is associated with the Hadoop Distributed File System service and can make the entire system go offline if it suffers a fault. MongoDB lacks a bit as compared to Hadoop in the area of fault tolerance which can lead to massive data loss and the shutting down of the current process.
Not to forget, Hadoop is more effective in large-scale applications and projects while MongoDB is more efficient in live data processing and mining. It is upon the individual requirements of the corporation or project, where it can be declared if one or the other is better built for.