Choosing the Best MongoDB Atlas Reporting Tool for Your Team

Table of Contents:

1. Introduction
2. Best MongoDB Atlas Reporting Tools

• MongoDB Charts
• Tableau
• Knowi
• Power BI
• QlikView
• Looker

3. Final Thoughts

Introduction

MongoDB Atlas is a Multi-cloud database service offered by the same team that created MongoDB. It is a fully managed cloud database that handles the complexity of deploying, managing, and troubleshooting your deployments on your favorite cloud service provider (Azure, AWS, and GCP). With MongoDB Atlas, you can easily and quickly have a MongoDB database running. Since it is a NoSQL database that stores unstructured data, choosing a reporting tool to pair with MongoDB Atlas can be difficult. In this article, we have made this easier for you by selecting the best MongoDB Atlas reporting tools and discussing their features.

Best MongoDB Atlas Reporting Tools

MongoDB Charts

MongoDB Charts is a tool for creating visual representations of your MongoDB data. It has built-in tools to help users share and collaborate on visualizations. Charts provides effortless integration with MongoDB Atlas. You can connect MongoDB Charts to your Atlas projects and quickly start visualizing your Atlas cluster data. You need a MongoDB Atlas user account and an Atlas cluster to visualize data using MongoDB Charts. Your Atlas user must have any project role other than the Project Read-Only role. You can easily access MongoDB Charts within Atlas by clicking the "Charts" tab. Atlas will then launch an instance of Charts linked to your project. You can then start building charts to populate your dashboard.

MongoDB Charts Features

MongoDB Charts offers the following features:

1. Integrations

MongoDB Charts only uses MongoDB Atlas as its data source. It offers a seamless integration with MongoDB Atlas views and collections. It is easy to connect MongoDB Charts to your Atlas projects and start visualizing data.

2. Visualizations

MongoDB Charts provides various types of charts for presenting your MongoDB Atlas data. Examples include combo, line, bar, area, grid, circular, text, and geospatial charts. MongoDB Charts provides various options for customizing the display and format of your visualizations. You can apply filtering capabilities on your charts to display a subset of results. You can also bin, sort, and limit data in your charts to highlight specific aspects of your data.

3. Embedding

MongoDB enables dashboard authors to embed charts and dashboards into external applications or share them via links. The embedding can be done using an embedding SDK or by generating an iframe of your chart or dashboard. You can embed charts or dashboards without authentication to allow anyone to view or require authentication through an embedding authentication provider.

4. Natural Language Charts

MongoDB Charts has the Natural Language Charts feature to simplify the process of creating charts. The feature requires you to enter a prompt about the chart you want to see, and an AI model creates the chart. This feature can generate various types of charts, including grouped bars, stacked bar charts, discrete line charts, discrete area charts, tables, and donut charts.

5. Aggregation

MongoDB Charts has a built-in aggregation functionality. This feature allows you to calculate various metrics about your data to understand it more deeply. Examples include standard deviation and mean.

6. Document data handling

MongoDB Charts is good at handling document-based data such as arrays and embedded objects. It uses a nested data structure to structure data according to your application while still retaining powerful visualization capabilities.

Benefits of MongoDB Charts

MongoDB Charts gives its users the following benefits:

1. Effortless integration with MongoDB Atlas

MongoDB Atlas users can easily access MongoDB Charts from within their MongoDB Atlas accounts and start visualizing their data.

2. Customizable visualizations

MongoDB Charts allows users to customize their visualizations by changing features such as axis and color to improve their appearance.

3. Fit for non-technical users

MongoDB Charts provides a drag-and-drop interface to enable users to create visualizations from their MongoDB Atlas data easily. Its Natural Language Charts feature enables non-technical users to generate visualizations by typing questions in natural language.

4. Drill-down capabilities

MongoDB Charts offers drill-down capabilities such as sorting, binning, and filtering to enable users to dig deeper into their charts and extract the finest details.

Limitations of MongoDB Charts

MongoDB Charts has the following limitations:

1. Only integrates with MongoDB

MongoDB Charts can only visualize data stored in MongoDB. If your data is stored in another source, you must move your data into MongoDB Atlas or look for another data visualization tool.

2. Single collection constraint

MongoDB Charts users cannot combine data across different collections and visualize it on a single chart, but they can only visualize data from a single collection. Although it allows users to join collections using a view, they encounter challenges such as the need to refresh data.

