MongoDB is a distributed Database at its core, so high availability, horizontal scaling, and geographic distribution are built in and easy to use. This training will help you master the leading document-oriented NoSQL database, MongoDB Architecture, CRUD, Schema Design, Data Modelling and Indexing using real-life case studies.

Goal: In this module, you will get an understanding of NoSQL databases, design goals, requirement of NoSQL database/ MongoDB, MongoDB® architecture and introduction to JSON and BSON among others. This module will also cover the installation of MongoDB® and associated tools.
Skills
• Understand NoSQL databases and their advantages
• Learn JSON and BSON
• Install MongoDB
Objectives
After completing this module, you should be able to:
• Differentiate database categories
• Learn MongoDB design goals
• List MongoDB tools
• Describe JSON and BSON
• Install MongoDB on Windows, Linux, MAC OS etc.
• Setup MongoDB environment
Goal: In this module, you will learn Schema Design and Data Modelling in MongoDB®, various data structure and tools available to manage Data Model in MongoDB.
Skills
• Understand Data Modelling Schemas
• Design Data Model relationships and tree structures
• Apply Data Modelling in various real-time contexts
Objectives: After completing this module, you should be able to:
• Understand different concepts of data modeling in MongoDB®
• Understand different types of data model
• Understand the challenges of designing a data model in MongoDB®
• Apply the knowledge of a real-world use case
Goal: In this module, you will get an understanding of CRUD Operations and their functional usage. You will learn how to perform read/write operations with CRUD.
Skills
• Insert, query, update, and delete documents
• Perform distributed read/write operations
• Perform query optimization
Objectives: After completing this module, you should be able to:
• Understand MongoDB’s development and production architecture
• Understand read and write concepts of MongoDB
• Understand how Journaling works
• Use Mongo shell for CRUD operations
• Understand different MongoDB® data types
Goal: In this module, you will learn the Indexing and Aggregation Framework in MongoDB®.
Skills
• Create multiple types of Indexes
• Manage indexes and indexing strategies
• Work with Geospatial indexes
• Use MapReduce framework
Objectives
After completing this module, you should be able to:
• Use various type of indexes in MongoDB®
• Use hint, explain plan of a query
• Work with Geospatial indexes
• Work with Aggregation Pipeline in MongoDB®
• Use MapReduce framework
Goal: In this module you will learn MongoDB® administrative activities such as Health Check, Backup, Recovery, Data Import/Export, Performance tuning, etc.
Skills
• Administer database health, query volume, recovery goals
• Determine performance characteristics
Objectives
After completing this module, you should be able to:
• Take database backup and restore MongoDB®
• Export and import data from/ to a MongoDB® instance
• Check server status and DB status
• Monitor various resource utilization of a mongod instance
• Understand various optimization strategies
• Create capped collection
Criteria MongoDB Cassandra
Data Model Document Bigtable like
Database scalability Read Write
Querying of data Multi-indexed Using Key or Scan

MongoDB is considered to be the best NoSQL database because of its following features:

  • Document-oriented (DO)
  • High performance (HP)
  • High availability (HA)
  • Easy scalability
  • Rich query language

MongoDB does not use conventional locking with reduction as it is planned to be light, high-speed, and knowable in its presentation. It can be considered as parallel to the MySQL MyISAM auto entrust sculpt. With the simplest business sustain, performance is enhanced, particularly in a structure with numerous servers.

MongoDB scrap stands on a collection. So, an album of all substances is kept in a lump or mass. Only when there is an additional time slot, there will be more than a few slice data achievement choices, but when there is more than one lump, data gets extended to a lot of slices and it can be extended to 64 MB.

Although MongoDB, Couchbase and Couchbase DB are common in many ways, still they are different in the case of necessities for the execution of the model, crossing points, storage, duplications, etc.

During the sequencing of the names of the database and the collection, the namespace is used.

Yes, it is deleted. Hence, it is better to eliminate the attribute and then save the object again.

Once the functions are done, the old files are converted to backup files and moved to the moveChunk directory at the time of balancing the slices.

When an index is too huge to fit into RAM, then MongoDB reads the index, which is faster than reading RAM because the indexes easily fit into RAM if the server has got RAM for indexes, along with the remaining set.

MongoDB uses the reader–writer locks, allowing simultaneous readers to access any supply like a database or a collection but always offering private access to single writes.

Goal: In this module, you will understand the setup and configuration of MongoDB® High Availability, Disaster Recovery, and Load Balancing.
Skills
• Create, deploy, and manage Replica sets
• Create and administer Sharded clusters
• Perform Data Partitioning with chunks
Objectives
After completing this module, you should be able to:
• Understand the concepts of replica set
• Understand the concept of sharing in MongoDB®
• Create a production like Sharded cluster
Goal: In this module, you will learn security related with MongoDB, Integration with various tools and technology. Also, you will learn to integrate it with various reporting and Analytical tools like Pentaho, Jaspersoft etc.
Skills
• Setup authentication and encryption
• Integrate MongoDB with various tools and applications
Objectives
After completing this module, you should be able to:
• Know security concepts in MongoDB®
• Understand how Authentication and Authorization works
• Integrate MongoDB® with Java
• Integrate MongoDB® with Hadoop, Hive, & Pentaho
• Integrate MongoDB® with Jaspersoft & Robomongo
Goal: In this module, you will learn MongoDB® tools to develop and deploy your applications. This module will also help you understand the multiple package components and advance concepts related to MongoDB integration, Hadoop and MongoDB integration.
Skills
• Perform MongoDB packaging
• Setup limits and thresholds
• Integrate with R
Goal: In this module, you will learn about various cloud products offered by MongoDB and how they can be used to host or manage your MongoDB deployments.
Skills
• Know MongoDB Cloud products
• Use Cloud products in MongoDB deployments
Objectives
After completing this module, you should be able to:
• Understand MongoDB Stitch
• Learn MongoDB Atlas
• Explore MongoDB Cloud Manager
• Setup MongoDB Ops Manager
Goal: In this module, you will learn some of the common real-time scenarios you might find in production and how they can be fixed, once identified.
Skills
• Troubleshoot slow queries
• Diagnose connectivity problems
Objectives
After completing this module, you should be able to:
• Understand diagnostic tools
• Learn common production issues
• Learn fixes and solutions

Mongo DB is not considered as a 32-bit system because for running the 32-bit MongoDB, with the server, information and indexes require 2 GB. That is why it is not used in 32-bit devices.

Write operations are saved in memory while journaling is going on. The on-disk journal files are really dependable for the reason that the journal writes are habitual. Inside dbPath, a journal subdirectory is designed by MongoDB.

The snapshot() method is used to isolate the cursors from intervening with writes. This method negotiates the index and makes sure that each query comes to any article only once.

It is a document-oriented database that is used for high availability, easy scalability, and high performance. It supports the dynamic schema design.

It is a group of mongo instances that maintains the same dataset. Replica sets provide redundancy and high availability and are the basis for all production deployments.

There are three main features of MongoDB:

  • Automatic scaling
  • High performance
  • High availability

MongoDB provides CRUD operations:

  • Create
  • Read
  • Update
  • Delete

In MongoDB, sharding means to store data on multiple machines.

In MongoDB, aggregations are operations that process data records and return computed results.

We can create a collection in MongoDB using the following syntax:

db.createCollection(name,options)

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