TL;DR (Summary)
- MongoDB is a popular NoSQL database designed to handle semi-structured and unstructured data using a document-based model (BSON).
- It’s highly scalable, flexible, and well-suited for modern applications like IoT, gaming, mobile apps, and big data analytics.
- MongoDB uses collections and documents instead of tables and rows, making it ideal for handling diverse data formats without rigid schemas.
- Tools like MongoDB Atlas support cloud deployment, while MongoDB Realm powers real-time mobile and offline-first applications.
- For visualization, MongoDB Charts is a native tool, while platforms like Knowi support native analytics across MongoDB and other data sources—without ETL.
- Alternatives like Amazon DocumentDB, DynamoDB, and Cassandra offer similar NoSQL capabilities with different architectural trade-offs.
Table of Contents
- Introduction to MongoDB
- What is MongoDB?
- MongoDB’s Mission
- How Does MongoDB Work?
- Advantages of MongoDB
- Deployment Options: On-Premise vs. Cloud
- MongoDB Alternatives
- How to Analyze MongoDB Data
- Knowi’s MongoDB Interface
- Common Use Cases and Tutorials
- MongoDB vs Other Databases
- Conclusion
- Frequently Asked Questions
An Overview on MongoDB
What is MongoDB?
MongoDB is a NoSQL database that stores data in a document-oriented format (BSON). Released in February of 2009 and developed in C++, it was designed for scalability and handling large amounts of unstructured data. It is a semi-structured database that allows users to seamlessly access data from a myriad of programming languages and other data tools. MongoDB is considered a “NoSQL database” meaning that data is not stored in tables unlike MySQL and PostgreSQL, but data is store in documents. These documents are grouped into MongoDB’s ‘collections’ where they can be accessed through APIs. This novel system was a major difference between MongoDB and its competitors when Eliot Horowitz and Dwight Merriman founded MongoDB.
MongoDB’s Mission
On their website, MongoDB writes, “We empower innovators to create, transform, and disrupt industries by unleashing the power of software and data.”(MongoDB) This idea stemmed from the many issues Horowitz encountered when trying to incorporate his more categorical structure into relational databases. The inefficiency and incoherence of many databases at the time prompted Horowitz to found his company and revolutionize the database industry.
In his words, “People develop classes, they develop rich structures. What they do then is take those rich structures and attach some object-relational mapping to those structures, and try to store it in a relational database.”(Horowitz) Today, MongoDB is used by major corporations including Adobe, Verizon, Lyft, and Twitch, just to name a few.
How does MongoDB Work?
MongoDB is unique in how it is structured as well as how data is stored within it. This differs from columnar databases like BigQuery or distributed systems like Cassandra. Read on to explore some basic concepts on how it organizes its data and backend components.
Logical Concepts
NoSQL vs. SQL Databases
“SQL” which stands for “Structured Query Language” is a type of DSL, or Domain Specific Language. Users can store data into a SQL-based relational database and use SQL to query and transform their data. Databases which do not use SQL are known as “NoSQL” or “non-relational”. The word “relational” refers to a model in which data are stored in tables, and key, identifying fields from each table are mapped in relation to one another. The term “NoSQL” is misleading as NoSQL databases (aka “Not only SQL”), including MongoDB, have querying functions, even if they do not function in the same way as SQL.
For a detailed comparison, see our guide on MongoDB vs PostgreSQL.
BSON
MongoDB stores its data in BSON, Binary JSON, documents. BSON and JSON are both forms of JavaScript, but they are encoded differently. Code in BSON can be transmitted faster than JSON, but is not discernable to humans. While Elasticsearch uses JSON natively and REST APIs typically return JSON, MongoDB’s BSON format provides performance advantages. In MongoDB’s case, the main focus is on efficiency rather than readability, so MongoDB hosts all of its documents in BSON. Based off of this, MongoDB develops its own structure of clusters and collections to hold its documents. Once a document has been created, MongoDB can import data from programming language drivers, including C++, Python, and Swift, or use third-party tools. MongoDB does this by converting data types into their array format allowing the user to subsequently perform functions on the dataset.
