Data & Storage
MongoDB where documents, flexibility and scale beat rigid schemas
A document database for varied, evolving or high-volume data — catalogues, events, content and IoT telemetry that never fit neatly into fixed tables.
1 read
Denormalised documents replacing multi-join queries
Horizontal
Sharded scaling for write-heavy collections
Schema-flex
New attributes without migration downtime
<50ms
Indexed query latency target
Definition
What MongoDB is
MongoDB stores JSON-like documents with flexible schemas, so records with different shapes coexist in one collection. Reads that would need many joins in a relational model become single-document fetches.
When to use it
Where MongoDB is the right call
Recognise your situation before committing engineering budget.
Capabilities
How we work with MongoDB
What is actually delivered, beyond the logo on a stack slide.
Document modelling
Access-pattern-first design deciding what to embed and what to reference.
Indexing strategy
Compound, partial, text and TTL indexes tuned against real query profiles.
Aggregation pipelines
In-database analytics and reporting views without exporting data.
Operations
Atlas or self-hosted replica sets, backups, point-in-time restore and alerting.
Strengths
- Flexible schema suits evolving and heterogeneous data
- Excellent write throughput and horizontal scaling
- Natural fit for JavaScript and TypeScript stacks
- Rich aggregation framework for in-place analysis
Trade-offs to plan for
- Multi-document transactions exist but relational integrity is weaker
- Bad document design is far harder to fix later than a bad table
- Reporting tools generally expect SQL; plan a warehouse path
Use cases
MongoDB in production
Representative systems we build and support on this technology.
Marketplace catalogue
Millions of SKUs with category-specific attributes and fast faceted search.
IoT and telemetry
High-frequency device readings with TTL retention and rollups.
Activity and audit streams
Append-only event history feeding dashboards and alerts.
Comparison
MongoDB vs PostgreSQL for enterprise data
| Dimension | MongoDB | PostgreSQL |
|---|---|---|
| Schema flexibility | High | Moderate with JSONB |
| Transactional integrity | Good, document-scoped by default | Excellent, ACID |
| Write-heavy scaling | Sharding built in | Vertical plus partitioning |
| Reporting and BI | Aggregation framework | Native SQL, widest tool support |
Engineering practices
Non-negotiables on every engagement
Decision guide
Which direction fits your situation?
Match your constraints to the recommended approach.
| If this sounds like you | We recommend |
|---|---|
| Financial or inventory system of record | PostgreSQL for integrity |
| Catalogue, events or telemetry at volume | MongoDB with access-pattern-driven modelling |
| Both needs in one product | PostgreSQL as the system of record, MongoDB for read and event workloads |
Delivery
Services built on MongoDB
Pairs well with
Industries running this
FAQ
MongoDB questions, answered
Related technologies
What we usually pair with it
PostgreSQL
An open-source relational database with genuine ACID guarantees, deep SQL, JSONB flexibility and vector search — the default system of record for serious business data.
Node.js
An event-driven runtime that handles thousands of concurrent I/O-bound requests efficiently — ideal for API gateways, integrations and real-time features.
Docker
Reproducible application images that remove "works on my machine" from the vocabulary and make deployments boring in the best possible way.
Planning a build or migration on MongoDB?
Share your requirements and current systems. We will return an architecture recommendation, trade-offs and a phased plan — under NDA.