Learning Path: MongoDB: Backend Programming with MongoDB from Udemy

What's inside

Learning objectives

Understand how to model your data in mongodb for optimal performance
Learn how to analyze, optimize, and scale your mongodb clusters
Get familiar with how mongodb integrates into the larger world of analytics and big data

Learn mongodb’s cloud services
Learn cloud analytics, automation, and database as a service

Understand how to model your data in mongodb for optimal performance
Learn how to analyze, optimize, and scale your mongodb clusters
Get familiar with how mongodb integrates into the larger world of analytics and big data
Learn mongodb’s cloud services
Learn cloud analytics, automation, and database as a service

Syllabus

MongoDB is the most popular NoSQL database in the market today. With support for real-time analytics, it is being used by many big enterprises for efficient and fast processing of data.

This video provides an overview of the entire course.

The viewer is probably familiar with SQL databases such as Postgres or MySQL and is interested in MongoDB. This video will position MongoDB versus relational SQL databases, with a special take on the why of the differences.

One of the biggest differences between MongoDB and SQL databases is the use of BSON documents to store data. This video shows how BSON documents are structured to contain complex data.

Replication is the core method to achieve durability in MongoDB. Developers need to understand why replication is important and the basics of how it works.

MongoDB clusters scale best by sharding partitioning data across many servers. Developers need to know the basics of how this works and how it affects their application design.

To start using MongoDB, you must be able to connect to a database server using the "mongo" command-line shell and perform basic operations.

How can you query the database using the shell?

The student should be able to write simple data to the database as well as query.

The student should be able to use the shell to create and drop various index types.

To start using MongoDB, the student must be able to connect to a database server using the pymongo driver.

Once you have a connection, how can you actually start programatically accessing the database?

The student needs to know how to manage cursors through large data sets in pymongo.

The student should be able to write performant bulk load/update scripts using pymongo.

The student needs to understand how MongoDB uses its basic b-tree indexes for both queries and sorting.

Understand how MongoDB documents give more flexibility to schema design.

Understand the trade-off between linking and embedding.

Understand how to design your schema to allow for scaling via sharding.

Understand how and when to use MongoDB's covered queries, geospatial indexes, and full-text indexes.

MongoDB provides excellent set of tools and services to maximize your application’s performance.

This video provides an overview of the entire title.

The student must understand the basics of MongoDB index types and how MongoDB uses them to accelerate queries.

How can you predict the performance of a query without benchmarking?

You should be able to examine a running system to see its performance characteristics.

You should be able to use the profiler to identify and correct slow queries in a running system.

The student must understand how to read the performance graphs available in MongoDB's cloud manager monitoring.

How can I (easily) configure MongoDB on my own cloud hardware?

How can I configure my database without going through the trouble of getting my own EC2 account?

How can I scale the read bandwidth using MongoDB replica sets?

How can I scale the write bandwidth via sharding, and what are the effects of different shard keys?

What impacts the single-node performance of a MongoDB system, and when does vertical scaling beat horizontal?

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers cloud deployment, which is essential for modern web applications and aligns with current industry practices

Explores integration with Hadoop, which is valuable for fast and efficient big data processing and analytics

Examines profiling and performance tools, which are critical for optimizing MongoDB application performance

Requires familiarity with SQL databases like Postgres or MySQL, which may pose a barrier to entry for some learners

Teaches how to scale write bandwidth via sharding, which is a core concept for managing large datasets

Explores MongoDB's cloud services, including analytics, automation, and Database-as-a-Service, which are valuable for modern data management

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learning Path: MongoDB: Backend Programming with MongoDB with these activities:

Review NoSQL Database Concepts

Show steps

Reinforce your understanding of NoSQL database principles before diving into MongoDB's specifics. This will help you appreciate the design choices and trade-offs made in MongoDB.

Browse courses on NOSQL Databases

Show steps

Read articles comparing NoSQL and SQL databases.
Summarize the key characteristics of NoSQL databases.
Identify different types of NoSQL databases.

