MongoDB Atlas

Introduction to MongoDB Atlas

• MongoDB Atlas is a cloud-based database service provided by MongoDB.
• It offers a range of features and tools to simplify database management for developers.
• The course instructor, Cam, will guide us through this unit.

Benefits of MongoDB Atlas

• Atlas simplifies database management by handling many tasks automatically.
• Key features and benefits include:
  • High Availability: Atlas provides native MongoDB features like high availability, ensuring your database is always accessible.
  • Scalability: It supports scaling your database as your application grows.
  • Analytics Nodes: You can use analytics nodes for data analysis.
  • Data Visualizations: Atlas offers tools for visualizing your data.
  • Maintenance: It handles routine maintenance tasks.
  • Automatic Upgrades: Atlas ensures that your database remains up-to-date with automatic upgrades, minimizing downtime.

MongoDB Atlas Features and Services

• This unit will cover some of the most useful features and services provided by MongoDB Atlas.

Atlas Clusters and Data Storage

• MongoDB Atlas uses clusters to store your data.
• The Atlas Data Explorer will be used to load and view sample data.

MongoDB Atlas Overview and Features

Introduction to MongoDB Atlas

• MongoDB Atlas is a comprehensive developer data platform provided by MongoDB.
• It simplifies working with data for a wide range of applications, reducing the need for additional technologies that may complicate development.

MongoDB Atlas Core Components

• Atlas Database: At the core of Atlas is the Atlas database, which is a Database as a Service (DBaaS). This means you don’t need to manage MongoDB yourself; Atlas handles all database management and lifecycle details behind the scenes.

• Replica Sets: In Atlas, database deployments use built-in replication known as a replica set. This ensures data redundancy and availability even in the event of a server failure.

Types of Database Deployments

Serverless instances

“Serverless” is a term used in cloud computing to describe a model where you don’t have to manage the underlying server infrastructure directly. However, it doesn’t mean there are no servers involved; rather, it means that as a developer or organization, you don’t have to worry about provisioning, scaling, or maintaining the servers yourself.

In a traditional server-based application, you would typically need to manage the servers, handle operating system updates, patching, and ensure that your application is highly available and can scale to meet changing demands. This can be complex and time-consuming.

In a serverless model, the cloud provider abstracts away most of the infrastructure management tasks. You focus solely on writing and deploying your code in the form of functions or services. The cloud provider takes care of provisioning servers, managing resources, scaling based on demand, and handling many operational aspects like load balancing and security.

The term “serverless” can be a bit misleading because there are still servers involved, but they are managed entirely by the cloud provider, and you are billed based on the actual usage of resources (e.g., the number of function invocations or the compute time), rather than pre-allocated server instances.

Popular serverless platforms include AWS Lambda (Amazon Web Services), Azure Functions (Microsoft Azure), Google Cloud Functions (Google Cloud), and more. These platforms allow developers to build and deploy applications without worrying about the underlying infrastructure, which can lead to increased development speed and reduced operational overhead.

• Scales on-demand, charging you only for the resources you use.
• Suitable for applications with highly variable or sparse workloads.
• A good choice if you’re unsure about hardware requirements or prefer a serverless architecture.

Clusters (Shared & Dedicated)

A cluster is a group of servers that store your data. Each server in the cluster is called a node. A cluster can have one or more nodes. A cluster can be deployed in a single region or across multiple regions.

• Clusters consist of several MongoDB servers working together.
• Two cluster options: shared and dedicated.
• Shared clusters include a free tier option, ideal for small projects.
• Dedicated clusters offer larger resources and customization options, including low and high traffic configurations, cluster autoscaling, and enhanced security features. Preferred for production workloads.

Cloud Providers and Geographic Regions

• MongoDB Atlas supports various cloud providers, including AWS, Google Cloud, and Microsoft Azure, with availability in over 100 geographic regions.
• You can deploy globally across multiple regions or clouds.
• Atlas allows you to change your scaling tier, region, or cloud provider as your needs evolve without downtime.

Additional Atlas Services

• Operational Insights: Provides insights into database operations and includes point-in-time backups with restore capabilities.

