The Complete Guide to MongoDB Charts for Beginners

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Hey there! Congrats on taking your first steps into the world of data visualization with MongoDB Charts.

As a fellow data geek, I‘m excited to dive deep on everything you need to know to become a MongoDB Charts pro.

In this comprehensive guide, we‘ll cover:

  • What Makes MongoDB Charts Special
  • Step-by-Step Setup
  • Navigating the Chart Builder Workspace
  • Chart Types and When to Use Each
  • Designing Beautiful Dashboards
  • Optimizing Your Data for Maximum Value
  • Collaboration and Sharing Features
  • Limitations to Know
  • And much more!

By the end, you‘ll have all the knowledge you need to start visualizing your MongoDB data like a pro. So let‘s get started!

What Makes MongoDB Charts Unique?

Before we dive into the details, it‘s important to understand what sets MongoDB Charts apart from traditional business intelligence tools.

Here are three key advantages:

Native Integration with MongoDB

MongoDB Charts is built directly into MongoDB Atlas and can connect directly to your clusters. This means:

  • No lengthy ETL process required to move data elsewhere
  • Real-time dashboards and visualizations as data changes
  • Leverage existing MongoDB roles and permissions for access control

By natively integrating Charting capabilities directly into MongoDB, it solves major pain points around optimizing data for visualization.

Document Data Model Flexibility

SQL databases require rigid tabular structures. But MongoDB‘s document model gives you the flexibility to store data in whatever schema makes the most sense for your application.

MongoDB Charts is designed to handle flexible schemas with nested objects and arrays much more gracefully than old-school BI tools.

You don‘t have to reshape your data just for the sake of charting. Charts can handle it as-is!

Simplicity and Ease of Use

MongoDB Charts uses an intuitive drag-and-drop interface to let you construct charts visually:

MongoDB Charts drag and drop

With just a few clicks, you can:

  • Select data sources
  • Assign field mappings
  • Apply filters
  • Switch chart types
  • Customize styles and colors

This simplifies what is often a labyrinthine process into something anyone can manage.

These design advantages add up to a uniquely powerful yet easy-to-use charting experience. You get flexibility AND simplicity.

Now let‘s dive into how to get started…

Step-by-Step Guide to Enabling MongoDB Charts

MongoDB Charts is included with MongoDB Atlas, but you need to take a couple steps to enable it:

Step 1) Create an Atlas Account

Head to and create an account if you don‘t already have one.

Step 2) Deploy a Free Atlas Cluster

Follow the prompts to deploy a free shared cluster. Select the cloud provider and region you prefer.

Step 3) Load Sample Data (Optional)

For easy testing, click "Load Sample Dataset" on the cluster overview page to quickly add sample collections.

Step 4) Open the Charts Module

In the left nav bar of Atlas, click on the Charts icon to open the Charts workspace.

Step 5) Create Your First Chart

Charts will provide a brief tutorial for creating your first simple chart against the sample data.

And that‘s all there is to it! Atlas will automatically handle deploying and configuring Charts on your cluster.

Now you‘re ready to start building visualizations against your live databases.

Let‘s explore how to construct charts using the builder interface…

Creating Charts with the MongoDB Charts Builder

The Charts builder provides a clean drag-and-drop workspace for designing your visualizations.

Here‘s an overview of the key components:

Charts builder annotated

A. Data Source Panel – Select the cluster, collection, or CSV data you want to chart.

B. Chart Types Panel – Choose from 10+ visualization types like bars, lines, pies, etc.

C. Fields Panel – Drag and drop fields here to define axes and data bindings.

D. Encoding Panel – Map fields to visual properties like X, Y, color, size.

E. Filter Panel – Filter data before visualizing it.

F. Customization Panel – Adjust visual settings like colors, labels, tooltips.

G. Query Panel – Shape data using aggregations before charting.

H. Preview Panel – Watch your chart update in real time.

With these intuitive panels, you can construct even complex visualizations with just a few clicks and drags. No coding required!

Let‘s walk through building a sample chart step-by-step:

1. Choose Data Source

I‘ll select a restaurants collection containing data on restaurants in various cities.

2. Pick Chart Type

I‘ll use a vertical bar chart to compare ratings across restaurant types.

3. Map Fields to Encodings

I‘ll drag city to the X-axis and rating to the Y-axis.

4. Customize Styles

I‘ll tweak colors, tooltips, and axis labels.

5. Apply Filters

I‘ll filter to just Chinese cuisine restaurants.

6. Preview the Final Chart

My customized vertical bar chart shows average Chinese restaurant ratings by city!

With these six simple steps, I created a polished interactive chart without writing any code.

Next let‘s explore the wide variety of chart types available.

MongoDB Charts Types Explained

MongoDB Charts supports a comprehensive set of visualization types.

Each chart serves a different analytical purpose. Here‘s an overview of the options:

Column Charts

MongoDB Column Charts

Column charts compare values across a limited set of categories. Use them when you have shorter text labels.

Bar Charts

MongoDB Bar Charts

Bar charts also compare values across categories. Use bar charts when category labels are lengthy.

Line Charts

MongoDB Line Charts

Great for visualizing trends and changes over time. Lines connect sequential data points.

Area Charts

MongoDB Area Charts

Area charts fill the space below a line, emphasizing volume and degree of change.

Pie Charts

MongoDB Pie Charts

Displays proportional comparisons across categories. Pie slices represent values.

Donut Charts

MongoDB Donut Charts

Just like pie charts but hollow in the center, allowing for labels.

Scatter Plots

MongoDB Scatter Plots

Plots correlations between two numeric variables. Shows clustering.

Bubble Charts

MongoDB Bubble Charts

Similar to scatter plots but with a third data dimension mapped to bubble size.