Review NoSQL Database Concepts

Show steps

Reinforce your understanding of NoSQL database principles before diving into MongoDB's specifics. This will help you better appreciate MongoDB's design choices and advantages.

Browse courses on NOSQL Databases

Show steps

Read articles comparing NoSQL and SQL databases.
Summarize the key characteristics of NoSQL databases.
Identify different types of NoSQL databases.

Review 'MongoDB: The Definitive Guide'

Show steps

Gain a solid foundation in MongoDB by studying a comprehensive guide. This will supplement the video lectures and provide a valuable reference for practical application.

View MongoDB on Amazon

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Read the chapters on data modeling and indexing.
Experiment with the code examples provided in the book.
Take notes on key concepts and best practices.

11 other activities

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Review 'MongoDB: The Definitive Guide'

Show steps

Deepen your understanding of MongoDB concepts and best practices. This book provides a solid foundation for the course material.

View MongoDB on Amazon

Show steps

Read the chapters on data modeling and indexing.
Experiment with the code examples provided in the book.
Take notes on key concepts and techniques.

Practice MongoDB Querying with Sample Datasets

Show steps

Sharpen your querying skills by working through practical exercises. This will help you become proficient in retrieving and manipulating data in MongoDB.

Show steps

Download sample datasets from MongoDB's website.
Write queries to filter, sort, and aggregate data.
Optimize queries for performance.

Practice MongoDB Querying with Sample Datasets

Show steps

Sharpen your querying skills by working through practical exercises. This will help you become more comfortable with MongoDB's query language.

Show steps

Download sample datasets from MongoDB's website.
Write queries to retrieve specific data from the datasets.
Experiment with different query operators and techniques.

Review 'Hadoop: The Definitive Guide'

Show steps

Gain a solid foundation in Hadoop by studying a comprehensive guide. This will supplement the video lectures and provide a valuable reference for practical application.

View Hadoop: The Definitive Guide: Storage and... on Amazon

Show steps

Read the chapters on data modeling and indexing.
Experiment with the code examples provided in the book.
Take notes on key concepts and best practices.

Create a Blog Post on MongoDB Indexing Strategies

Show steps

Reinforce your understanding of indexing by explaining different indexing strategies in a blog post. This will help you articulate your knowledge and identify any gaps in your understanding.

Browse courses on Indexing

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Research different MongoDB indexing strategies.
Write a clear and concise explanation of each strategy.
Include examples to illustrate how each strategy works.

Document MongoDB Data Models

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Solidify your understanding of data modeling by creating documentation for different MongoDB schemas. This will improve your ability to design efficient and scalable databases.

Browse courses on Data Models

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Choose a real-world application domain.
Design a MongoDB schema for the application.
Document the schema, explaining the design choices.
Present your data model to peers for feedback.

Build a Simple CRUD Application with MongoDB

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Apply your knowledge by building a practical application that interacts with a MongoDB database. This will solidify your understanding of the concepts covered in the course.

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Design the data model for your application.
Implement the CRUD operations (Create, Read, Update, Delete).
Test your application thoroughly.

Build a Simple REST API with MongoDB

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Apply your knowledge by building a practical application that interacts with a MongoDB database. This will give you hands-on experience with the entire development process.

Browse courses on REST API

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Set up a MongoDB database and a REST API framework.
Implement CRUD operations for a specific resource.
Test the API endpoints thoroughly.
Deploy the API to a cloud platform.

Review 'Practical MongoDB'

Show steps

Explore real-world applications of MongoDB and learn how to solve common problems. This book provides practical insights and examples that complement the course material.

View Practical MongoDB on Amazon

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Read the chapters on specific use cases, such as e-commerce or social media.
Analyze the code examples and adapt them to your own projects.
Consider how the concepts apply to your own work.

Contribute to a MongoDB Open Source Project

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Deepen your understanding of MongoDB by contributing to an open-source project. This will expose you to real-world development practices and challenges.