• Online Archive: Useful for later stages of the data lifecycle, enabling data sharing and archiving.

• Command Line Management: You can manage Atlas from the command line using the Atlas CLI or administration API.

Beyond Just a Database as a Service

• MongoDB Atlas offers a suite of products and services to empower developers and build applications faster.

• Services include:

  • Atlas Search: Enables building rich search experiences on top of your data without the need for a separate search engine.

  • Atlas Device Sync: Keeps data synchronized between the cloud and mobile or Edge devices for always-on experiences.

  • Atlas Data Lake: Automatically extracts data from Atlas databases to a fully-managed data lake optimized for analytical queries.

  • Atlas Data Federation: Allows seamless querying, transformation, aggregation, and writing of data across Atlas databases, data lakes, or cloud object storage environments.

  • Atlas Charts: A built-in data visualization tool for creating, sharing, and embedding rich dashboards using your data.

  • Atlas App Services: Includes a data API for reading and writing data in Atlas via standard HTTPS requests, GraphQL API, functions, and triggers to enable event-driven application architectures.

• MongoDB Atlas is designed to empower innovators by leveraging the power of software and data to create, transform, and disrupt industries. It offers a wide range of tools and services to support this mission.

MongoDB Atlas Setup and Data Exploration

In this video, we’ll walk through setting up a MongoDB Atlas account, deploying an Atlas cluster, and using the Data Explorer to load and view sample data.

Account Setup

1. Sign Up: To get started, go to the MongoDB homepage and click the “Try Free” button in the upper right-hand corner. Fill out the signup form to create a new Atlas account. Alternatively, you can use the “Sign up with Google” option if you have a Gmail account.

2. Atlas Dashboard: After signing in, you’ll be directed to your Atlas dashboard. Your first organization will be active in the upper left-hand corner. Organizations are used to group users and teams and grant them access to projects.

3. Projects: Projects are used to define and organize resources like database clusters. You can create separate projects for development, testing, and production environments.

Deploying an Atlas Database

1. Create Database: Click on “Build a database” in the middle of the screen to create your first Atlas database.

2. Deployment Selection: Choose your deployment type. For this example, we’re creating a free tier cluster. Click “Create” to proceed.

3. Configuration Settings:
   • Cloud Provider and Region: Select your cloud provider (e.g., AWS) and region (e.g., US East) based on your preferences and location.
   • Cluster: Choose your cluster type (e.g., M0 for free tier, or upgrade to paid tiers like M2, M5, or M10). Note that backup settings are available for M2 and above clusters.
   • Cluster Name: You can customize the cluster name or leave the default (e.g., “cluster 0”).
4. Create Cluster: Click “Create cluster” to create your Atlas instance.

Security Configuration

1. Administrative User: In the security quickstart page, create an administrative user by specifying a username and password.

2. IP Address Access: MongoDB Atlas blocks access from all IP addresses by default. To work with MongoDB from your local machine, click “Add my current IP address” to add your IP address to the access list. You can also add other IP addresses if needed.

3. Click “Finish” and “Close” after configuring security settings.

Loading Sample Data

1. Go to Databases: Click “Go to databases” to access the database deployment screen.

2. Loading Sample Data: Since the database is empty, click the ellipsis (…) next to “Browse collections” and select “Load sample data set.”

3. Data Loading: Click “Load sample data set” again to initiate the process. This may take a few minutes.

4. Data Explorer: Once the sample data is successfully loaded, click “Browse collections” to open the Atlas Data Explorer, where you can view, filter, and modify your data.

Data Exploration

• In the Data Explorer, you can navigate through databases and collections on the left-hand menu.
• Clicking on a collection’s name allows you to view the documents within that collection.
• Use the filter bar to specify queries and filter documents based on specific criteria.
• Click “Apply” to filter the data accordingly.

Now, you are equipped to work with the Data Explorer in MongoDB Atlas, allowing you to manage and explore your data efficiently.

Recap:

• Set up an Atlas account and deployed an Atlas cluster.
• Created a database user and added your IP address to the access list.
• Used the Data Explorer to load and view sample data.
• Learned how to filter and interact with collections and databases in the Data Explorer.