Clustering
Clustering is a technique for grouping data into individual clusters with similar data points. It uses intrinsics characteristics of data such as datatype to as metrics for similarity. The concept of cluster analysis exists in MongoDB, just with a slightly different functionality. MongoDB has a feature called “replica sets” which connects multiple nodes and will identify redundant data, anomalies, and many other patterns. Sharding, which was also mentioned in the scalability section, is a key tool MongoDB uses to support their horizontal scalability. Shard keys, unique identifiers for each shard, ensure that data on different servers remains connected to the primary cluster.
Semi-Structured Data
MongoDB is a database which employs semi-structured data through BSON documents. Unlike relational data—which requires data to be structured in cells, rows, and columns—semi-structured data does not have a fixed schema. MongoDB’s document-oriented system relies heavily on nesting within a document to organize data. These documents support unstructured data (i.e. videos, audio files, and emails) while clearly identifying fields for easy retrieval.
To summarize, semi-structured data is an in-between of unstructured and structured data where there is structure without a tabular format. On one hand, semi-structured data allows for greater flexibility and freedom with how one chooses to order their data. Data can be integrated from multiple datasets without having to fit one format. On the other hand, without proper understanding of how to nest objects, data can easily be stored suboptimally within a collection causing an organizational headache.
What are the Advantages of MongoDB? What is MongoDB Used For?
Flexibility
Supporting semi-structured data allows users to import various types of unstructured datasets and reorganize their data into identifiable groups. Many of MongoDB’s customers store video files (NOD Games, Mediastream, etc.) while many others need to store audio files (Electronic Arts, Yousician, etc.). This type of data can not be stored in a relational database management system. In addition, the fact that many web APIs, including RESTful and HTTP-based APIs, already use JSON as their data format gives MongoDB an edge over many SQL databases.
MongoDB supports both the import and export of JSON data so HTTP-based APIs have no issue transferring data with MongoDB. Overall, MongoDB is able to combine disparate data stored across programs into one centralized database using its document-oriented system.
Scalability
MongoDB is designed to be able to work on multiple servers and handle large loads of data. MongoDB uses a horizontal scaling, also known as a scale-out model, to handle their data. This spreads the load of the dataset evenly across multiple servers to prevent crashing or other complications. MongoDB also supports functions such as memory mapping, caching, and indexing—all of which help servers run smoothly. It uses a tool called sharding which breaks up clusters of data into subsets known as shards. These shards are distributed across servers and nodes, scaling horizontally.
This horizontal scaling approach is similar to Snowflake’s architecture but differs from traditional databases. For comparing scaling approaches, see Snowflake vs MongoDB.
Geospatial Indexes and Operators
MongoDB supports the creation of geospatial indexes, specifically the 2d index and the 2d sphere index. These indexes optimize spatial queries and enable fast retrieval of geospatial data. The 2d index is suitable for flat (Euclidean) coordinate systems, while the 2d sphere index supports spherical coordinate systems, making it ideal for Earth-like models. Using MongoDB, these indexes can be queried using the query optimization tool which selects which geospatial index to use and improves query performance.
MongoDB’s aggregation framework includes a set of geospatial aggregation operators. These operators enable complex analysis and calculations on geospatial data, such as finding the centroid of a polygon, measuring distances between points, or performing geometric calculations on shapes. One of MongoDB’s most notable geospatial aggregation operators is ‘$geoNear’ which calculates the distance between points and sorts by proximity.
For more on geospatial analytics, see our guide on geospatial data analysis.
Exporting Documents
Users can export files from MongoDB by using the command-line tool “mongoexport”. “mongoexport” will return a JSON or CSV file from the document data. Although some tools can retrieve data directly from MongoDB, in order to analyze the data many tools require the data to be exported directly into a file. One thing to be careful of is when data is exported to a CSV, MongoDB will have to modify the document formatting which may result in a flawed copy.
