Try this live MongoDB analytics example

You can use this MongoDB example instance below to see how to query MongoDB with Knowi. Hit the Show Me button to give it a try. You can also change the settings and customize the query.

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Business intelligence for MongoDB

A lot of BI tools offer stunning dashboards and visualizations but require your data to be confined to a strict structure and schema-making them useless for NoSQL data sources like MongoDB.

Knowi is different. Knowi was built from the ground up with the aim of providing a unified business intelligence solution for unstructured data like MongoDB.

Embedded analytics

Knowi can be embedded right into your application using an iframe or javascript. That means you can white-labeled Knowi as the analytics solution inside your product and sell its features as part of your GTM.

With powerful dashboards and reporting, users can get real-time analytics that will provide tangible value to your product offering. No ETL required. No additional hardware needed.

4 steps to connect your MongoDB data to Knowi

  1. Click "Add Connection"
  2. Select MongoDB from the Datasources
  3. Insert your Mongo connection credentials.
  4. Hit "Connect"

And that's it. You're now natively connected to your MongoDB data can now be used to create dashboards, visualizations, ad hoc reports, and more.

How is Knowi different than the alternatives?

The three biggest options companies look at for MongoDB analytics are MongoDB Charts, using the Mongo BI Connector to get their data into one of the old guard BI tools like Tableau, or using ETL/ELT processes to move all their data into a relational data warehouse.

BI Connector

A lot of business intelligence tools like Tableau use the Mongo BI Connector. The problem here is that the BI Connector works by putting a SQL layer on top of Mongo, forcing your unstructured data into a relational structure, invalidating the entire point of using a NoSQL database. It's a classic square peg in a round hole problem. This solution can also add additional expenses through enterprise licensing and driver purchasing.

MongoDB Charts

MongoDB Charts is great if you have a super simple use case. If your company is 100% using MongoDB data and it will always be that way, you don't need advanced integration or embedding, and it does not need to scale. If you need to level up your real-time analytics beyond that, that's where Knowi comes in.

ETL + Data Warehouse

The third option is to use ETL or ELT to apply a schema to all of your data and run regular migrations to a data warehouse. This suffers from the same square peg in a round hole problem where you are forcing schema on unstructured data. It also requires the build out of costly ETL pipelines and maintenance of data warehouse infrastructure.
Lastly, going this route often results in versioning issues, since the data your working with in your data warehouse is only as up-to-date as the last ETL process run.

Knowi avoids all of these issues because it was built from the ground up to support unstructured non-relational data. Knowi is built on data virtualization which enables true native integration that other business intelligence platforms simply cannot do.

Need to scale beyond MongoDB Charts?

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01.

Native MongoDB analytics

You simply connect Knowi to MongoDB and start writing queries. Knowi is the only complete BI solution that is fully native to MongoDB and supports nested objects and arrays. No ODBC drivers, no SQL layer in the middle, no pre-defined schemas, no ETL. No mess. No fuss.

02.

Cross database joins

Join MongoDB data with NoSQL, Relational, RDBMS, and APIs on the fly across data centers or multiple cloud providers, eliminating costly ETL processes that move and SQL-ify your MongoDB data.

03.

Search-based analytics

Transform how your company uses its data with the use of Google-search-like capabilities on top of MongoDB. Knowi's search-based analytics will enable your business users to perform ad-hoc analysis in real time.

04.

Embedded Mongo Database Analytics

Build Data-Driven Applications: With just a few clicks, you can securely embed dashboards directly into your applications your business teams are already using. Users can also share MongoDB dashboards or email PDF reports to extend analytics reporting to offline users company wide.

05.

Machine Learning

Combine hindsight and foresight with our machine learning workbench. Integrate machine learning directly into your MongoDB data analysis workflows. With Knowi ML, you can automatically trigger actions based on resulting calculations. You choose to integrate your custom algorithms or tap into our library of open source algorithms.

06.

Triggers, Alerts, and Actions

Automate actions or notifications based on the results of your MongoDB analytics. Easily send notifications with data attached or invoke a webhook to initiate a process in a downstream application.

Check out some of our recent MongoDB blog posts

Want to read more about integrating MongoDB with Knowi? Check out these blog posts where we go much more in depth.

Native Analytics On MongoDB Atlas With Knowi - Tutorial

Introduction In this post, we'll give a hands-on, end-to-end tutorial on using Knowi to connect to data in MongoDB Atlas and build visualizations from it

Read More »

Visualization Solutions for MongoDB

Introduction In the first part of our MongoDB series, we provided an overview of MongoDB Atlas, MongoDB's cloud-based, open-source, NoSQL database offered as a fully

Read More »

Getting Started with MongoDB Atlas: Overview and Tutorial

Introduction Database-as-a-Service In recent years, the database industry has undergone a number of changes, resulting in an increased shift towards a database as a service

Read More »
You're in good company

Features of using Knowi for MongoDB analytics

  • Cloud or On-premise Deployments
  • Plugin MongoDB Queries
  • Schema Discovery & MongoDB Query Generation
  • Join and blend data across various NoSQL and SQL based data sources
  • Direct query execution into your database to drive visualizations, or, store and track seamlessly using our scalable, schema-less, flexible cloud warehouse
  • Share and embed dashboards to internal groups or via a simple URL
  • Works on any device
  • Choose from nearly 30 different visualization options
  • Interactive filters
  • Drilldowns
  • Natural Language Processing
  • Machine Learning
  • Prediction algorithms that auto-selects the best prediction models and forecast for any dataset
  • Plug-in architecture for custom logic & custom prediction algorithms
  • Incremental data pulls and warehouse updates
  • Push API to send real-time data
  • Data Export API
  • Single Sign-On API for embedding inside your portal

Frequently Asked Questions

Questions and answers about MongoDB analytics and reporting

How does Knowi compare to MongoDB Charts?

Knowi tends to be a great fit for the use cases that are too complex for MongoDB Charts. Some use cases work fine sticking to only MongoDB data, but there are also a lot of companies where Mongo is only one part of their data ecosystem. For these companies, being able to support a variety of NoSQL and SQL data sources and join data across databases is crucial. This is where Knowi is often a better fit.
Knowi also works well when companies need to be able to embed their analytics solution in their existing workspace, website, or app.

With Knowi, do I need to move my MongoDB data to a data warehouse?

No. Knowi uses data virtualization to access and run real-time analytics on MongoDB data natively. Many business intelligence platforms require you to use ETL processes to move all of your NoSQL data to a data warehouse and apply schema. With Knowi, none of that is needed. That said, some of our customers who already have data warehouses use Knowi on top of that as the visualization and analytics engine.

Is Knowi + MongoDB a good solution for data science use cases?

As with all applications, it depends on the use case itself. But Knowi's ability to do real-time analytics natively on MongoDB data can certainly power a lot of data science applications. Additionally, with Knowi, you can join across databases; meaning you can join your MongoDB data with data from relational databases. So if you have some unstructured data in MongoDB and some data in a structured SQL database, you can still do analytics.

Lastly, Knowi's ability to easily pull in data from REST APIs can be useful for pulling in data from outside sources for data science projects.

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