Browse courses on MongoDB

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Identify a MongoDB open-source project on GitHub.
Find an issue to work on or propose a new feature.
Submit a pull request with your changes.
Respond to feedback from the project maintainers.

Contribute to an Open Source MongoDB Project

Show steps

Deepen your understanding of MongoDB by contributing to an open-source project. This will give you hands-on experience working with a real-world codebase and collaborating with other developers.

Show steps

Find an open-source MongoDB project on GitHub.
Identify a bug or feature that you can contribute to.
Submit a pull request with your changes.

Career center

Learners who complete Learning Path: MongoDB: Backend Programming with MongoDB will develop knowledge and skills that may be useful to these careers:

Database Developer

A Database Developer is involved in the design, development, and implementation of databases. This course helps a Database Developer by providing a deep dive into MongoDB's advanced features, including data modeling, complex querying, indexing, and database operations. By learning to optimize MongoDB for performance, a Database Developer also learns to build efficient and scalable database systems. Furthermore, the course includes how to efficiently integrate MongoDB with Hadoop for big data processing, which is very important for the work that a Database Developer does on large projects.

See salaries and explore the career path for Database Developer

Backend Developer

A Backend Developer focuses on the server-side logic and databases that power applications. This course is particularly useful for a Backend Developer who needs to work with NoSQL databases like MongoDB. It covers essential concepts such as data modeling with BSON documents, complex querying, indexing, and database operations, which are crucial for building efficient and scalable backends. The course also covers cloud deployment, which is essential for deploying web applications, and the integration of MongoDB with Hadoop for fast big data processing, all of which are important for a Backend Developer's role.

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Application Developer

An Application Developer designs, codes, tests, and debugs applications. This course helps an Application Developer by offering a solid understanding of how to use MongoDB, a popular NoSQL database. The course provides insights into important concepts such as data modeling for optimal performance, complex querying, indexing, and database operations, which are important skills for an Application Developer. The course also touches on the practical aspects of cloud deployment and integration with Hadoop, all of which are very relevant for an Application Developer who needs to handle scalable applications.

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Data Engineer

A Data Engineer builds and manages the systems that collect, store, and process data. This course helps a Data Engineer by providing the essential knowledge of MongoDB, a popular NoSQL database. The course’s coverage of data modeling, complex querying, indexing, and database operations is essential for building efficient and scalable data pipelines. In particular, the course’s focus on cloud deployment and integration with Hadoop for big data processing is incredibly relevant to the work of a Data Engineer. A Data Engineer should take this course to master working with a crucial aspect of data infrastructure.

See salaries and explore the career path for Data Engineer

Big Data Engineer

A Big Data Engineer is responsible for building and maintaining systems that handle large volumes of data. This course helps a Big Data Engineer by teaching how to integrate MongoDB with Hadoop for fast and efficient big data processing. The course’s focus on analyzing, optimizing, and scaling MongoDB clusters provides the practical skills needed to manage the large-scale data systems that a Big Data Engineer works with. The course’s coverage of cloud deployment also gives the ability to manage these systems in the cloud, which is essential for a Big Data Engineer who needs to work with modern cloud-based data platforms.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course will help a Software Engineer who intends to work with NoSQL databases, such as MongoDB. The course’s coverage of data modeling, complex querying, and indexing, along with database operations, is highly relevant to a Software Engineer whose responsibility is to build scalable applications. The course's focus on cloud deployment and integration with Hadoop will strengthen a Software Engineer's capacity to develop robust systems. This course provides the practical skills necessary to work with performance tools and cloud integration, which are valuable for a Software Engineer.

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Database Administrator

A Database Administrator is responsible for the performance, integrity, and security of a database system. This course helps a Database Administrator in the context of MongoDB databases by covering crucial topics like data modeling for optimal performance, indexing, and query optimization. The course's focus on analyzing, optimizing, and scaling MongoDB clusters, as well as its detailed look at performance tools, equips a Database Administrator with the practical knowledge to manage databases effectively. Moreover, the course teaches how to integrate MongoDB with big data, which is essential for a Database Administrator who works with large-scale data systems.

