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Big Data / Data Science


MongoDB Training is an open source software that falls under the “Document” category. MongoDB helps develop applications faster because it uses tables; stored procedures are no longer required. It offers an advantage to developers because earlier the tables were to be translated to the object model before they could be used in the application. Now, the object model and the stored data have the same structure, similar to the JSON format, called BSON. MongoDB also supports scalability and provides various options to handle data consistency.

 

TARGET AUDIENCE

  • System Administrators
  • Research Professionals
  • Software developers, architects and administrators
  • Database and analytics professionals
  • Project managers and trainers

 

What you will learn

MongoDB is one of the most widely used NoSQL tools in the Big Data domain.  MongoDB Training includes

  • Installation of MongoDB
  • JSON
  • Data Modeling and Schema Design and also the
  • Framework of Data Monitoring, Indexing, and Aggregation.

Java is one of the most popular programming languages for working with MongoDB.

  • Learn how to work with the MongoDB Java Driver,
  • Using MongoDB as a Java Developer
  • Become proficient in creating a table for inserting video using Java programming.

 

Mongo DB Course Objective

  • Mongo DB is very versatile and scalable so.

MongoDB Installation

Data Indexing and Aggregation

  • Installation of Java
  • Setting up MongoDB JDBC Driver
  • Database connecting and understanding the collections and documents
  • Learning about the Java Virtual Machine libraries.

 

Mongo DB Training outcome

  • Techniques to work with a variety of unstructured data like images, videos, log data, and others.
  • Understanding Grids MongoDB file system for storing data.
  • Understanding database security risks
  • MongoDB integration with Java and Robomongo
  • CRUD concepts and its functionalities.

 

Mongo DB Course Content

  • Introduction to MongoDB Challenges of RDBMS
  • RDBMS
  • Types of relational databases
  • NoSQL database, its significance, how NoSQL suits Big Data needs
  • Introduction to MongoDB, MongoDB installation, JSON feature
  • Installing MongoDB
  • Basic MongoDB commands and operations
  • Mongo Chef (Minogue) Installation
  • MongoDB Datatypes.
  • Install MongoDB
  • Install Mongo Chef
  • The need for NoSQL
  • Types of NoSQL databases
  • OLTP
  • OLAP
  • Limitations of RDBMS
  • ACID properties
  • CAP Theorem
  • Base property
  • Learning about JSON/BSON
  • Database collection & document
  • MongoDB uses
  • MongoDB Write Concern – Acknowledged
  • Replica Acknowledged, Unacknowledged.
  • Write a JSON document.
  • Understanding CRUD and its functionality
  • Use Insert query to Create a data entry
  • Use find query to Read data
  • Use update and replace queries to Update
  • Use delete query operations on a DB file.
  • Concepts of data modeling
  • Difference between MongoDB and RDBMS modeling
  • Model tree structure
  • Operational strategies
  • Monitoring and backup.
  • Write a data model tree structure for a family hierarchy.
  • MongoDB® Administration activities such as Health Check, Backup, Recovery, database sharing and profiling
  • Data Import/Export, Performance tuning etc.
  • Use shard key and hashed shard keys
  • Perform backup and recovery of a dummy dataset
  • Import data from a CSV file, Export data to a CSV file.
  • Concepts of data aggregation and types
  • Data indexing concepts, properties and variations.
  • Aggregation using pipeline, sort, skip and limit, create an index on data using a single key, using multiset.
  • Understanding database security risks
  • MongoDB security concept and security approach
  • MongoDB integration with Java.
  • MongoDB integration with Java and Robot mongo.
  • Implementing techniques to work with a variety of unstructured data like images, videos, log data, and others
  • Understanding Grids MongoDB file system for storing data.
  • Work with a variety of unstructured data like images, videos, log data, and others.

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