Tableau

Tableau is a powerful business intelligence tool that supports integration with MongoDB Atlas via MongoDB's Tableau connector. The connector requires a federated database instance within MongoDB Atlas. The federated database must be sourced from a MongoDB cluster version 5.0 or higher. The connector also requires you to download some files for Tableau to recognize the connector. First, you must download the MongoDB JDBC driver and move it into a specific directory according to your operating system. You must also download the Custom Tableau Connector and move it to a specific directory. To connect Tableau and MongoDB Atlas, log in to MongoDB Atlas and click the "Data Federation" tab. Click the "Connect" button for the instance you want to connect to and then the "Connect using the Atlas SQL Interface" button. Select "Tableau Connector" from the drowndown button and copy the URI in the connector tab. You can then launch Tableau, click the "Connect" button, and select the "MongoDB Atlas by MongoDB" connector. You will be prompted to enter the connection details including the copied URI, database, username, and password for the your MongoDB Atlas federated database. Tableau will then display all your databases and collections in your federated database instance.

Tableau Features

Tableau has the following features for its users:

1. Integrations

Tableau has native connectors to spreadsheets, PDFs, files, SQL databases, cloud data warehouses, and some NoSQL data sources such as MongoDB. However, it lacks connectors to NoSQL databases such as Apache Cassandra. It requires users to rely on ODBC or JDBC connectors to pull data from such sources.

Tableau users can connect to MongoDB Atlas via the MongoDB's Tableau connector. The connector requires you to download the MongoDB JDBC driver and the Custom Tableau Connector and store them in specific directories. You must copy the URI for the connector under the "Data Federation" tab within MongoDB Atlas. You can then find the Tableau Connector option under the "Connect" tab in Tableau. You will be required to provide the connection details such as the copied URI, database, username, and password for your Atlas database. Once connected, you will be able to access the databases and collections in MongoDB Atlas from within Tableau.

2. Visualizations

Tableau has various types of beautiful and powerful visualizations to help users visualize their MongoDB Atlas data. Examples include charts, stories, maps, and dashboards. Tableau visualizations are customizable, hence, you can edit them to improve their appearance.

3. Search-based Analytics

Tableau has a Natural Language Processing feature known as Ask Data to help users analyze their data using a common language. You can ask questions of your MongoDB Atlas data in natural language and get instant answers in the form of charts without the need to master any query language.

4. Cross-database Joins

Tableau has a cross-database join feature that can enable you to join your MongoDB Atlas data with data from other supported sources. It supports inner join, right outer join, left outer join, and full outer join operations. The join operations depend on a common field across the data sources involved.

5. Embedded Analytics

Tableau supports embedded analytics, enabling users to embed their dashboards, visualizations, and analytical capabilities into their external products. You have to generate a simple HTML embed code and use it to embed content into custom web portals, data products, and third-party applications.

6. Alerts/Anomaly Detection

Tableau has an alert feature that enables you to configure conditions in your data that will trigger notifications when met. You can configure the alerts on views and dashboards, but not on storypoints. Tableau can notify users via email, Slack, or within Tableau.

Benefits of Tableau

Tableau users have the following benefits to enjoy:

1. Integrates with MongoDB Atlas

Tableau supports integration with MongoDB Atlas via MongoDB's Tableau connector. The connector requires you to download the MongoDB JDBC driver and the Custom Tableau connector for the integration to work.

2. Beautiful visualizations

Tableau offers beautiful and powerful visualizations through which you can present your MongoDB Atlas data. You can also customize the visualizations to improve their appearance.

3. Fit for non-technical users

Tableau's Natural Language Processing feature (Ask Data) helps non-technical users uncover insights from their data by asking questions in natural language.

Limitations of Tableau

Tableau has the following limitations:

1. Complex integration

The process of integrating Tableau and MongoDB Atlas is complex and requires an individual with strong technical skills. You must make some downloads and create directories for the same.

2. Few integrations

Tableau lacks connectors to some data sources, especially NoSQL databases. You may encounter challenges when you want to join your MongoDB Atlas data with data from such sources.

3. Legacy architecture

Tableau has a legacy architecture that reflects its founding DNA. You must use Tableau Desktop to publish workbooks to the Tableau Server or Cloud. Tableau is a good tool for exploratory data analysis, but it requires your data to be in a structured format and ready for analysis.

