MongoDB :

MongoDB is a powerful, open-source, NoSQL database management system designed to handle large volumes of unstructured or semi-structured data, making it ideal for modern applications that require flexibility, scalability, and high performance. Unlike traditional relational databases that store data in tables with predefined schemas, MongoDB uses a document-oriented data model, where data is stored in BSON (Binary JSON) format within flexible, schema-less documents. These documents are grouped into collections, providing a more natural and dynamic way to represent complex data structures that may evolve over time. MongoDB’s horizontal scalability, achieved through sharding, allows it to distribute data across multiple servers, making it capable of handling massive amounts of data and high-traffic workloads. Additionally, MongoDB supports rich querying, indexing, and aggregation capabilities, enabling developers to perform complex data operations efficiently. Its replica set feature ensures high availability by maintaining multiple copies of data across different servers, providing automatic failover and fault tolerance. With built-in support for geospatial queries, full-text search, and real-time analytics, MongoDB is a versatile database solution that is widely used in a variety of applications, including content management systems, e-commerce platforms, IoT applications, and mobile apps, where the ability to store, retrieve, and analyze diverse and rapidly changing data is crucial. MongoDB’s intuitive query language, which is similar to JSON, combined with its strong ecosystem of tools and libraries, further enhances its popularity among developers, making it a go-to choice for building robust, scalable, and flexible applications in today’s data-driven world.

Why MongoDB?

MongoDB is often chosen over traditional relational databases and other NoSQL databases for several reasons:

1. Schema Flexibility:

• MongoDB’s document-oriented structure allows developers to store data in a more natural and flexible way, particularly for applications where data requirements evolve over time. Unlike relational databases that require a fixed schema, MongoDB can store documents with varying structures within the same collection.

2. Scalability:

• MongoDB is designed to scale horizontally, making it suitable for handling large volumes of data and high-traffic applications. Sharding in MongoDB allows data to be distributed across multiple servers, improving performance and enabling databases to grow with the application.

3. Performance:

• MongoDB can offer better performance for certain types of workloads, especially those involving large datasets or requiring rapid reads and writes. The document-based model allows for efficient data retrieval and manipulation, particularly in scenarios where data is accessed or modified as whole objects.

4. Ease of Use:

• MongoDB’s query language is similar to JSON, making it intuitive and easy for developers to learn and use, especially for those already familiar with JSON or JavaScript. Its support for various programming languages also adds to its ease of integration into existing tech stacks.

5. Rich Query Language:

• MongoDB offers a powerful and flexible query language that supports rich queries, including filtering, sorting, and aggregating data. The aggregation framework in MongoDB allows for complex data processing pipelines, similar to SQL’s GROUP BY and JOIN operations.

6. High Availability and Fault Tolerance:

• MongoDB’s replica set feature ensures that data is replicated across multiple servers, providing high availability and automatic failover. If a primary server goes down, a secondary server automatically becomes the primary, ensuring continued operation.

7. Geospatial Support:

• MongoDB has built-in support for geospatial queries, making it a popular choice for applications that require location-based services, like mapping and tracking applications.

8. Community and Ecosystem:

• MongoDB has a large and active community, extensive documentation, and a rich ecosystem of tools and libraries. This makes it easier to find support, integrate with other technologies, and leverage a wide range of third-party tools.

9. Suitable for Modern Applications:

• MongoDB is particularly well-suited for modern, cloud-based, microservices architectures. Its ability to handle unstructured data, combined with its scalability and performance, makes it ideal for applications like real-time analytics, content management, and Internet of Things (IoT) systems.

Where to use MongoDB?

MongoDB is well-suited for a variety of use cases, especially those involving large, complex, or evolving datasets. Here are some scenarios where MongoDB is commonly used:

1. Content Management Systems (CMS):

• Use Case: Websites or platforms that need to manage and serve a variety of content types (e.g., articles, blogs, multimedia content).
• Why MongoDB: Its flexible schema allows for the easy storage of diverse content types and metadata, which can vary significantly from one piece of content to another.

2. Real-Time Analytics:

• Use Case: Applications that require real-time data analysis, such as monitoring systems, social media analytics, or online recommendation engines.
• Why MongoDB: MongoDB’s high-performance read and write capabilities, along with its powerful aggregation framework, make it suitable for processing large volumes of data quickly.

3. Internet of Things (IoT):

• Use Case: Applications that collect and process data from IoT devices, such as sensors, smart home devices, or industrial equipment.
• Why MongoDB: Its ability to handle unstructured and semi-structured data, combined with horizontal scalability, makes it ideal for storing the diverse and high-velocity data generated by IoT devices.

4. E-Commerce Applications:

• Use Case: Online stores and marketplaces that need to manage product catalogs, user accounts, shopping carts, and transaction histories.
• Why MongoDB: The flexibility to store complex, nested data structures, such as product attributes, reviews, and user preferences, allows for a more dynamic and personalized shopping experience.

