We are living in the world of technologies where we have multiple solutions for handling large volumes of data. Among all, MongoDB and Hadoop are the two different platforms that have gained popularity for their features and capabilities. However, there is much difference among these two platforms. In this blog,we will discuss Hadoop Vs MongoDB, the need to use these platforms along with the head to head comparison between Hadoop and MongoDB. Let’s get started!
Hadoop is a software programming framework, an open source software that is used for handling large volumes of data and compute the same across the network of computers. The Hadoop framework is similar to Shell scripts and C++, purely based on the Java programming language. In simple terms, Hadoop is used to store, process and manage the data across different big data applications that are running as clustered systems.
Hadoop is capable of processing both structured and unstructured data, allowing it to be more scalable among the different servers. Hadoop is categorized into two layers: Map reduce layer, also known as processing and computation layer and storage layer also known as Hadoop Distributed File System.
Hadoop is a set of tools that is used for processing big data. Hadoop is capable of providing the capability of parallel processing of multiple data sets. Let us discuss some of the key factors that mark Hadoop as significant.
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MongoDB is a NoSQL database management platform that is highly scalable and flexible, capable of accommodating different sets of data models and also storing the data in the form of key-value sets. It is a document based platform that is designed as a solution to work with the large volumes of data which cannot be processed through the relational models. MongoDB is available for free and is also an open-source platform.
MongoDB is used by those who need to build the applications that require quick evolution, utilizing the scale out architecture. In simple terms, MongoDB makes use of documents and collections. Collections are the set of the documents and each document includes the set of key value pairs.
There are many reasons why MongoDB stood unique among the other different platforms. Few of them are listed below:
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Both the platforms or frameworks are definitely the best choices for big data management.However, there are differences among each other and it is the choice of the organization to choose one among them based on their requirements. Let us go through the major differences between Hadoop and MongoDB.
Hadoop: Hadoop is capable of storing both structured and unstructured data whereas a traditional database needs structuring and normalization of the data that you will be storing. Hadoop makes use of the distributed file system which helps in adding multiple nodes to a particular cluster and also helps in improving the storage capacity that is available.
MongoDB: MongoDB makes use of the concept of sharding which helps in distributing the data across a multiple nodes that are present with in the cluster and also help in scaling them horizontally.
Hadoop: Hadoop is primarily designed as a database that is used for storage and retrieval of the data.
MongoDB: MongoDB is used for processing and analysing large volumes of data.
Hadoop: Hadoop is written in Java programming language. It includes a collection of multiple packages that makes up the processing framework.
MongoDB: MongoDB is written in C++ programming language.
Hadoop: Hadoop makes use of the map reduce process to process the large volume of datasets. This algorithm works well when there is a processing of one piece of data at a particular point of time. When there is a need to connect the variables, it works a bit slow when compared with the single time processing.
MongoDB: MongoDB is capable of processing and updating the data using the aggregate pipeline framework provided by MongoDB.
Hadoop: The primary concern of Hadoop is with the data storage it is considered as most effective when it comes to the optimisation of the disk space, but there is also a possibility that the query research delivery can be delayed.
MongoDB: MongoDB is capable of making more memory for sending the data quickly. It makes use of indexes keeping them along with some data in the memory which also allows predicting the latency.
Hadoop: Hadoop is definitely not a replacement for the relational database management system but it is also capable of providing additional support for the relational database management system like achieving the data along with some higher set of use cases.
MongoDB: MongoDB is developed with the motto of supplanting or augmenting the relational database management system and also providing it with multiple ranges of potential applications
Hadoop: Hadoop includes different sets of softwares which are responsible for the creation of the data processing framework.
MongoDB: MongoDB is specifically used for querying, aggregating, indexing or replicating the data that is stored in the system. The data that is stored is presented in the form of binary and the data storage is done in the collections.
Hadoop: Hadoop is capable of handling large volumes of batch processes and also capable of running the ETL jobs efficiently.
MongoDB: MongoDB is flexible and robust when compared to Hadoop in terms of its features.
Hadoop: Hadoop depends upon the name not which can be a point of failure.
MongoDB: MongoDB processes no fault tolerance which might lead to the data loss occasionally.
Hadoop: Hadoop can be used for working with structured and unstructured data.
MongoDB: MongoDB can be used only in JSON and csv format.
Hadoop: The cost can be high in Adobe as it is a group of various different softwares.
MongoDB: The cost is comparatively low as it is a single product.
MongoDB is definitely a clear winner whenever it comes to the real time data processing. Hadoop is also doing a great job by storing and processing large volumes of data. Spark can also be used to make the processing faster. By using this spark framework, the processing of data takes place in the memory which will increase the speed at which the data processing is taking place. MongoDB also has multiple tools which are built in for the purpose of real time data processing. It also makes use of some external tools which are possibly connectors like Spark and Kafka, allowing faster and easy data processing.
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These days, the organizations are definitely looking for faster and quicker access to their data to obtain some meaningful insights and also make precise decisions. The features that are available in MongoDB are capable of solving and meeting the data challenges that occur. Below are some of the advantages of using MongoDB:
There is a requirement of manual coding when you need to use joins. This will further lead to slow execution and also less optimum performance.
Below listed are the advantages of Hadoop.
Below listed are the disadvantages of Hadoop:
Each and every business unit will have its own requirements and situations. Choosing the right solution for the business is one of the precise decisions taken by the business management. Both Hadoop and MongoDB have their own Features making them significant and popular in the world. Hadoop training and MongoDB training will help you gain the immense knowledge that is required for you to become a professional.
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Yes MongoDB is faster than Hadoop. It is more scalable and makes use of the aggregation pipeline flame work that helps in crossing and returning the results As Quick As possible.
Yes mongo baby is used for big data, considering it as a powerful choice for storing a large volume of data. MongoDB is a non relational database system or platform that can be used based on the distinctive requirements.
MongoDB cannot be replaced with Hadoop. MongoDB is definitely a flexible and scalable platform that is definitely a replacement for the relational database management system but acts as a supplement of archiving the data.
Yes, Hadoop is definitely a good choice for big data as it helps in storing and processing the large volume of data present in the cluster servers. It is also capable of executing the distributed process and is providing the building blocks for the applications and services that can be built.