Knowi

Knowi is a complete business intelligence platform that supports native integration with MongoDB Atlas without installing any drivers. It is fully native to MongoDB Atlas with support for arrays and nested objects. There are no ODBC drivers, pre-defined schemas, SQL layer in the middle, or ETL steps. With Knowi, you can easily connect to your MongoDB Atlas instance to discover collections without installing any drivers or moving your Atlas data into a relational structure. You can use Knowi's demo MongoDB Atlas instance or edit the settings to connect your own instance and quickly start visualizing your MongoDB Atlas data. Once connected, you can join your data with other MongoDB collections, relational data, NoSQL, or REST API sources and start building dashboards. You can also leverage Knowi's machine learning features for prescriptive and predictive analytics to build powerful data applications that can transform your business.

Knowi Features

Knowi has the following key features for its users:

1. Data-as-a-Service

Knowi has a data-as-a-service feature that enables it to work with any data, anywhere. It supports native integration with a wide range of SQL, NoSQL, files, and REST APIs, enabling users to access their data instantly. Knowi also enables users to source data from multiple sources by blending data from structured and unstructured sources.

Knowi supports native integration with MongoDB Atlas without installing any drivers or using ETL steps to move your data into a relational structure. You just connect to MongoDB Atlas and start writing queries. You can use their demo MongoDB Atlas instance or edit the settings to connect to your own instance.

2. Multi-Source Joins

Knowi supports multi-data source joins, allowing you to join your MongoDB Atlas data with NoSQL, relational, and APIs on the fly without depending on costly ETL steps that move and force your MongoDB Atlas data back into a relational structure. Knowi supports inner join, right outer join, left outer join, full outer join, and loop join operations. The data sources involved must have a common field.

3. Search-based Analytics

Knowi has a search-based analytics feature powered by Natural Language Processing for self-service reporting. It provides Google search-like Natural Language capabilities on top of MongoDB Atlas, enabling you to ask data questions within Knowi in plain English and get instant answers in the form of visualizations.

Users can embed this feature into their applications of choice to ask questions about their MongoDB Atlas data directly from these applications and get immediate responses.

Knowi has also added this feature to Slack and Microsoft Teams, allowing users to ask questions about their data directly from these applications and get instant answers.

4. Embedded Analytics

Knowi supports embedded analytics, enabling you to securely embed dashboards into your applications with just a few clicks. You can also share MongoDB Atlas dashboards or email PDF reports to facilitate analytics reporting among offline users.

You can choose simple URL-based embedding, secure URL embedding with encrypted request payload, or Single SignOn (SSO) API embedding that enables token exchange from your system users to map to Knowi with user rights and permissions.

5. Visualizations

Knowi has a data presentation layer with 40+ different visualizations that you can use to present your MongoDB Atlas data. You can customize the visualizations to improve their appearance. Knowi offers filters and drill-down capabilities to help users dig deeper into their data and extract fine-grained details. Users with CSS/JavaScript skills can create custom visualizations to meet their specific needs.

6. Machine Learning

Knowi's machine learning workbench enables you to integrate machine learning directly into your MongoDB Atlas analytics workflows and trigger actions based on the resulting calculations. You can combine hindsight and foresight and perform prescriptive and descriptive analytics on your data. You can tap into Knowi's built-in library of machine learning algorithms or integrate your custom algorithms.

7. Alerts/Anomaly Detection

Knowi has an alert feature that can help you automate actions or notifications based on your MongoDB Atlas analytics results. You can use it to monitor changes to your business data and send notifications via Slack, WebHook, or email or configure a webhook to initiate a process in a downstream application.

Benefits of Knowi

Knowi offers the following benefits for its users:

1. Seamless integration with MongoDB Atlas

Knowi offers seamless integration with MongoDB Atlas without requiring users to install any drivers or depend on costly ETL steps to move their data into a relational structure.

2. Supports multi-source joins

Knowi has a multi-data source join feature to help users create the largest possible dataset by combining their MongoDB Atlas data with NoSQL, relational, and APIs on the fly, without using complex ETL steps to move their data.

3. Fit for non-technical users

Knowi has a search-based analytics feature powered by Natural Language Processing to help non-technical users uncover insights from their data using plain English.

4. Customizable visualizations

Knowi enables users to customize their visualizations to get the desired appearance. They can also create custom visualizations using CSS/JavaScript to meet their specific needs.

Limitations of Knowi

Knowi has the following limitations:

1. Not open source

Knowi is a commercial tool.

2. Out-of-the-box visualizations are not the "prettiest"

Knowi doesn't have the "prettiest" out-of-the-box visualizations. However, users with CSS/JavaScript skills can customize them to get the desired look and feel.

