Challenges of Traditional MongoDB Analytics

TL;DR

Using MongoDB for analytics often creates major challenges that slow teams and waste resources:

• 30-minute queries for simple analytics
  
• Schema changes that break dashboards
  
• Developers stuck building reports instead of features
  
• SQL-based BI tools that need complex workarounds
  
• $200K+ yearly in ETL and infrastructure costs
  

MongoDB is powerful for operations but not optimized for analytics. Traditional BI tools only add more ETL, cost, and delay.

Knowi eliminates these challenges by connecting directly to MongoDB, adapting to schema changes automatically, and enabling real-time, self-service analytics without developer dependencies.

Table of Contents

What are the MongoDB challenges?

MongoDB is a flexible, powerful database platform designed for modern application development. But when it comes to analytics, these same strengths often create serious yet predictable challenges.

The problem isn’t the tool itself – it’s simply because MongoDB wasn’t designed for this specific use case. As a result, it runs into the same core bottlenecks, bringing analytical workflows to a crawl and decimating productivity.

When Simple Analytics Turn into 30-Minute Queries

MongoDB was not built with complex analytical queries in mind. It’s optimized for operational workloads, and its joins require multiple lookup stages, unwind operations, and nested aggregations. This means that MongoDB analytics is both complex to write and slow to execute, with queries often taking 30 minutes or more to complete.

The problem only gets worse at scale. For example, IoT data can grow fast, accumulating billions of records before you know it. If you try to run analytics on that kind of data through a database that wasn’t built for it, everything breaks down:

• ETL processes fall behind incoming data volume
• Aggregation pipelines timeout mid-query
• Dashboards become useless because they’re hours out of date

Teams are forced to choose between incomplete data and unusable wait times, and neither option supports effective decision-making.

To see how Knowi handles this challenge, check out our MongoDB to Real-Time Dashboards: Step-by-Step Tutorial, where we show how to query billions of MongoDB records in seconds without ETL.

How MongoDB’s Schema Flexibility Becomes a Weakness

Schema flexibility is one of MongoDB’s biggest strengths. The tool is designed to constantly evolve to meet your needs, and development teams take advantage of this fact to improve their applications.

But this flexibility comes at a great cost, with each structural change creating a headache for the analytics team:

• Queries fail when field names change
• Dashboards go blank when structures are modified
• Teams spend hours fixing what used to work

Traditional analytics tools expect stable schemas. And if progress in one area creates problems in another, you don’t have a truly efficient system.

Schema evolution doesn’t have to break dashboards – learn more in our NoSQL Analytics in 2025: Challenges and Use Cases, which explores how flexible data structures can still power reliable analytics.

The Developer Bottleneck of Engineers Becoming Report Builders

Your developers understand MongoDB’s structure better than anyone. So who does everyone turn to when they need a report? The engineers who are supposed to be building your product. 

But this only creates a costly cycle:

• Marketing needs a dashboard, so engineering gets a ticket
• A simple join requires 50 lines of complex aggregation pipeline code
• What should be a quick task becomes a three-week sprint
• Meanwhile, product development stalls

The real cost isn’t just slow reports – it’s the opportunity cost of misallocated engineering talent.

Platforms that combine NLP and AI can eliminate this dependency entirely – explore how in AI-Powered Analytics: Query Your Data Like ChatGPT.

When SQL-Based BI Tools Meet MongoDB

Tools like Tableau were designed for structured data and expect SQL databases, while MongoDB was built for flexibility with nested documents and dynamic schemas.

Of course, this doesn’t stop analytics teams from wanting to use these tools to get the most out of their MongoDB data. However, doing so requires building complex workarounds just to connect the two:

• Custom scripts that break with every schema change
• ETL processes that need constant maintenance
• Developers pulled away from product work to fix integrations

These workarounds are time-consuming to build and expensive to maintain, making convenient data visualization more of a chore than an added benefit.

The True Cost: $200,000 Annually in Hidden Expenses

The above challenges aside, data teams are always surprised to discover that MongoDB analytics costs much more than they expected.

A lot of these expenses are due to ETL: Moving data out of MongoDB into a separate analytics database means duplicating infrastructure, creating lag, and building fragile pipelines that break whenever source systems change.

The expenses also manifest in:

• Engineering time: Building pipelines, maintaining connections, fixing breaks
• Opportunity cost: Developers focused on analytics instead of product development

When all is said and done, the costs are staggering, with companies typically investing around $200,000 annually.

Solving the MongoDB Analytics Challanges with Knowi

Knowi lets you completely circumvent the traditional challenges of MongoDB analytics, working with the database’s design instead of against it. Here’s how:

Turning 30-Minute Queries into 30-Second Responses

Knowi connects directly to MongoDB and queries in real time, using native MongoDB queries optimized specifically for analytical workloads. This allows it to efficiently handle complex joins and process billions of records, getting results in seconds instead of half an hour later.

Keeping Dashboards Working When Schemas Change

Knowi automatically adapts to schema changes as they happen. So when development teams rename fields or restructure documents to improve their applications, analytics dashboards continue functioning without manual intervention.

Freeing Developers to Build Product Instead of Reports

Knowi’s drag-and-drop interface lets non-technical business users extract insights from MongoDB analytics without writing any code. This means engineers can get back to shipping features while analysts get answers immediately.

Working with traditional BI tools like Tableau Without the Workarounds

Knowi provides native MongoDB support that integrates directly, unlike traditional BI tools like Tableau. There’s no need to build custom scripts, maintain ETL processes, or pull developers away to fix broken connections.

If your analytics span multiple databases, learn how to seamlessly unify them in How to Join MongoDB Data with MySQL, Elasticsearch, REST APIs, and Amazon Redshift.

Eliminating ETL Infrastructure and Associated Costs

By connecting directly to MongoDB, Knowi removes the need for separate analytics databases, transformation pipelines, and duplicate storage – cutting infrastructure costs by up to 70%.

See how this approach simplifies your stack in Knowi’s architecture overview.

Trading MongoDB Analytics Bottlenecks for a Modern Solution

The challenges that come with traditional MongoDB analytics are completely avoidable if you use the right solution.

Knowi eliminates them through:

• Query optimization that delivers sub-second performance on billions of records
• Automatic schema adaptation that keeps dashboards working as data evolves
• Self-service capabilities that free developers from report-building
• Native MongoDB support that works with Tableau and other existing tools
• Direct connections that remove ETL infrastructure and costs

Request a demo now to see what MongoDB analytics looks like without the bottlenecks.

Frequently Asked Questions