The “mongoexport” command can also be used to share documents to different clusters and projects. Once retrieved, data from MongoDB can be inputted into data warehouses or analytics platforms. MongoDB analytics solutions like Knowi can connect directly without export, supporting multi-datasource joins and REST API integration. Knowi also has a function to directly add data from MongoDB by inputting the host or IP address, the database name, and password.
Additionally, “mongoexport” can sort or remove rows and columns and perform many other changes to the data to better fit the user’s needs. This limits any problems when the database has to be queried or transformed into a graph or chart.
MongoDB Realm (Mobile Applications)
Many mobile applications require a database which can synchronize data offline and cache that data for a more efficient user experience. Apps need to have data such as user profiles on hand and stored in the proper location for their applications to function. Developers created MongoDB Realm to meet the needs of these apps.
Realm’s efficiency and integration across programming languages makes it a great fit for this type of work. Backend programs are often overlooked, but they are integral to the functionality of any application. It is being used by 7-Eleven, Imgur, and many more companies. Srikanth Gandra had this to say about Realm, “We’ve heard good feedback from store managers. They can start using devices immediately, rather than waiting minutes to download the data on initial startup, like they used to. Data accuracy, especially around inventory when sales happen or shipments arrive, has really improved.”
Interestingly enough, some video gaming startups use Realm with some success. It can be difficult to find a platform that can handle the traffic of successful mobile applications; MongoDB’s Realm provides a solution to many issues that developers face and has proved successful in all kinds of applications.
MongoDB Deployment Options: On-premise vs. Cloud
Many platforms are limited to either on-premise data storage or storage in the cloud. On premise essentially means that the data is stored on local servers, while the Cloud is a term for the servers a company hosts.
MongoDB has the ability to do both; “MongoDB Atlas” is a subscription tool which allows users to host their data on cloud servers while the free version of MongoDB hosts data on-premise. On-premise hosting requires users to individually back-up their data, but users have the benefit of accessing files offline. The cloud has the benefit of allowing users to always retrieve their data, provided they still have access to their account.
More on MongoDB Atlas
What is MongoDB Atlas and how does it work? To augment the explanation above, MongoDB Atlas is a cloud database which fully manages users’ data using a suite of data services. For a detailed tutorial, see our MongoDB Atlas getting started guide. Atlas competes with other cloud databases like Amazon DocumentDB.
MongoDB also calls the tool a “multi-region” application as users can access the tool in most places with a connection to the internet. Atlas will store clusters on these cloud services and it will retrieve them whenever a user reopens the webpage. Because the data is being stored on the cloud, Atlas can monitor the data for the user and generate performance reports and optimization recommendations.
MongoDB Alternatives
Other than Atlas, there are certainly many NoSQL cloud databases on the market today. A few notable examples are Amazon’s DocumentDB, Azure’s CosmosDB, and Couchbase’s Capella.
1. DocumentDB
DocumentDB is a JSON document database run by Amazon Inc. DocumentDB is highly compatible with MongoDB as it is a NoSQL database and also runs on a document system. It was built as an improved version of previous Amazon databases with much newer technology. One of these improvements is DocumentDB’s auto-growth capabilities which scale storage capacity to meet user demands.
2. DynamoDB
Amazon DynamoDB is a serverless, fully managed NoSQL database from AWS. It combines key‑value and document‑store models, allowing each item to have a flexible set of attributes. DynamoDB automatically scales throughput and storage based on demand, so applications can handle spikes in traffic without manual intervention. The service replicates data across multiple AWS regions for high availability, provides on‑demand and point‑in‑time backups, and encrypts data at rest and in transit. DynamoDB also integrates deeply with other AWS services and can process huge workloads, supporting millions of requests per second.
