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The Best MongoDB Visualization, Reporting and Analytics Tools [Updated 2024]

Read this article to learn about choosing the best MongoDB visualization and reporting tool for your team.

Choosing The Best MongoDB Visualization, Reporting, and Analytics Tool

In today’s data-driven world, businesses are becoming increasingly reliant on accurate and efficient data analysis to drive decision making. As a result, selecting the optimal BI tool or data platform is a critical decision. MongoDB, a popular NoSQL database, is compatible with various BI tools and data platforms, but determining the best suited tool for your organization’s needs is not a trivial task. Continue reading for an in-depth comparison between some of the top MongoDB data visualization tools on the market.

In this article, we will compare 5 of the best BI tools / data platforms for analyzing MongoDB data:

To start, we will give a high level overview of each tool. We will then explore each one’s ease of use, pricing model, data integration flow, customization options, and functionality to help you make an informed decision. We will conclude with a summary on what each tool is best for. The best tool for you depends on your specific needs, budget, and technical skillset.

An Overview of the Best MongoDB Visualization Tools

Screenshot: Sample MongoDB Dashboard with Visualizations in MongoDB Charts
Screenshot: Sample MongoDB Dashboard

MongoDB Dashboards & Data Visualization Tools: MongoDB Charts

MongoDB Charts is a native data visualization tool integrated within MongoDB. It offers a way of natively visualizing MongoDB data, allowing you to create interactive charts and dashboards without needing to move your data using additional tools. Charts can be embedded directly into MongoDB applications or shared with others.

Pros:

  • Already integrated with MongoDB data
  • Drag and drop builder

Cons:

  • Only supports MongoDB data
  • Doesn’t easily support cross-collection joins
Screenshot: Looker for MongoDB Dashboards & Data Visualization
Screenshot: Sample Looker Dashboard (source: https://cloud.google.com/blog/products/data-analytics/new-looker-dashboards)

MongoDB Dashboards & Data Visualization Tools: Looker

Looker is a data visualization tool acquired by Google in 2019. Looker can connect to BigQuery, Redshift, and many other SQL databases. It does require tools to ETL your data from your data store into the platform. Aside from dashboards and visualizations, Looker has many capabilities packed in including embedding, actions, and data modeling.

Pros:

Cons:

  • Requires MongoDB connector and JDBC driver to pull data in
  • Requires learning LookML
GIF of Knowi for MongoDB Dashboards & Data Visualization
GIF of Knowi Dashboard (source: knowi.com)

MongoDB Dashboards & Data Visualization Tools: Knowi

Knowi is an all-in-one data analytics platform that makes analyzing MongoDB data easy. What sets Knowi apart from the others is that you do not need to ETL/ELT your data before querying or visualizing it. With its drag-and-drop builder and Mongo Query Language support, it was designed to fit the needs of both technical and non-technical users. It can connect into and join between SQL and NoSQL databases (including MongoDB and MongoDB Atlas) as well as external applications such as Salesforce, Quickbooks, and HubSpot.

Pros

  • Supports MongoDB and MongoDB Atlas natively without any additional tools/connectors
  • Seamlessly supports NoSQL/SQL data blending
  • Supports MongoDB Query Language natively

Cons

  • Limited ML Capabilities
  • May require query optimization with direct connections
Screenshot: Sample Tableau Dashboard for MongoDB Dashboards & Data Visualization
Screenshot: Sample Tableau Dashboard (source: https://www.tableau.com/data-insights/dashboard-showcase/superstore)

MongoDB Dashboards & Data Visualization Tools: Tableau

Tableau is a data visualization tool known for its aesthetics and simplicity, enabling users to create visually appealing dashboards with minimal effort. It supports a wide range of data sources and uses a drag and drop interface. Tableau was built before data storage systems such as MongoDB were developed, and as a result, best supports structured data.

Pros

  • Drag and drop interface
  • Chart types are visually appealing

Cons

Screenshot: Sample Qlik Dashboard for MongoDB Dashboards & Data Visualization
Screenshot: Sample Qlik Dashboard (Source: https://www.qlik.com/us/dashboard-examples/dashboard-design)

MongoDB Dashboards & Data Visualization Tools: Qlik

Qlik is a data analytics tool known for its intuitive design for data exploration, allowing users to discover insights via a visual builder. It offers both a cloud and an on-premise version. Some features Qlik supports includes reporting, dashboard and visualization building, embedding and more.

Pros

  • Good for data exploration
  • Generally user friendly

Cons

  • Requires MongoDB connector and driver
  • Steep learning curve for complex use cases

MongoDB Reporting & Data Visualization Tools: Pros & Cons Comparison Guide

ToolProsCons
Knowi– Supports MongoDB and MongoDB Atlas natively
– Seamlessly supports NoSQL/SQL data blending
– Supports MongoDB Query Language natively
– Limited ML capabilities
– May require query optimization with direct connections
MongoDB Charts– Already integrated with MongoDB data
– Drag and drop builder
– Only supports MongoDB data
– Doesn’t easily support cross-collection joins
Looker– Lots of visualization options
– Supports git version control
– Requires MongoDB connector and JDBC driver to pull data in
– Requires learning LookML
Tableau– Drag and drop interface
– Chart types are visually appealing
– Requires MongoDB connector and driver
– Requires relational data
Qlik– Good for data exploration
– Generally user friendly
– Requires MongoDB connector and driver
– Steep learning curve for complex use cases

MongoDB Visualization Tools: Key Considerations Compared 

Next, we will dive into the following categories for each tool:

