MongoDB makes it possible to store and process large sets of data in ways that increase business value. The flexibility of unstructured, schema-less, storage, combined with robust querying and post-processing functionality, make MongoDB a compelling solution for enterprise big data needs.
With this comprehensive 2-in-1 course you master all the techniques for deploying MongoDB across various platforms and environments. You will learn some of the best production practices such as creating a test, dev and prod cluster. Lastly you will learn how to effectively secure your clusters in production. It also shows different MongoDB cluster architectures affect application performance, scalability and reliability.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Learning MongoDB Deployments addresses in-depth, the installation and configuration of various MongoDB deployments (dev, test, prod, demo). Using a best-practice approach, this course familiarizes the user with installation requirements and options while delving into an infrequently-covered topic: MongoDB configuration.
Taking this course will help you master all the techniques for deploying MongoDB across various platforms and environments. You will learn some of the best production practices such as creating a test, dev and prod cluster. Lastly you will learn how to effectively secure your clusters in production.
The second course, MongoDB Tools and Services takes you through real world examples that you can watch and use directly for your application. You will learn the ins and outs through the hacks covered in the course. This is your one stop course to boost your application performance for your audience.
In this video course, we will explore the profiling and performance tools for MongoDB. We will make it even more accessible by moving to MongoDB cloud services, including analytics, automation, and even database-as-a-service. Finally, we will show different MongoDB cluster architectures affect application performance, scalability and reliability.
About the Authors:
Micheal Shallop started programming in 1981 on a Tandy TRS-80 Model 1 and hasn't stopped since. He graduated in 1991 from Oklahoma State University with an Honors degree in Computer Science. In his career, he's coded in many programming languages and has used a variety of databases, relational and otherwise. He was the technical author of a patent awarded in 2011 for his work on real-time data collection, aggregation, and forecasting in a conventional (automotive) business.
He is currently working for givingassistant. org designing and writing a back-end, event-driven, object-oriented, data-agnostic framework utilizing AMQP as the data transport vector and PHP 7.1 as the primary language. He has been programming in PHP for Mongo since 2010 and has been the architect for several systems, mostly back-end frameworks.
Micheal is interested in anything with a programming language behind it. Most recently, he has been experimenting with Arduino programming on the Raspberry Pi, and writing a social media site in Python. He is also technically skilled in RabbitMQ, general database tech, Python, C/C++, and Linux.
Rick Copeland is the Principal Consultant of Arborian Consulting, which provides MongoDB and Python-focused consulting, training, and custom development services. Rick has been using MongoDB since 2009 and Python since 2005, and has spoken at various user groups and conferences on both topics. He is a member of the Python Software Foundation and the Masters of MongoDB.
