MongoDB analytics that reads the document as it is.
Knowi queries MongoDB with the aggregation framework, keeps nested documents and arrays intact, and joins collections with your SQL databases, other NoSQL stores and REST APIs at query time. Dashboards, alerts and plain-English questions sit on top. Nothing moves to a warehouse first.
What you leave with
- A working dashboard on one of your collections, built on the call.
- A read on which of your nested fields and arrays need no flattening at all.
- A straight answer on whether Atlas Charts already covers you.
Build MongoDB reports and data visualization in real time with true native integration.
Natively integrate to data sources like SQL, NoSQL (MongoDB, Elasticsearch, InfluxDB…), Rest API and cloud data sources
Plugin MongoDB Queries
Schema Discovery & MongoDB Query Generation
40+ visualizations to create dashboards that matter
Direct query execution into your database to drive visualizations, or, store and track seamlessly using our scalable, schema-less, flexible cloud warehouse
Auto-generate intutive dashboards or get instant insights with Knowi’s Private GPT.
Single Sign-On API for embedding inside your portal
Choose between Cloud or On-Prem deployment options
📆 Book your 30 minute demo
MongoDB analytics should read the document as it is, not as a table it was never meant to be.
Most MongoDB reporting starts by undoing the document model.
You chose MongoDB because an order can hold its line items and the schema can change next sprint. Warehouse-first BI tools cannot read that shape, so they rebuild it as rows before they draw a chart.
The SQL translation layer
MongoDB's BI Connector and its successor, the SQL Interface, expose collections as relational schemas so Tableau, Power BI or Excel can send SQL. Every array becomes a child table and every nested field a joined column. The query you tune is not the query MongoDB runs.
ETL into a warehouse
Flatten each collection into tables, load them on a schedule, report on the copy. The pipeline breaks whenever a document gains a field, and the dashboard is only as current as the last run.
Atlas Charts
Native inside Atlas and a fair choice for a single collection. Each chart maps to one collection, so the moment the answer needs a second source, a join or a self-hosted cluster, you are back to routes one and two.
Knowi takes none of the three routes. It runs MongoDB's own query language against your cluster and treats nested documents and arrays as data, not as a problem to solve first.
Where flatten-first MongoDB analytics and native MongoDB analytics part ways
Three rows are the reason to look. The rest is what you keep.
A tool that flattens the document first is optimising for its own storage model. Native MongoDB analytics means nothing about your data model changes in order to report on it. The connector reference lives on the MongoDB analytics product page.
The query is MongoDB's own.
Knowi connects to the cluster, lists your collections with sampled fields, and lets you write the aggregation pipeline you would write in the shell. $match, $unwind, $group, $lookup and $project run inside MongoDB. Map-Reduce covers the jobs that outgrow aggregate.
Cloud9QL, Knowi's SQL-style post-processing layer, runs on the result when you want a date bucket or a calculated field without touching the pipeline. The pipeline stays the source of truth.
Join collections with whatever sits next to the cluster.
MongoDB is rarely the whole estate. Billing is in Postgres, events are in Elasticsearch, the CRM is behind a REST API. Knowi runs each source's native query, then joins the results in one dataset, across 70+ connectors.
No ingestion step and no warehouse. The MongoDB side of the join is still the pipeline you wrote, nested fields included.
MongoDB dashboards, alerts and plain-English questions on the same datasets
Once the pipeline returns a dataset, everything downstream is standard Knowi: real-time dashboards, scheduled reports, alerts. Nothing here needs a second tool.
MongoDB dashboards, live or scheduled
40+ visualization types with drilldowns and filters. Direct-execution widgets fetch from the cluster when opened. Embed a dashboard in your own product with an iframe or JavaScript, with single sign-on, white-labelled.
Triggers on the result, not on a copy
Set a condition on any dataset. When it trips, Knowi sends the notification with the data attached, or calls a webhook so a downstream system acts. Datasets can also be written back to MongoDB.
Questions in plain English
Ask for the collections, fields and filters you want and Knowi generates the MongoDB query, explains an existing one, or flags what is wrong with it. Business users get answers without learning pipeline syntax.
MongoDB analytics in three steps, no pipeline to build first
This is what the demo walks through on your data. We build a working dashboard in the first call.
Pick MongoDB or MongoDB Atlas, enter hosts, database and credentials. Replica sets take multiple hosts. Add the Cloud9Agent for a database inside your network, or tick legacy support for 4.4 and below.
Knowi pulls the collection list with field samples. Drag fields in the visual builder or write MQL in the editor. Choose direct execution, or a scheduled run into the Elastic Store.
Turn the dataset into widgets, join it with a SQL or REST source, set the alert, embed the dashboard. The same dataset feeds the plain-English questions.
