In this MongoDB Tutorial, we shall learn the basics of MongoDB, different CRUD Operations available in MongoDB, and integrating MongoDB to applications developed using programming languages like Java, Python, Kotlin, Java Script, etc.
An Introduction to MongoDB
- a Document Database – where entries are stored as documents.
- an Open Source – source code is made freely available and may be redistributed and modified.
The records are stored as Documents in MongoDB. A Document consists of field:value pairs. A MongoDB Document look similar to a JSON Object.
Following is an example of MongoDB Document.
cars: [ "BMW 320d", "Audi R8" ]
- MongoDB is a NoSQL Database. A NoSQL Database is one that provides mechanism for storage and retrieval of data that is modeled in means other than tabular relations. MongoDB uses field-value pairs to store records and is considered one of the simplest NoSQL Databases.
- MongoDB is a Cross Platform. If can run on Windows Vista or Later, Linux, OS X 10.7 and later, Solaris and FreeBSD.
- MongoDB is Schema-less, meaning fields can vary from document to a document in a collection.
- MongoDB is horizontally Scalable. You may add as many number of nodes (cheap commodity hardware) in the cluster to address large data sets and high throughput applications.
- MongoDB provides Automatic Failover and Data Redundancy. MongoDB Replica Set realized replication of data over nodes, which helps to achieve data redundancy and thus even if primary node fails or goes down, the data is still available on other nodes.
- MongoDB supports Multiple Storage Engines.
Storage Engines are the guys that manage how data is stored in memory. Currently MongoDB supports following storage engines.
- MongoDB can do federated faceted search operations.
- MongoDB can do graph operations.
With all the capabilities and features of MongoDB known, let us have a look into the places where we can use MongoDB.
As NoSQL databases can scale horizontally much more quickly and inexpensively, and low latency for responses operating on highly selective access criteria, and are designed to take advantage of cloud architectures, and many more reasons, MongoDB fits well in Big Data Ecosystem.
MongoDB provides MapReduce, Aggregation Pipelines and many more in-built commands to work with high volumes of data.
Real time Analytics
MongoDB’s low latency and capability to analyze semi-structured or unstructured data makes it a great choice for real time analytics.
Index – MongoDB Tutorial
With a brief introduction tour to MongoDB, let us dive into working with MongoDB.
- MongoDB Database
- MongoDB Collection
- MongoDB Document
- MongoDB Integration with Other Programming Languages
Follow this MongoDB Tutorial to learn the basics and advanced concepts of MongoDB Database, to use it in your application development with ease.