3. Sophisticated user interface

Knowi has an intuitive user interface for business users. However, its user interface for data engineers is a bit complex and it may take some time to get used to.

Power BI

Power BI is a business intelligence tool created by Microsoft and it has a MongoDB Atlas connector. The connector requires an Atlas cluster running MongoDB 5.0 or higher. You must also enable Atlas SQL within MongoDB Atlas and copy a connection string (URL) to be used for the connection. The MongoDB Atlas connector for Power BI requires you to download and install the MongoDB ODBC driver. The latest versions of Power BI have the MongoDB Atlas connector for Power BI included, but if your version doesn't have it, you must download and install it. To connect to MongoDB Atlas, launch Power BI and click the "Get Data" tab. Find the "MongoDB Atlas SQL connector" and click "Connect". You will be prompted to paste the MongoDB URI, which is the connection string that you copied from Atlas, and the database name. You can enter an SQL query in the "Native query" field and Power BI will use it as a direct source of data instead of importing data into Power BI. Enter your MongoDB Atlas username and password and click the "Connect" button.

Power BI Features

Power BI offers the following features:

1. Integrations

Power BI has many software-as-a-service connectors to enable users to connect to apps, cloud data warehouses, and databases.

The latest versions of Power BI have a MongoDB Atlas connector included. You can also download and install the connector if your Power BI version doesn't have it. The connector requires you to download the MongoDB ODBC connector. You must also enable Atlas SQL and copy a connection string for your cluster within MongoDB Atlas. The MongoDB Atlas SQL connector can be found under the "Get Data" tab in Power BI. It will require you to provide connection details such as the connection string copied from Atlas, the database name, and your MongoDB Atlas username and password. The connector supports Direct query to enable you to run direct queries against MongoDB Atlas instead of importing your data into Power BI.

2. Visualizations

Power BI has various visualizations that you can use to build reports and dashboards. Examples include decomposition trees, doughnut charts, and waterfall charts. You can also download additional charts from Microsoft AppSource. You can also create custom visualizations and share them with other Power BI users.

3. Ask Questions of Your Data

Power BI has a Natural Language Processing feature called Q&A that you can use to uncover insights from your MongoDB Atlas data using natural language. You can type your questions in natural language and Power BI will return answers in the form of visualizations. You can specify the desired visualization type in your question or let Q&A pick the best visualization for your depending on your data.

4. Alerts/Anomaly Detection

Power BI enables users to configure alerts and be notified when their data changes beyond certain limits. You can configure the frequency with which Power BI will check your data for the alert condition. Power BI can notify you within its notification center or via email.

5. Embedded Analytics

Power BI supports embedded analytics, enabling you to embed items like reports, tiles, and dashboards on websites and web applications. It also lets you embed content in other Microsoft services such as Excel, Teams, PowerPoint, Dynamics 365, and others. You can customize the embedded content and brand it as your own.

Benefits of Power BI

Power BI users enjoy the following benefits:

1. Integrates with MongoDB Atlas

Power BI supports integration with MongoDB Atlas, allowing users to analyze and visualize their data.

2. Customizable visualizations

Power BI visualizations can be customized to improve their look and feel. Users can also create custom visualizations to meet their unique needs.

3. Fit for non-technical users

Power BI has Natural Language Processing capabilities, enabling non-technical users to ask their questions in natural language and get back immediate answers in the form of charts.

Limitations of Power BI

Power BI users encounter the following challenges:

1. Complex integration

Integrating Power BI and MongoDB Atlas is a complex process. One has to download the MongoDB ODBC connector and probably the MongoDB Atlas connector for Power BI. This can be a challenge for individuals without technical skills.

2. Limited integrations

Power BI doesn't support integration with all data sources. You may encounter challenges when you want to join your MongoDB Atlas data with data from unsupported sources.

3. Bulky user interface

Power BI has a bulky user interface. Users may find it difficult to navigate from one component to another.

QlikView

QlikView is a BI platform that supports integration with MongoDB Atlas through the MongoDB Connector for BI. Thus, you must download the connector from MongoDB's official website and install it for the integration to work. The BI connector gives users SQL-based access to their MongoDB Atlas databases. However, the BI connector for Atlas is only available for M10 and larger clusters. You can connect to MongoDB Charts from MongoDB Atlas by clicking the "Connect" button for your cluster. Select "Standard Connection" and click "Choose a connection method." Next, choose "Connect Your Business Intelligence Tool" and use the provided information to connect to QlikView.

