About the course
Understand how good design facilitates scale and achieve top performance by gaining insights into indexing strategies.
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.
We need to discuss database schemas. Yes, MongoDB is touted as schema-less but here's where we show that proper design is what allows our collections to scale. Indexing is something everyone talks about, but few understand. We'll explain MongoDB indexing, and index properties because a successful indexing strategy is a key to performance and scaling. Finally, we'll talk about CRUD commands from the MongoDB client and how to write effective queries.
Taking this course will help you understand supported standards and data types in MongoDB, and best practices to design collections to scale and index them. Also, you will learn some basic CRUD commands.
Style and Approach
Through real-world and best-practice data schemas, we show you how design impacts performance and show you how to scale your system. We demonstrate the effective use of MongoDB queries for inserting, updating, deleting, and fetching data.
What You Will Learn
- A collection schema matters in a schema-less system; or, when to stop de-normalizing data relationships
- How a data schema impacts scale
- Introduction to database indexing: concepts and terminology
- How indexes increase operational overhead but improve query performance
- MongoDB Indexes vs. index properties
- Advanced index concepts and indexing best-practices
- Administrative queries; how to delete collections or even entire databases.
- How to apply indexes, or query to see what indexes already exist.
- Perform a quick backup and recovery of your collections
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 MongoDB since 2010 and has been the architect of 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++, Linux
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