Hadoop was built to store and analyze large volumes of data across several computer clusters. It's a group of software programs that construct a data processing framework. This J**a-based framework can process enormous amounts of data quickly and cheaply....
Hadoop was built to store and analyze large volumes of data across several computer clusters. It's a group of software programs that construct a data processing framework. This J**a-based framework can process enormous amounts of data quickly and cheaply.
Hadoop's core elements include HDFS, MapReduce, and the Hadoop ecosystem. The Hadoop ecosystem is made up of many modules that help with system coding, cluster management, data storage, and analytical operations. Hadoop MapReduce helps analyze enormous amounts of organized and unstructured data. Hadoop's parallel processing uses MapReduce, while Hadoop is an Apache Software Foundation trademark.
Millions of people use MongoDB, an open-source NoSQL document database. These users include startups and multinationals. MongoDB capabilities are used by industry-leading companies and consumer tech startups.
MongoDB is a document-oriented C++ database. It solves SQL schema-based databases' performance, **ailability, and scalability issues. It's a database that works like the web. MongoDB, like other NoSQL databases, doesn't employ tables, rows, or columns. It stores its data in BSON documents, which bundle relevant information under a single title.
Go through this article to find out more about Hadoop and MongoDB and how they are different from each other.
Apache Hadoop is an open-source J**a platform. It manages the processing and storage needs of data-intensive applications. The Hadoop platform must first distribute large data and analytics jobs among the computer cluster's nodes. These tasks are then separated into reasonable workloads that can be completed simultaneously.
Hadoop can process structured and unstructured data and scale from one server to thousands without sacrificing reliability. Hadoop-based programs run on clusters of commodity machines with massive data collections. These machines offer more processing power at a lower cost. Hadoop employs a distributed file system called the Hadoop Distributed File System (HDFS) to store its data. This is like s**ing data on a PC's local file system.
At its base, Hadoop is composed of two primary layers, which are −
The Processing and Computation layer, also known as the Map Reduce layer.
The Storage layer also known as Hadoop Distributed File System.
Google developed MapReduce for creating distributed applications. It was intended for dependable and fault-tolerant processing of multi-terabyte data sets on huge clusters (thousands of nodes) of commodity hardware. Hadoop is an Apache-managed open-source platform where MapReduce is implemented. This is like s**ing data on a PC's local file system.
Hadoop Distributed File System (HDFS) is based on Google File System (GFS), which operates on commodity hardware. It's like other distributed file systems. However, this system differs significantly from others. It's error-tolerant and runs on low-cost hardware. It delivers high throughput for accessing application data and is suitable for large datasets.
In addition to the two primary components, the Hadoop framework includes the two modules below.
Yet Another Resource Negotiator (YARN) − It manages the cluster's nodes and resources. It schedules work.
Hadoop Common − Offers standard J**a libraries that are applicable to all modules and can be used by any of them.
MongoDB is a document-oriented open-source database that stores and manipulates data efficiently. Anybody can use it. MongoDB is a NoSQL database since its data are not structured in tables.
MongoDB Inc. first made the database public in February 2009. Server-Side Public License governs its use. It offers official driver support for C, C++, and C#. Programming languages supported include Net, Go, J**a, Node.js, Perl, PHP, Python, Motor, Ruby, Scala, Swift, and Mongoid. To design apps using these languages. Facebook, Nokia, eBay, Adobe, Google, and others store large volumes of data with MongoDB.
_id: _ MongoDB documents must h**e an id field. MongoDB's _id field stores a unique value. _id is like the document's principal key. MongoDB will construct a _id field if you create a new document without one.
Collection: This is a group of documents in MongoDB. In any other RDMS, such as Oracle or MS SQL, a table is the same thing as a collection. One database can h**e more than one collection.
Cursor: A pointer to the set of results from a query. Clients can move a cursor back and forth to get results.
Database: This is a container for collections, just like RDMS is a container for tables. On the file system, each database has its own set of files. There can be more than one database on a MongoDB server.
Document: A document is basically a record in a MongoDB collection. In turn, the document will be made up of field names and values.
Field: A name-value pair in a document. There may be zero or more fields in a document. In relational databases, a field is like a column.
The following table highlights the major differences between Hadoop and MongoDB −
| Basis of comparison | Hadoop | MongoDB |
|---|---|---|
| Data Storage | It works with structured and unstructured data. Due to Hadoop's distributed file system, adding more nodes to a cluster increases storage space. | In MongoDB, CSV or JSON formats are used. A technique called sharding is used by MongoDB to enable it scale horizontally by distributing data across different nodes. |
| Purpose | Its primary function will be seen as a database. | It was developed specifically to evaluate and process a huge quantity of data. |
| Language Used | Hadoop is written in j**a. | MongoDB is written in C++. |
| Data Processing | Hadoop uses MapReduce to process huge datasets. When processing one piece of data at a time, this algorithm works well. MapReduce may slow things down when variables need to be connected. | You may process and update data with MongoDB's aggregate pipeline framework. Atlas Search's aggregation pipelines and full-text search help restrict searches. |
| Memory Management | On the other side, Hadoop is primarily concerned with disc storage. It is more effective at optimizing the use of disc space, but the delivery of query results will be delayed as a result of the requirement to read from the drive. | MongoDB makes the best use of its memory so that data can be sent quickly. It keeps indexes and some data in memory so that latency can be predicted. |
| RDBMS support | It is not intended to serve as a replacement for relational database management systems (RDBMS), but rather to provide additional support for RDBMS in the archiving of data while also giving it a wide variety of use cases. | It is developed with the intention of either supplanting or augmenting the RDBMS and providing it with a broad range of potential applications. |
When compared to conventional databases, Hadoop and MongoDB offer a number of advantages, making them superior choices for managing large amounts of data.
MongoDB can perform all the functions traditionally associated with a database. Because of the flexibility of its structure, MongoDB makes it simple to store data in a manner that does not call for a great deal of prior transformation before it can be used. Because of its query language, it can obtain data quickly and effectively, and even to process it on the fly.