3. Cassandra
Apache Cassandra is an open‑source distributed database designed for extreme scalability and availability. It employs a partitioned wide‑column data model and replicates data across multiple nodes and even multiple data centers, eliminating single points of failure. Cassandra scales linearly by adding nodes to the cluster, which allows for write‑heavy workloads and large data volumes without downtime. It uses its own SQL‑like language called Cassandra Query Language (CQL) and offers a flexible schema where new columns can be added without interrupting service. Because it prioritizes availability and partition tolerance, Cassandra does not support cross‑partition transactions or foreign keys, and secondary indexing is limited; however, its high throughput makes it attractive for write‑intensive applications.
Overall, the primary differentiating factor between databases is whether they use SQL or not. Beyond that, most formatting between NoSQL databases is analogous.
How to Analyze MongoDB Data
Obviously, staring at raw collection data isn’t the best way to make sense of data. In order to take action on data, finding the right visualization and reporting tool is crucial. Unlike Kibana for Elasticsearch or native MongoDB Charts , some tools support multiple databases. Here are popular analytics options:
Native MongoDB Visualization Tools
These tools do not require an ETL tool or additional connector to analyze MongoDB data.
MongoDB Charts
MongoDB has a native tool called MongoDB Charts. Charts is MongoDB’s visualization tool which connects to a MongoDB collection and creates a dashboard with various charts. MongoDB Charts, like many other visualization tools, requires specific formatting to properly map out data. For a comparison of visualization tools, see our blog on visualization solutions for mongodb.
Ensuring all of a document’s data is correctly organized is necessary for Charts to work. Charts can only create bar and line graphs, scatterplot, number charts, and other basic visualizations. Furthermore, Charts cannot easily facilitate joining between separate collections which complicates the process of comparing data.
Knowi
Within Knowi, users can access advanced visualization options without an additional tool for ETL. Knowi can join MongoDB with MySQL, Elasticsearch, and REST APIs. The tool also supports MQL (Mongo Query Language) natively. For getting started, see our step-by-step MongoDB analytics guide.
The tool also supports MQL, Mongo Query Language, natively allowing users to query in whichever language they please. A con to Knowi is that it has somewhat limited ML capabilities. If ML features are a must, another option in this list might be a better fit.
MongoDB Visualization Tools that require ETL
These tools require an ETL tool or additional connector to analyze MongoDB data.
Tableau
Tableau Software, or simply Tableau, is strictly a data-visualization company with a focus on aesthetics and simplicity. Tableau tries to make the process of creating visualizations as easy as possible by minimizing gratuitous features. The tool was specifically designed for businesses and has a long-standing reputation in many industries.
One consideration to keep in mind is that Tableau is fairly pricey compared to other options on the market. It also has some issues with interfaces such as mobile applications and other tools it was not primarily designed for.
Looker
Looker has integrated support for SQL making it easy to use with MySQL, PostgreSQL, OracleDB, and other relational databases. LookML, Looker’s modeling language, groups data into “views” which are analogous to tables in relational databases. Looker has a somewhat steeper learning curve compared to other analytics tools on this list, but many consider it a great option once that barrier has passed.
All of these tools have their own use cases where they are best fit for the job so make sure to thoroughly consider the different options. For a more in-depth look at analytics tools for MongoDB, take a look at this blog as it also takes a deeper dive into the options mentioned above.
Pros and Cons of MongoDB Reporting & Data Visualization Tools Compared
| Tool | Pros | Cons |
| Knowi | Supports MongoDB and MongoDB Atlas nativelySeamlessly supports NoSQL/SQL data blendingSupports MongoDB Query Language natively | Limited ML capabilitiesMay require query optimization with direct connections |
| MongoDB Charts | Already integrated with MongoDB dataDrag and drop builder | Only supports MongoDB dataDoesn’t easily support cross-collection joins |
| Looker | Lots of visualization optionsSupports git version control | Requires MongoDB connector and JDBC driver to pull data inRequires learning LookML |
| Tableau | Drag and drop interfaceChart types are visually appealing | Requires MongoDB connector and driverRequires relational data |
MongoDB Interface in Knowi
To explore the MongoDB interface on the Knowi website click here. Below is a quick tutorial of how the process works and the specific features Knowi offers.