5. Mobile and Web Applications:

• Use Case: Apps that need to handle dynamic, user-generated content, such as social media platforms, messaging apps, or collaborative tools.
• Why MongoDB: Its JSON-like document structure aligns well with the data formats used in mobile and web apps, making it easier to develop and maintain these applications.

6. Gaming Applications:

• Use Case: Games that require real-time updates, leaderboards, player profiles, and in-game transactions.
• Why MongoDB: MongoDB’s ability to handle large volumes of concurrent read and write operations makes it suitable for the high-performance demands of gaming environments.

7. Geospatial Data and Location-Based Services:

• Use Case: Applications that need to manage and query geospatial data, such as mapping services, ride-sharing apps, or logistics platforms.
• Why MongoDB: Its built-in support for geospatial indexes and queries allows for efficient storage and retrieval of location-based data.

8. Personalization Engines:

• Use Case: Systems that provide personalized content or recommendations, such as news feeds, product recommendations, or music streaming services.
• Why MongoDB: The ability to store and analyze large amounts of user behavior data allows for the creation of sophisticated recommendation algorithms.

9. Finance and Banking Applications:

• Use Case: Applications that manage financial transactions, user accounts, or fraud detection.
• Why MongoDB: Its support for ACID transactions (with multi-document transactions) and high availability features make it suitable for applications requiring data consistency and reliability.

10. Log and Event Data Storage:

• Use Case: Applications that need to store and analyze logs, audit trails, or event data, such as monitoring systems or cybersecurity tools.
• Why MongoDB: Its scalability and ability to handle large volumes of semi-structured data make it ideal for log management and event tracking.

11. Customer Data Management (CDM):

• Use Case: Systems that manage customer information, interactions, and preferences, such as CRM systems.
• Why MongoDB: The flexibility to model complex customer profiles and interaction histories, combined with the ability to scale as customer data grows, makes MongoDB a strong choice.

In summary, MongoDB is particularly well-suited for use cases that involve large, dynamic datasets, require high performance and scalability, and benefit from a flexible schema. It’s a strong choice for modern applications across a wide range of industries.

Module 1: Intoduction to MongoDB

• Concepts
• Scaling
• SQL and Complex Transactions
• Documents Overview
• Installing MongoDB(Windows)
• Installing MongoDB (Linux)
• JSON
• Dynamic Schema
• Mongoimport
• Cursors Introduction
• Query Language : Basic Concepts
• Query Language : Projection
• Query Language: Advantages of a Dynamic Schema
• Shell : Queries
• Sorting
• Query Language: Cursors

Module 2 : CRUD , ADMIN COMMANDS

• Insertion
• Update
• save() command
• Partial Updates & Document Limits
• Removing Documents
• Multi Update
• Upsert
• Wire Protocol
• Bulk() Operations and Methods
• Common Commands
• runCommand()
• isMaster()
• serverStatus()
• currentOp() & db.killOp()
• stats() & collection.drop()
• Review of Commands

Module 3 : Storage Engine

• Introduction
• Storage Engine : WiredTiger
• createIndex(), getIndexes() & dropIndex()
• Collection Scans
• Index Notes
• Unique Indexes
• Sparse Indexes
• TTL Indexes
• Geospatial Indexes
• Text Indexes
• Background Index Creation
• Explain Plans
• Covered Queries
• Read & Write Recap
• currentOp() & killOp()
• The Profiler
• mongostat and mongotop

Module 4: Replication Overview

• Asynchronous Replication
• Statement-based vs. Binary Replication
• Replication Concepts
• Automatic Failover
• Recovery
• Starting Replica Sets
• Initiating a Replica
• Replica Set Status
• Replica Set Commands
• Reading & Writing
• Failover
• Read Preference
• Read Preference Options

Module 5 : Reconfiguring a Replica Set

• Arbiters
• Priority Options
• Hidden Options & Slave Delay
• Voting Options
• Applied Reconfiguration
• Write Concern Principles
• Examining the ‘w’ Parameter
• Write Concern Use Cases & Patterns
• Reexamining the Page View Counter Pattern
• wtimeout & Capacity Planning
• Replica Sets in a Single Datacenter
• Replica Sets in Multiple Datacenters
• Replica Sets and Storage Engine Considerations

Module 6: Scalability

• Sharding & Data Distribution
• Replication with Sharding
• Chunks & Operations
• Sharding Processes
• Cluster Topology
• Running on localhost
• The Config Database
• Adding the Initial Shards
• Enable Sharding for a Collection
• Working with a Sharded Cluster
• Cardinality & Monotonic Shard Keys
• Shard Key Selection Example
• Process and Machine Layout
• Bulk Inserts and Pre-splitting

Module 7 : Backup and Restore

• Overview of Security
• Security continued: Authentication and Authorization
• SSL and Keyfiles
• Security and Clients
• Intra-cluster Security
• Overview of Backing Up
• Mongodump
• Filesystem Snapshotting
• Backing Up a Sharded Cluster
• Backup Strategies
• Additional Features of MongoDB
• GridFS