  • Ease of Use
  • Pricing
  • MongoDB Data Integration
  • Customization Options
  • Functionality

Ease of Use Comparison Between MongoDB Visualization Tools:

ToolEase of Use
MongoDB ChartsMongoDB Charts offers a seamless user experience by leveraging the familiarity of the MongoDB ecosystem. It uses a drag-and-drop, no code visual builder.
LookerLooker allows users to build interactive dashboards using LookML, making it suitable for more technical users.
KnowiKnowi has a user-friendly interface with intuitive drag-and-drop functionality, making it accessible to users with varying levels of technical expertise. To build visualizations, you can use the drag-and-drop builder, native Mongo Query Language, and/or Cloud9QL (SQL-like syntax). It also supports using natural language to retrieve a visualization.
TableauTableau’s user interface is known for its simplicity, enabling users to create visually appealing dashboards with minimal effort through a drag-and-drop builder.
QlikQlik’s drag-and-drop interface and guided data exploration features make it relatively easy for users to navigate and analyze MongoDB data. You can explore your data and find connections and patterns without mapping any relationships in advance.

Pricing Comparison Between MongoDB Visualization Tools:

ToolPricing
MongoDB ChartsMongoDB Charts is free to use for self-hosted deployments and available as a paid add-on for MongoDB Atlas. Using MongoDB Charts is free for up to approximately 500,000 chart renders per month, and up to 1GB of data transferred. Beyond the first 1GB, it costs $1 per additional GB of data transferred. *
LookerLooker’s pricing is customizable based on factors such as the number of users, data volumes, and required features. You must speak with a sales representative to obtain pricing information. It does have a more limited free version called Looker Studio.
KnowiKnowi offers flexible pricing options based on the number of users and features needed. There are three different plans on the cloud hosted version as well as an on-premise deployment option. One thing to factor in is that you do not need any ETL/ELT tools with Knowi, cutting your overall data tool costs down.
TableauTableau’s pricing structure is tiered and can be costly for large-scale deployments, especially when considering additional add-ons and data sources.
QlikQlik’s pricing model varies based on deployment type (cloud or on-premises) and user licenses, making it suitable for both small and large organizations.
* From MongoDB’s pricing page: “In addition, any applicable Atlas data transfer fees apply, as per the Data Transfer Fees section of the Atlas Pricing page. Charts is hosted on AWS us-east-1 region, and uses this region to calculate the fees.”

MongoDB Data Integration Comparison Between MongoDB Visualization Tools:

ToolData Integration
MongoDB ChartsBeing an official MongoDB tool, Charts integrates seamlessly with MongoDB Atlas and provides native connectivity.
LookerLooker requires using a MongoDB connector and JDBC driver to pull MongoDB data in.
KnowiKnowi offers native connection to MongoDB and MongoDB Atlas, no ETL/ELT tools or connectors required.
TableauTableau requires a connector to pull data from MongoDB and other data sources.
QlikQlik requires connectors to connect to MongoDB and other data sources.

Customization Option Comparison Between MongoDB Visualization Tools:

ToolCustomization
MongoDB ChartsCharts offers limited customization options, primarily focused on visual styling and branding, making it suitable for basic analytics needs.
LookerLooker offers significant customization options, including custom visualizations and embedding capabilities, enabling users to build tailored experiences.
KnowiKnowi offers extensive customization options through its widget and dashboard builder, enabling users to tailor their visualizations’ and reports’ look and feel.  
TableauTableau provides a wide range of customization options, allowing users to create highly interactive dashboards and reports with advanced features.
QlikQlik offers extensive customization capabilities, empowering users to create personalized dashboards and visualizations to suit their needs.

Functionality Comparison Between MongoDB Visualization Tools:

ToolFunctionality
MongoDB ChartsMongoDB Charts focuses on basic data visualization and exploration, with limited advanced analytical features
LookerLooker offers robust analytics capabilities, including data exploration, cohort analysis, and advanced calculations, allowing users to gain valuable insights from their MongoDB data. It offers embedding, reporting, machine learning capabilities, and more.
KnowiKnowi provides advanced analytics capabilities, including natural language insights, data forecasting, and natural language querying, making it a robust choice for in-depth analysis. With Knowi, you can embed, trigger alerts, send reports, share assets, create machine learning models, and more.
TableauTableau offers a broad range of analytics features, including advanced calculations, statistical modeling, and predictive analytics, making it a comprehensive data analysis tool
QlikQlik provides powerful data discovery features, associative analytics, and augmented intelligence capabilities, making it suitable for complex data analysis scenarios.

Conclusion – What is the Best MongoDB Reporting Tool for Your Team?

When analyzing MongoDB data, the choice of an analytics platform depends on your specific use case. A tool’s ease of use, pricing, data integration process, customization options, and functionality are all important deciding factors. 

Knowi stands out since it does not require ETL/ELT, can join between structured and unstructured data, and supports MQL (Mongo Query Language) natively. MongoDB Charts is a great option since you do not need to transport your data, and it is free to start with usage-based pricing. (See the pricing section above for more details.) Overall, MongoDB Charts is a great choice for basic use cases with a low volume of data and chart loads. Tableau, Qlik, and Looker provide comprehensive analytics capabilities with varying degrees of customization and pricing models, but tend to end up being costly since they require connectors and additional tools to move and translate your data. 

Understanding your organization’s needs, budget, and technical skillset will help you select the most suitable tool to unlock the full potential of your MongoDB data.

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