MongoDB is fantastic...and challenging. This database is popular among developers due to its flexibility, but getting your data out of Mongo and into a BI tool isn’t a plug-and-play cakewalk.
If you’re looking for a tool that can easily convert data from MongoDB into a BI-friendly, tabular format for tools like Power BI, Tableau, and Google Data Studio, you’ll need to rely on a third-party connector. Many of these tools are designed to convert data from MongoDB, JSON, XML, and popular data sources like Salesforce and Google Sheets into a BI-friendly format.
Why use MongoDB?
MongoDB is a NoSQL database, meaning that it stores data as objects in “dictionaries,” rather than in tabular format. To access the data, backend tools like Python or Node.js are typically used to import and transform the data.
Luckily, there's hope. Here’s our list of top options for parsing data from MongoDB:
1. MongoDB BI Connector
MongoDB BI Connector is a connector made directly for querying MongoDB, by MongoDB. It is designed for connecting data from MongoDB directly to BI sources like Power BI, Tableau, and Sisense.
As attractive as this option seems, the connector comes with a major caveat: it can only be implemented with a MongoDB Enterprise Advanced subscription, which means setting up a brand new database on their cloud and paying for lots of extra features. If you are looking to use a pre-existing database, this option is out—but given that it’s MongoDB’s native connector it’s worth mentioning.
2. Apache Drill
Apache Drill is a NoSQL query engine which allows you to directly query existing databases using native SQL. One of its benefits is that it utilizes a columnar engine which provides speed similar to OLAP processing. It is designed to be used to facilitate querying JSON stores but can also be connected to common BI tools using a JDBC or ODBC driver.
EasyMorph is another tool that is designed to integrate with JSON-based data sources. With EasyMorph you can set up basic ETL processes and send data between databases.
With EasyMorph, you can use a visual interface to create even the most complex logic and workflows. While that may be attractive to non-technical users, it’s likely to chafe for data analysts that prefer a SQL-first approach. Basic packages start at $69.99 / mo.
Another tool worth mentioning is Python. Python is a tried-and-true method for setting up ETL processes between MongoDB and SQL databases for programmers who are familiar with the language.
It takes a lot of technical expertise, but if you already know Python and have a decent amount of time to spare, there are libraries that are designed for connecting to MongoDB. Because the time investment is so high, this method is recommended only if you are looking to deeply customize a specific ETL process, such as adding dynamic functions to API parameters.
Panoply is a fantastic tool that integrates data from a large number of sources including MongoDB. With Panoply, you are up to speed in under 30 seconds from signup to your first data warehouse. It is extremely easy to use and set up, and has a fantastic user interface with clean graphics and design.
Panoply offers a wide range of connections, starting with SQL and NoSQL databases like MySQL, MongoDB, and Postgres, along with API connections to Salesforce, Twitter, Twilio, and Google Ads. It contains an integrated workbench that allows you to query the data warehouse directly from the web app, allows you to quickly set up jobs to schedule data imports, and contains a full admin panel for customizing user permissions. In addition, it also integrates with ETL tools like Stitch and Alteryx out of box.
If you’re looking for a way to analyze your MongoDB data in a BI tool sooner rather than later, Panoply is the way to go. You can start syncing data under 60 seconds and connecting to your BI or analytics tool only takes a minute more. See for yourself with a 14 day free trial.