MongoDB’s BI Connector: The Smart Connector for Business Intelligence

In today’s world, data is being produced and stored all around us. Businesses leverage this data to provide insights into what users and devices are doing. MongoDB is a great way to store your data. From the flexible data model and dynamic schema, it allows for data to be stored in rich, multi-dimensional documents. But, most Business Intelligence tools, such as Tableau, Qlik, and Microsoft Excel, need things in a tabular format. This is where MongoDB’s Connector for BI (BI Connector) shines.

MongoDB BI Connector

The BI Connector allows for the use of MongoDB as a data source for SQL based business intelligence and analytics platforms. These tools allow for the creation of dashboards and data visualization reports on your data. Leveraging them allows you to extract hidden insights in your data. This allows for more insights into how your customers are using your products.

The MongoDB Connector for BI is a tool for your data toolbox which acts as a translation layer between the database and the reporting tool. The BI Connector itself stores no data. It serves as a bridge between your MongoDB data and business intelligence tools.

MongoDB BI Connector Flow

The BI Connector bridges the tooling gap from local, on-premise, or hosted instances of MongoDB. If you are using MongoDB Atlas and are on an M10 or above cluster, there’s an integrated built-in option.

Why Use The BI Connector

Without the BI Connector you often need to perform an Extract, Transform, and Load (ETL) process on your data. Moving it from the “source of truth” in your database to a data lake. With MongoDB and the BI Connector, this costly step can be avoided. Performing analysis on your most current data is possible. In real-time.

There are four components of a business intelligence system. The database itself, the BI Connector, an Open Database Connectivity (ODBC) data source name (DSN), and finally, the business intelligence tool itself. Let’s take a look at how to connect all these pieces.

I’ll be doing this example in Mac OS X, but other systems should be similar. Before I dive in, there are some system requirements you’ll need:

Instructions for loading the dataset used in the video in your Atlas cluster can be found here.


This post was originally published on the MongoDB Blog.

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MongoDB Atlas Data Explorer

At a recent MongoDB .local Chicago event, MongoDB CTO and Co-Founder, Eliot Horowitz made an exciting announcement about the Data Explorer feature of MongoDB Atlas. It is now available for all Atlas cluster sizes, including the free tier.

The easiest way to explore your data

What is the Data Explorer? This powerful feature allows you to query, explore, and take action on your data residing inside MongoDB Atlas (with full CRUD functionality) right from your web browser. Of course, we’ve thought about security; Data Explorer access and whether or not a user can modify documents is tied to her role within the Atlas Project. Actions performed via the Data Explorer are also logged in the Atlas alerting window.

Bringing this feature to the “shared” Atlas cluster sizes — the free M0s, M2s, and M5s — allows for even faster development. You can now perform actions on your data while developing your application, which is where these shared cluster sizes really shine.

Check out this short video to see the Data Explorer in action.


This post was first published on the MongoDB blog.

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