QlikView Features

QlikView has the following key features:

1. Integrations

QlikView users can depend on the hundreds of connectors provided by Qlik in its connector factory to connect to various healthcare, finance, CRM, and other applications. It supports native integration with NoSQL data sources such as MongoDB Atlas, ElasticSearch, and Apache Cassandra and cloud data warehouses such as Amazon Redshift and Google BigQuery. The Qlik team boasts of its ability to rapidly build new connectors.

QlikView users can use the MongoDB Connector for BI to connect to MongoDB Atlas. The connector can be downloaded from MongoDB's official website. You can connect to QlikView from MongoDB Atlas by clicking the "Connect" tab for your cluster. You can then follow the on-screen prompts and use the provided information to connect to QlikView.

2. Visualizations

QlikView gives users access to visualizations such as gauges, bar charts, pie charts, tables, and treemaps to present their MongoDB Atlas data. The visualizations are customizable, hence, you can edit them after adding them to your sheet. You can interact with the visualizations by selecting, searching, zooming, and drilling down to find specific answers.

3. Search and conversational analytics

QlikView depends on Qlik's search and analytics feature to enable users to extract insights from their data using natural language. Qlik has an AI assistant powered by Natural Language Processing capable of understanding 10 different languages.

4. Embedded Analytics

QlikView users can use Qlik's no-code and pro-code options provided by Qlik to embed their dashboards and analytical capabilities into their external products and cloud applications such as Salesforce and JIRA.

5. Machine Learning

QlikView users can use Qlik's AutoML feature to build machine learning models from their MongoDB Atlas data. You can uncover patterns in your data and use them to make predictions. AutoML offers features such as scoring and ranking to help you select and deploy the best-performing model.

6. Alerts/Anomaly Detection

QlikView enables users to configure alerts and check their data in different applications. You can Configure QlikView to check the data on a scheduled basis or when the application reloads. The alert notifications can be received via email or mobile.

Benefits of QlikView

QlikView users enjoy the following benefits:

1. Integrates with MongoDB Atlas

QlikView supports integration with MongoDB Atlas using the MongoDB Connector for BI, enabling users to pull their data for analysis and visualization.

2. Fit for non-technical users

QlikView has Natural Language Processing capabilities, allowing non-technical users to uncover insights from their MongoDB Atlas data using natural language.

3. Customizable visualizations

QlikView users can customize their visualizations to get the desired appearance.

4. Fit for advanced analytics

QlikView users can build machine learning models from their data and use them for advanced analytics.

Limitations of QlikView

QlikView users encounter the following challenges:

1. Complex integration

QlikView users must download and install the MongoDB Connector for BI to connect to MongoDB Atlas. This can be complex for users without technical skills.

2. Users cannot extend integrations

QlikView doesn't support integration with all data sources. Users cannot also create custom connectors to integrate with data sources of choice. They have to wait for the Qlik team to build new connectors.

3. Over-dependence on Qlik

QlikView doesn't stand as an independent BI tool. It depends on Qlik to provide features such as machine learning, data source integrations, and others.

Looker

Looker is a cloud-based BI platform that can access MongoDB Atlas via the MongoDB Connector for BI. Your MongoDB Atlas must use an M10+ cluster. You must also create a user account and grant the user read permissions on the target database. Atlas only allows connections with a client whose IP address is in its IP access list, hence, you must get your Looker IP address and add it to the Atlas project's IP access list. You must also enable the MongoDB Connector for BI within MongoDB Atlas by opening the Connect page for the cluster within MongoDB Atlas and taking note of the hostname, port, and user. This information will be required when creating the connection in Looker. The connection can then be done from Looker. Open the "Admin" section, choose "Connections" and click "Add Connection". Select "MongoBI" from the Dialect drown-down button. The connector will prompt you to provide the connection details and click a "Connect" button.

Looker Features

Looker has the following features for its users:

1. Integrations

Looker supports integration with SQL databases, cloud data warehouses, and NoSQL data sources such as MongoDB Atlas. Looker lacks connectors to most NoSQL data sources, requiring users to put the data in a relational format to query against.

Looker users can access their MongoDB Atlas data via the MongoDB Connector for BI. The connector requires you to add your Looker Server to the Atlas IP access list. You must also take note of the hostname, port, and user for your Atlas cluster. The connector requires you to enable SSL encryption between Looker and MongoDB Server. The connection can be done from Looker's Admin section by creating a new connection. You have to choose "MongoBI" from the Dialect drown-down menu and provide the Atlas cluster information.