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Solutions Developer

A Solutions Developer is responsible for designing and developing software solutions. This course will help a Solutions Developer by teaching core aspects of developing with MongoDB databases. The course covers important topics such as data modeling, complex querying, and indexing, all of which are important for building scalable and efficient applications. The course offers practical experience with cloud deployment, which is essential for a Solutions Developer who works on web applications. This will give a Solutions Developer the tools to implement real-world, data-centric applications.

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Cloud Engineer

A Cloud Engineer is responsible for designing, building, and managing cloud infrastructure. This course helps a Cloud Engineer by covering MongoDB's cloud services, including analytics, automation, and Database-as-a-Service. The course’s focus on cloud deployment and scaling MongoDB clusters makes it highly relevant to a Cloud Engineer who works on deploying and managing applications on cloud platforms. The course will allow a Cloud Engineer to leverage MongoDB’s cloud offerings to maximize their application performance by using the included profiling and performance tools.

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Data Architect

A Data Architect designs and oversees the implementation of data management systems. This course will help a Data Architect learn how to use MongoDB effectively. The course provides in depth knowledge of data modeling, complex querying, and indexing, which are crucial for structuring and managing databases efficiently. The material on cloud deployment, integration with Hadoop, and scaling are especially relevant for a Data Architect involved in creating large scalable data systems. A Data Architect who wishes to work with NoSQL databases should take this course as it provides hands-on experience with core features.

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Technical Consultant

A Technical Consultant provides expert advice on technology solutions. This course will help a Technical Consultant by providing detailed knowledge of MongoDB's advanced features, like the included data modeling, complex querying, and indexing techniques. The course teaches the consultant how to analyze, optimize and scale MongoDB clusters, which is essential for providing expert advice on database management. The course will also give the consultant a deep understanding of MongoDB cloud services. The course's practical focus will give a Technical Consultant the hands-on skills that they can then convey to clients.

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Solutions Architect

A Solutions Architect designs and oversees the implementation of technology solutions for businesses. This course may be useful for a Solutions Architect because it provides a deeper understanding of MongoDB, including how to model data for optimal performance, analyze and scale MongoDB clusters, and integrate MongoDB into the larger world of analytics and big data. The course's focus on advanced data modeling, complex querying, and cloud deployment will give a Solutions Architect the ability to design systems using MongoDB in order to maximize their application performance. An understanding of performance and optimization is important for a Solutions Architect.

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Machine Learning Engineer

A Machine Learning Engineer designs and implements machine learning systems. This course may be useful for a Machine Learning Engineer who needs to work with MongoDB to store and manage data used in machine learning models. The course provides a solid understanding of how to use MongoDB effectively, including data modeling, complex querying, and indexing. The course also touches on integrating MongoDB with other data processing systems, which may be useful for building end to end machine learning pipelines. The course teaches how to analyze and optimize data handling with MongoDB, which can be useful for a Machine Learning Engineer.

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Systems Analyst

A Systems Analyst examines computer systems and determines how they can be improved. This course may be useful for a Systems Analyst by teaching how to manage MongoDB databases. The course’s coverage of data modeling, complex querying, and indexing is crucial to a Systems Analyst who needs to evaluate database performance. The course’s focus on analyzing, optimizing, and scaling MongoDB clusters equips a Systems Analyst with hands-on knowledge of improving system performance. Knowledge of MongoDB’s cloud services is highly relevant for a Systems Analyst who works with cloud infrastructure.

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Data Analyst

A Data Analyst examines data to draw meaningful conclusions and insights. This course may be useful to a Data Analyst because it teaches how to work with MongoDB, a database frequently used in data analysis. The course’s coverage of querying, indexing, and database operations can be valuable for a Data Analyst, especially one who needs to work directly with databases. Although the course focuses more on the technical aspects of database management, the skills gained will equip a Data Analyst with the necessary toolkit to better understand how data is structured and accessed.

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Learning Path

MongoDB: Backend Programming with MongoDB

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