MongoDB Atlas Charts provides a free built-in data visualization tool for creating interactive charts directly from your MongoDB databases. This beginner‘s guide will walk through the core components of Charts, provide step-by-step configuration instructions, showcase effective chart types for different data shapes, and share tips for customizing eye-catching visualizations.
Introduction to MongoDB Charts
MongoDB Atlas is a popular cloud database service that provides a fully managed document database platform to power modern applications. The integrated Charts module eliminates the need for a separate business intelligence tool to visualize and explore your MongoDB data.
With just a few clicks, you can connect Charts to your database clusters and collections within Atlas. Charts will automatically scan the documents and nested data structures and allow you to start building charts instantly.
Key capabilities include:
- Interactive drag-and-drop chart builder – Easily construct a wide array of chart types with no coding required.
- Live chart previews – See changes in real-time as you configure your charts.
- Dashboard organization – Arrange multiple charts together into shareable dashboards.
- Broad selection of visualization types – Bars, lines, pies, scatter plots, heatmaps and more.
- Simple sharing and embedding – Securely share or embed charts and dashboards.
This guide aims to provide MongoDB developers, analysts, and administrators with hands-on instruction for unlocking the power of Atlas Charts to gain insights from their document databases.
Core Components
Atlas Charts consists of a few key components for creating, organizing, and sharing data visualizations:
Dashboards
Dashboards allow you to group multiple related charts together for a consolidated view across different data collections, trends, or categories.
You can add filters that apply across all charts in a dashboard. This allows viewers to easily change the time range or filter to specific categories to update all visualizations dynamically.
Dashboards can be kept private, shared with other Atlas users, or made public for broader embedding and sharing.
Chart Builder
This is the main workspace where you will create new visualizations or edit existing charts. It provides an intuitive drag-and-drop interface and live preview for constructing charts.
Core features include:
- Select data source – Connect to MongoDB collections or views
- Fields pane – Lists available data fields, arrays, and nested documents
- broad array of chart types – Bar, line, column, pie etc.
- Encode pane – Map fields to axes, legends, colors etc.
- Aggregation pipeline – Preprocess data before visualizing
Variety of Chart Types
Charts supports an expansive range of visualization types including:
- Column and bar charts
- Line and area charts
- Pie, donut and gauge charts
- Scatter plots and heatmaps
- Pivot tables
- Geospatial charts
Each chart excels at capturing specific data shapes and relationships. The next section provides guidance on picking optimal charts.
Step-by-Step Guide
Follow along with these steps to get started building charts from a sample data set:
- Sign up – Create a free Atlas account
- Create cluster – Deploy a free shared cluster
- Enable Charts – Access Charts from the Atlas UI sidebar
- Add sample data – Load in the included sample movies data set
- Open Chart Builder – Select "Build a Chart"
This will launch the Chart Builder tool with the sample movies data set pre-selected as the data source.
Now you can start constructing charts by picking the visualization type and mapping data fields to the required axes, legends, and encoding options.
Top Charts for Common Data Types
The wide selection of charting options available can seem daunting for beginners. Here is a quick guide for picking the right chart depending on the shape of your data:
Categorical Data
- Bar charts – Good for nominal categorical data without inherent order
- Column charts – Useful for ordinal categorical data with logical sequence
- Pie/donut charts – Best for proportional breakdown across categories
Time-Series Data
- Line charts – Ideal for showing trends and trajectories over time
- Area charts – Emphasize cumulative total and magnitudes
- Gauges – Display progress against key targets and KPIs
Distribution Patterns
- Scatter plots – Relationships between paired metric variables
- Heatmaps – Reveal clusters, densities and correlations
Geospatial Data
- Maps – Visualize location-based trends and spatial patterns
The Chart Builder makes it easy to test different options with the live preview feature updating in real-time.
Sample Dashboards
Charts comes pre-configured with a sample dashboard pulling data from a movies dataset. This provides examples of how multiple charts can be arranged together.
The sample includes:
- Rating distribution donut chart
- Top rated movies bar chart
- Avg. rating heatmap
- Box office revenue gauge
- Weekly revenue chart
- Awards nominations over time line chart
You can clone this template and modify the underlying data sources to build custom dashboards tailored to your needs.
Dashboards can also be made public or embedded into web apps using the provided snippets.
Customizing and Optimizing
To move beyond basic charts, explore some of these tips for customization:
- Add descriptive labels for axes, legends, annotations
- Enable tooltips to provide details on hover
- Apply conditional formatting rules to highlight outliers etc.
- Change default colors, fonts, styles
- Add custom images or logos
- Create calculated fields with formulas
- Use aggregations to group, filter, sort etc.
Taking time to properly label charts, indicate units of measure, highlight anomalies, and tell a compelling story makes a world of difference.
Extensibility and Integrations
Charts provides options for integration beyond the Atlas UI including:
- APIs – Embed or automate chart creation programatically
- Export charts in variety of resolutions and formats
- REST endpoints to retrieve chart metadata JSON
Plus out-of-the-box integrations with all other Atlas managed services.
This allows power users to build custom reporting dashboards or analytics workflows leveraging Charts visualizations.
Putting It All Together
With its intuitive drag-and-drop builder interface, automatic data discovery and modeling, and deep Atlas integration, MongoDB Charts provides one of the easiest yet powerful data visualization solutions for modern applications.
This beginner‘s guide introduced the core concepts and components to enable those new to Charts to start creating beautiful, meaningful charts from their document data instantly.
For further customization, be sure to consult the detailed MongoDB Charts Documentation.
Now that you have unlocked visual insights into your critical business data, you can spot trends, identify patterns, and share compelling stories through interactive charts and dashboards.