Where Atlas Charts is enough, and where Knowi is the wrong fit
A MongoDB BI decision made on a feature grid tends to get remade later. The short version. Longer reads: choosing a MongoDB reporting tool and MongoDB vs SQL.
- Every collection you report on is in Atlas and will stay there.
- No dashboard needs data from a second database or an API.
- You do not need to embed analytics in your own product under your own brand.
- Nobody outside the engineering team needs to ask their own questions.
- Everything is already modelled into a warehouse and MongoDB is a small export. A warehouse-native tool will feel more native.
- You want to buy on a credit card. Knowi is sales-led, priced as a flat annual platform fee.
- You are standardised on Microsoft and Power BI already sits on every desk.
“My team tested various data visualization tools like PowerBI, Domo, Sisense and Knowi emerged as the top choice for its speed in creating useful dashboards and ease of use for non-technical users. While we selected Knowi as a viz tool, I’m also happy we did so from a data engineering perspective. Knowi’s architecture models data thoughtfully, which I’d credit for enabling a collection of elegant features.”
Phil Bryant, VP Business Intelligence, MacroFabTeams running analytics on Knowi
What buyers ask about MongoDB analytics before the call
Does Knowi run MongoDB analytics without ETL or a data warehouse?
Yes. Knowi connects to the cluster and runs MongoDB Query Language directly, including aggregation pipelines, so there is no extract step and no warehouse copy. Widgets can execute against the cluster live, or on a schedule into Knowi's Elastic Store to keep load off production. Teams that already have a warehouse use Knowi on top of it as well.
How does Knowi handle nested documents and arrays in MongoDB analytics?
They arrive as structured fields, not as flattened child tables. You decide in the pipeline whether to $unwind an array or keep it, and nested paths such as address.city or items.sku can be selected in the visual builder or referenced in the editor. Nothing is forced into a relational schema on the way in.
Does Knowi run MongoDB analytics on Atlas and self-hosted clusters?
Both. Pick MongoDB Atlas or standard MongoDB when you create the datasource, then enter hosts, database and credentials. Replica sets take multiple hosts. For a cluster inside your network, the Cloud9Agent runs behind your firewall and connects without exposing the database to the internet. Legacy support covers 4.4 and below.
Can I join MongoDB data with SQL databases and REST APIs?
Yes. Knowi runs the native query on each source, then joins the results in one dataset at query time. That covers MongoDB with PostgreSQL, MySQL, Snowflake, Elasticsearch, REST APIs and the rest of Knowi's 70+ connectors. No pipeline moves the MongoDB data anywhere first, and nested fields on the MongoDB side stay intact.
How does Knowi compare to Atlas Charts for MongoDB analytics?
Atlas Charts is the right answer for a single Atlas collection with no outside data. Each chart maps to one collection and it stays inside Atlas. Knowi is the answer once dashboards need a second source, a cross-database join, a self-hosted cluster, white-label embedding in your own product, or business users asking questions in plain English.
Do I need the MongoDB BI Connector to use Knowi for MongoDB reporting?
No. Knowi does not use the BI Connector or ODBC drivers. It speaks MongoDB's own query language, so there is no SQL translation layer in the middle. For reference, MongoDB's documentation states the BI Connector reaches end-of-life and will no longer be supported after September 2026, with the SQL Interface as its recommended replacement. Neither is part of a Knowi connection.
See MongoDB analytics on your own collections.
Thirty minutes with a solutions engineer on your cluster. Bring one collection with nested fields and one source next to it. You leave with a dashboard, not slides.
See Knowi on your MongoDB data →Atlas or self-hosted. Cloud or on-premise.
Phil Bryant
VP Business Intelligence, MacroFab
Hundreds of companies trust Knowi to unify their analytics
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
- Click “Add Connection”
- Select MongoDB from the Datasources
- Insert your Mongo connection credentials.
- 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. Try a live MongoDB connection with Knowi.
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
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.
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.
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.
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.
MongoDB Reporting & Analytics
Welcome to the most powerful solution for analytics and visualization on MongoDB. Knowi is the only MongoDB BI provider that not only delivers truly native MongoDB visualization and reporting but also allows you to join disparate data sources. You can connect your MongoDB data with other relational data sources, NoSQL databases, and data from REST APIs. If that’s not enough, Knowi comes with integrated machine learning and search-based analytics for advanced business intelligence using MongoDB data.
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.
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.
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.
See our latest MongoDB Reporting and Visualization Tool Comparison Guide to know what solution can be best for your MongoDB analytics usecase
Frequently Asked Questions about MongoDB analytics and reporting
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.
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 use case.