Welcome. Before we can understand how to use MongoDB as the database in an application, we need to start with the fundamentals and answer the question, what is a document database? In this video, we're gonna explore two key concepts in MongoDB. What is the document model? And what does it mean to have a flexible schema? We'll start with the document model. In MongoDB, a document represents a single object or entity, such as a user, a product, or a blog post. For example, in this document, there is information about one person. The few fields include name, title, and interest, but we can easily add more like an address. For every document, MongoDB automatically assigns a unique ID field when it's inserted unless one is explicitly provided. This ID field serves as the document's unique identifier and ensures that each individual document can be quickly retrieved, updated, and or deleted. In MongoDB, a document is the smallest unit of data that stores information about an object and its related metadata. A group of documents is contained in a collection. A database is the container for a group of collections. This simple hierarchy makes it easy to organize and manage your data. Now that we've covered the document model, let's talk about one of its most important features. It's flexible schema. A flexible schema means it can handle polymorphic data, data that can take on multiple forms within the same structure. So within a single collection in MongoDB, documents can have varying data types and hierarchical structures. This flexibility is especially useful when working with unstructured or semi structured data that doesn't fit neatly into a predefined schema. For example, let's think about a social media application with a collection for posts. Documents in this collection will have common fields like ID, time stamp, and lengths, which are all relevant for all types of posts. But the documents can also have fields that vary. For instance, a text post will have a content field for the text. A photo post will have a photo URL and caption field for the image, and a video post will have video URL, title, and duration fields. These different document structures can exist side by side in the same collection without requiring complex data models or multiple tables. This flexibility allows you to easily add new features or data types to your application as it evolves. For instance, if the app introduces live stream posts, a new document structure can be added to the same post collection. MongoDB's flexible schema makes it usable for a variety of applications, especially those that deal with diverse and rapidly changing data. This is why you'll find it used everywhere from IoT and mobile apps to gaming and content management. MongoDB's document model is a great fit for generative AI because it handles unstructured data and can store vector embeddings. These embeddings are numerical representations of data that allow for semantic searches, making it easy to build AI driven features using MongoDB. To help you leverage MongoDB's document model and flexible schema, this skill badge will introduce you to the core tools and features for effective database management. You'll gain the skills to start building your own applications. Let's recap what we covered. We explored MongoDB's document model and flexible schema, which allows MongoDB to support diverse and complex data.