MongoDB Heatmaps

Great for spotting trends and patterns in dense datasets. Color coding emphasizes intensity.

Geospatial Charts

MongoDB Geospatial Charts

Plot points onto geographic maps using latitude/longitude coordinates.

Funnel Charts

MongoDB Funnel Charts

Illustrate sequential stages in a process with cascading flows.

Text Charts

Format and display rich text, markdown, links, and images in a chart.

This chart type palette gives you diverse options to explore your data from every angle.

Now let‘s discuss how to combine charts into shareable dashboards.

Creating Beautiful Data Dashboards in MongoDB Charts

While individual charts are useful, often you‘ll want to assemble collections of charts into dashboards to share deeper insights.

Benefits of Dashboards

Well-designed dashboards empower users to:

  • Identify trends across multiple metrics
  • Interact with filters to slice and dice data
  • Explore data visually without coding skills
  • Understand key takeaways at a glance
  • Make faster, more informed decisions

Here are some best practices for creating effective MongoDB Charts dashboards:

Combine Related Charts

Group charts covering connected metrics together into rows or tabs. This makes comparison easy.

For example, sales managers might want to see:

  • Regional revenue
  • Regional profit
  • Regional expenses

Grouped together on one tab.

Pick Coherent Visual Types

Use consistent chart types across your dashboards for clarity. For example, always use line charts for trends over time.

Include Text and Labels

Text charts allow you to annotate your dashboards with explanations, insights, instructions, etc.

Add Interactive Filters

Let users narrow down the data by specific criteria to surface insights.

Curate Select Key Metrics

Dashboards shouldn‘t try to visualize every possible metric simultaneously. Pick a focused set of 3-5 key metrics to avoid clutter.

Refine Iteratively

Tweak and adjust until the data story is crystal clear. Enlist stakeholders to give feedback.

Simplify Sharing

Use view-only links and embeds so other users can interact without a MongoDB account.

Applying these dashboard design principles will maximize their value for driving data-informed decisions.

Next let‘s explore collaboration features.

Collaboration Features

MongoDB Charts offers excellent capabilities for cooperating on dashboards:


Annotate charts and dashboards with threaded conversations and commentary.


Get notifications when someone edits or interacts with your charts.

Present Mode

Hide construction panels for clean, distraction-free dashboard viewing.

Granular Permissions

Control edit vs view access on dashboards and charts individually.

Public Sharing

Open up dashboards to anyone with a simple public link.


Embed live read-only dashboard views into webpages.


Download as PDFs or CSV data for offline analysis.

These collaboration tools make it a breeze to discuss and share insights as a team.

Now let‘s talk about best practices for structuring your data for maximum charting value.

Designing Your Data for Peak MongoDB Charts Performance

MongoDB‘s flexible document model makes it easy to evolve your schemas rapidly. But for peak charting value, a bit of upfront model optimization goes a long way.

Here are some tips for structuring your data with MongoDB Charts in mind:

Flatten Nested Structures

MongoDB Charts can only map top-level fields visually. Flatten nested objects when possible.

Standardize Field Names

Use a consistent naming convention like cityPopulation not CityPop for clarity.

Unify Similar Fields

Don‘t mix cost, price, and value. Standardize on one preferred term.

Add Unique Identifiers

Include fields like customerID to enable joins/lookups during analysis.

Model Time Series Cleanly

Store dates, timestamps, and periods consistently to chart trends.

Limit Array Nesting

Deep nested arrays are difficult to chart – keep flattened when possible.

Index Charting Fields

Add indexes on fields used for axes, filters, and tooltips to optimize performance.

While MongoDB affords tremendous schema flexibility, taking the time to intentionally model your data will pay dividends when visualizing it later.

Now let‘s dive into limitations of the free tier.

MongoDB Charts Limits in the Free Tier

The MongoDB Charts free tier has the following constraints:

  • Only shared clusters eligible (not dedicated M0+ tier)
  • Limited to 1 dashboard with 5 charts max
  • Dashboards can‘t be shared/embedded publicly
  • 100k chart render requests per month
  • No ability to cache charts or dashboards

For full functionality, you‘ll need to upgrade to a paid Atlas tier. Some key paid features include:

  • Unlimited dashboards and charts
  • Public dashboard sharing
  • Chart/dashboard caching for performance
  • PDF dashboard exports
  • Custom color palettes
  • Chart streaming to BI tools
  • On-premise charting with MongoDB Server

The $18/month M0 tier unlocks the complete paid capabilities. Overall the free tier still provides tremendous value.

Key Takeaways and Next Steps

Congratulations – you reached the end of our comprehensive MongoDB Charts guide!

Let‘s recap some key learnings:

  • MongoDB Charts integrates visualization natively into Atlas for live dashboards without ETL.

  • The intuitive drag-and-drop builder makes chart creation easy for anyone.

  • Diverse chart types like bars, pies, maps, and heatmaps allow you to explore data from every angle.

  • Beautiful interactive dashboards can be shared and embedded to spread insights across your organization.

  • Some strategic modeling will allow MongoDB Charts to provide maximum value from your data.

  • Paid tiers unlock more powerful features like unlimited charts and public sharing.

I hope these tips give you the knowledge to start visualizing your own MongoDB data like a pro!

Here are some next steps to apply your new skills:

  • Enable Charts in your Atlas account if you haven‘t already

  • Model a collection for charting using the best practices provided

  • Build your first chart against your real data using the step-by-step instructions

  • Create a dashboard to glean hidden insights from your data

  • Share it with colleagues and stakeholders for feedback

Then wash, rinse, and repeat as you hone your data visualization skills.

The journey to data insights is an exciting one. I wish you the very best as you chart it using MongoDB Charts! Let me know if you have any other questions.

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