MongoDB

MongoDB

Module 1: Introduction to MongoDB

Module 2: Aggregation

Module 3: Backing up and Restoring Data

Module 4: Bulk Operations

Module 5: Collections

Module 6: Configuration

Module 7: CRUD Database Operations

Module 8: Indexes

Module 9: Hashed Indexes

Module 10: MongoDB Java

Module 11: Managing MongoDB

Module 12: Mongo as Shards

Module 13: MongoDB - Configure

Module 14: MongoDB Aggregation (Advanced)

Module 15: MongoDB Aggregation (Advanced)

Module 16: MongoDB Authorization Model

Module 17: Pluggable Storage Engines

Module 18: MongoDB Python

Module 19: Python Driver

Module 20: Querying for Data

Module 21: Projection

Module 22: Replication

Module 23: Update Operators

Syllabus

View Syllabus In Pdf

MongoDB181705143363.pdf

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MongoDB301705143481.pdf

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MongoDB451705143519.pdf

View Syllabus In Pdf

MongoDB601705143550.pdf

Fees

Rs.12000

    Course Highlights

  • MongoDB is well known for its horizontal scaling and load balancing capabilities.
  • Document-Oriented: Data is stored in JSON-like BSON documents.
  • Flexible Schema: MongoDB is a schema-less NoSQL database.
  • Indexing: Efficient indexing capabilities enhance query performance.
  • Replication: Provides high availability through replica sets.
  • Aggregation Framework: Powerful and expressive aggregation pipeline for performing data transformations and analysis within the database itself.
  • Geospatial Indexing: Supports geospatial data and queries enabling the storage and retrieval of location-based information.
  • Automatic Sharding: Enables horizontal partitioning of data across multiple servers facilitating scalable and distributed database architectures.
  • Ad Hoc Queries: Supports complex queries allowing for flexible and dynamic retrieval of data based on various criteria.
  • Document Validation: Allows the definition of data validation rules to enforce data integrity at the database level.
  • Storage Engine Pluggability: Allows for the use of different storage engines to optimize performance and storage characteristics based on specific use cases.
  • Cloud Integration: Supports cloud-based deployments and is integrated with major cloud platforms.
  • Time-To-Live (TTL) Indexes: Enables automatic removal of documents from a collection after a specified period useful for managing data retention policies.
  • Wildcard Indexes: Supports wildcard indexes allowing for more flexible indexing options to optimize query performance.
  • Backup and Restore: Provides tools for backup and restoration of data including point-in-time recovery options.

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