2. Visualizations

Looker comes with various visualizations that you can use to visualize your MongoDB Atlas data. Examples include bar, area, radar, and gauge charts. You can customize the visualizations to improve their look and feel.

3. Analytics

Looker is shipped with Looker Blocks, which are pre-built data models for the common data sources and analytical patterns. With Looker Blocks, you can quickly and easily analyze and visualize your data by reusing the work done by others rather than starting from scratch. You can customize the blocks to get what you need.

4. Embedded Analytics

Looker has an embedded analytics solution called Looker Embedded that lets users embed their visualizations, dashboards, and analytical experiences into the applications of choice. It requires you to generate an iframe and use it to embed content into HTML-formatted portals, web pages, and applications.

5. Alerts/Anomaly Detection

Looker has an alert feature through which it can notify you when your query results meet certain conditions. Looker lets you configure the frequency with which it will be checking your data for the alert condition. You can receive the alert notifications via email or Slack.

Benefits of Looker

Looker users enjoy the following benefits:

1. Integrates with MongoDB Atlas

Looker supports integration with MongoDB Atlas via the MongoDB Connector for BI, allowing users to access their data for analysis and visualization.

2. Customizable visualizations

Looker allows users to customize their visualizations to get the desired look and feel.

3. Multi-Cloud Friendly

Looker is a multi-cloud-friendly BI tool. This gives its users much flexibility when choosing where to deploy it and the underlying databases.

Limitations of Looker

Choosing Looker comes with the following challenges:

1. Complex integration

Integrating Looker and MongoDB Atlas is a complex process. One has to enable the MongoDB Connector for BI, add their Looker application to the Atlas IP access list, and perform other configurations. This can be a challenge to individuals without technical skills.

2. Limited integrations

Looker doesn't have connectors to some data sources, especially NoSQL data sources.

3. It may require a learning curve

Looker requires users to master LookML, its proprietary, markup language, to access some of its features. This may require a learning curve.

4. Limited US customer support

Looker has undergone many changes after its acquisition by Google, including scaling down its US customer support team. Looker has also confused its users regarding its name (Looker Studio).

Final Thoughts

MongoDB Atlas is a fully managed, multi-cloud database service offered by the same team that created MongoDB. With MongoDB Atlas, you can easily and quickly have a MongoDB database running. Since it stores unstructured data, choosing a BI tool to pair with MongoDB Atlas can be challenging.

MongoDB Charts, Tableau, Knowi, Power BI, QlikView, and Looker are some of the best MongoDB Atlas reporting tools for your team.

MongoDB Charts is a tool for creating visualizations from MongoDB data. You can easily connect MongoDB Charts to MongoDB Atlas and start visualizing your data. You only need a MongoDB Atlas user account and an Atlas cluster. The connection can be done from within MongoDB Atlas by clicking the "Charts" tab.

Tableau is a powerful business intelligence tool that supports integration with MongoDB Atlas via MongoDB's Tableau connector. The connector requires a federated database instance within MongoDB Atlas sourced from MongoDB cluster version 5.0 or higher. You must also download the MongoDB JDBC driver and the Custom Tableau connector and store them in specific directories.

Knowi is a unified data analytics solution that offers native integration with MongoDB Atlas without installing any drivers, depending on ETL steps, or having to SQL-ify your data. You can use its demo MongoDB Atlas instance or edit the settings to connect to your own instance. Knowi lets you join your MongoDB Atlas data with NoSQL, relational, and REST APIs on the fly without using complex ETL steps to move your data.

Power BI is a business intelligence tool created by Microsoft with a MongoDB Atlas connector. If your version of Power BI doesn't have the MongoDB Atlas connector included, you can download and install it. The connector requires you to download the MongoDB ODBC driver. It also requires an Atlas cluster running MongoDB 5.0 or higher.

QlikView is a business intelligence tool that supports integration with MongoDB Atlas via the MongoDB Connector for BI. Thus, you must download and install the connector for the integration to work. It requires an M10 or larger Atlas cluster.

Looker is a cloud-based BI platform that supports integration with MongoDB Atlas via MongoDB Connector for BI. You must be running an M10+ MongoDB Atlas cluster. You must create a new Atlas user account and grant the user read permission on the target Atlas database. You must also add your Looker IP address to your Atlas project's IP access list.