Getting the Most from DocumentDB
It's been common, if inconsistently applied, knowledge for many years that relational databases are a less-than-ideal fit for some types of software problems. Indeed, entire categories of software development tooling, such as object-relational mappers (ORMs), exists to bridge the gap between highly normalized relational data and in-memory, object-oriented representations. In practice, ORMs can create as much complexity as they alleviate, so developers began looking at the relational database itself as ripe for potential disruption.
Thus came the rise of NoSQL and databases that eschew the traditional rows/columns/tables/foreign keys metaphor for other choices like JSON document stores, graph databases that represent data and relationships as nodes with connecting edges, key/value stores that act as a glorified hashtable, and others. The wide range of options meant you could choose the right tool for your particular needs, instead of trying to squeeze a relational database square peg into your application's round hole. Solutions like MongoDB, Cassandra, Redis, and Neo4j rose to prominence and became de facto industry standards for developers interested in leveraging the power and flexibility NoSQL.
While NoSQL was a boon to software developer productivity, the initial product offerings did little to alleviate the administrative burden of managing your database. Server provisioning, backups, data security at-rest and in-transit... all these challenges (and many more) remained as developers adopted NoSQL in greater numbers. Fortunately for them and all of us, the rise of the cloud and managed database service offerings like Azure DocumentDB brought us the best of both worlds: fast, flexible, infinitely-scalable NoSQL with most of the administrative headaches assumed by a dedicated team of experts from Microsoft. You focus on your data and your application, and rely on a 99.99% SLA for the rest!
In this "Introduction to Azure DocumentDB" course, you’ll learn how to use Azure DocumentDB (DocDB) in your applications. You'll create DocDB accounts, databases, and collections. You'll perform ad-hoc and application-based queries, and see how features like stored procedures and MongoDB protocol support can help you. You'll also learn about ideal DocDB use cases and the pricing model. By the end of this course, you’ll have a solid foundation to continue exploring NoSQL and DocumentDB.
An Introduction to Azure DocumentDB: What You'll Learn
|Lecture||What you'll learn|
|Intro||What to expect from this course|
|DocumentDB Overview||A high-level overview of the DocumentDB feature set|
|Overview of Managing DocumentDB||A discussion of DocumentDB features for managing resources, data, scalability, configuration, and so on|
|Creating an Account||Creating a top-level DocDB account in the Azure portal|
|Creating a Collection||Creating and configuring a DocDB collection in the Azure portal|
|Importing Data||Discussion and demonstration of moving data into a DocDB collection|
|Overview of Developing with DocumentDB||A discussion of DocumentDB features from a development point of view|
|SQL Queries||How to author queries in the Azure portal|
|Programming with DocumentDB||Reading and writing data in code, using the .NET SDK|
|Stored Procedures||Authoring DocDB stored procedures and executing them using the DocDB REST API|
|MongoDB Protocol Support||Configuring and using DocDB's MongoDB protocol support|
|Use Cases||A brief discussion of scenarios well-suited for DocDB use|
|Pricing||A review of the DocDB pricing model, and discussion of cost estimation and Total Cost of Ownership|
|Ecosystem Integration||A short review of DocDB integration with other Azure services|
|Summary||Course wrap up|
If you have thoughts or suggestions for this course, please contact Cloud Academy at email@example.com.
Microsoft Power BI is a customizable data visualization solution that integrates with a wide variety of cloud and on-premises data sources. Power BI has native support for integrating with DocumentDB, enabling you to shape and transform JSON data and quickly build charts, graphs and reports to share with other Power BI users. Azure Data Factory is a data ingestion, transformation and movement service in the cloud. Data Factory has native support for DocumentDB, as both a source or sink of data in any defined processing pipeline.
Azure Stream Analytics is a real-time stream processing and analytics service. It's typically used in high-scale application scenarios, like, for example, IoT data ingestion. Steam Analytics supports Azure DocumentDB as an output destination for streamed, transformed and aggregated event data. Azure Logic Apps is a workflow application service built on top of the Azure App Service infrastructure. It supports a declarative definition of business and operational logic and can stitch together data from dozens of public online services and internal Azure services, including DocumentDB. Azure Search is a managed cloud-scale search service for building fast indexes across multiple, disparate data sources, including JSON documents stored in Azure DocumentDB.
Finally, Azure Functions are a server-less cloud compute service for authoring and executing code, written in C#, Node.js, F# and other languages, directly in the browser. Functions can be triggered on-demand, or from several possible automated sources, like timers, HTTP requests, slack messages or more. Parameter bindings can also be used to directly integrate function inputs and outputs with Azure data like queue messages, binary blob data, and DocumentDB JSON documents.
About the Author
Josh Lane is a Microsoft Azure MVP and Azure Trainer and Researcher at Cloud Academy. He’s spent almost twenty years architecting and building enterprise software for companies around the world, in industries as diverse as financial services, insurance, energy, education, and telecom. He loves the challenges that come with designing, building, and running software at scale. Away from the keyboard you'll find him crashing his mountain bike, drumming quasi-rythmically, spending time outdoors with his wife and daughters, or drinking good beer with good friends.