To connect MongoDB to Knowi, enter the datasource name, hostname/IP address, database name, and password. In the screenshot above, the information for the demonstration has already been entered. Now, just create a new query and select Mongo Database to begin querying.
Here is an example of a query in MQL on this NBA dataset. AI CodeGen, the button at the top, is powered by OpenAI and will explain the query as well as find any issues.
Knowi also has a drag and drop feature visible on the left side of the UI. After selecting a given collection, simply expand the dataset to access the collection’s documents and retrieve a given variable. Knowi will write the query in MQL once the desired metrics and groupings are put in.
Common MongoDB Use Cases and Tutorials
- Common MongoDB Query Errors and Solutions
- Is MongoDB Good for Analytics?
- MongoDB Tips and Best Practices
- MongoDB Atlas Tutorial
- Getting Started with MongoDB Analytics
MongoDB vs Other Databases
- Elasticsearch vs OpenSearch – For search use cases
- Cassandra vs MongoDB – For distributed systems
- NoSQL Reporting Tools Comparison
- Data Storage Architecture Options
Conclusion
MongoDB was founded with the objective of creating a better database for programmers and developers. Today, MongoDB plays an integral role in managing unstructured data for thousands of companies as a leading NoSQL database.
To explore analytics options, check our MongoDB analytics platform or see how to compare MongoDB analytics tools. Ready to visualize your MongoDB data? Book a demo to see how Knowi can help turn yourdata into action
MongoDB’s scalability makes it great for any company in need of fast results on large datasets. Its tools such as Atlas and Compass also limit the load on local servers by using cloud servers to host their data in an easily accessible form. MongoDB has spread fast and leading corporations such as Adobe and Lyft are already using the platform to host all things data. Its scalability and versatility give MongoDB a decisive edge over many other NoSQL databases.
In conclusion, MongoDB is a great option for both on-premise and cloud work, and with the help of Knowi, companies can seamlessly query their data and turn data into action.
Frequently Asked Questions
What is MongoDB used for?
MongoDB is used to store and manage semi-structured data across various applications like content management, mobile backends, IoT, gaming, and analytics. It’s ideal for use cases requiring scalability and schema flexibility.
What’s the difference between MongoDB and SQL databases?
SQL databases store data in tables with a fixed schema and use SQL for querying. MongoDB, a NoSQL database, stores data in flexible, JSON-like documents (BSON) grouped into collections. It doesn’t require a fixed schema and supports horizontal scaling.
What is BSON and how is it used in MongoDB?
BSON (Binary JSON) is MongoDB’s format for storing documents. It allows faster parsing and supports more data types than JSON. BSON is not human-readable but is optimized for performance and indexing within MongoDB.
Is MongoDB good for analytics?
Yes. MongoDB can handle complex datasets and integrates with tools like Knowi or MongoDB Charts for analytics and visualization. Knowi supports native MongoDB connections and multi-source joins, eliminating the need for ETL.
Can MongoDB be deployed in the cloud?
Yes. MongoDB Atlas is a fully managed cloud database solution that offers global scalability, backups, performance monitoring, and multi-region support. It complements on-premise deployment options.
What are the advantages of MongoDB over other NoSQL databases?
MongoDB offers:
- Flexible document structure (BSON)
- Native support for JSON APIs
- Robust cloud deployment (MongoDB Atlas)
- Built-in analytics and mobile support (Realm)
- Rich ecosystem and community
What tools can I use to visualize MongoDB data?
- MongoDB Charts: Native, no-ETL solution for basic charts.
- Knowi: Advanced platform supporting MongoDB, SQL, and REST APIs with MQL support and drag-and-drop dashboards.
- Tableau/Looker: Require ETL connectors but offer robust visualization features.
Is MongoDB better than DynamoDB or Cassandra?
It depends on your use case:
- MongoDB: Great for flexibility, cloud + on-prem, and analytics.
- DynamoDB: Best for serverless, AWS-native apps with automatic scaling.
- Cassandra: Ideal for write-heavy, distributed systems with high availability.
