Expertise
MongoDB expertise: modelling and operating a document database
MongoDB is the most widely used document database. It stores flexible-schema BSON documents, offers rich indexes, a powerful aggregation pipeline, multi-document transactions and horizontal scaling through sharding. MongoDB Atlas adds managed hosting, full-text search and vector search.
Agencei uses MongoDB when the data model is genuinely document-oriented: catalogues with variable attributes, event logs, user profiles, content, data from third-party APIs. We pair it mostly with Node.js and NestJS, and we audit existing databases whose performance is degrading.
This expertise is for teams building a product with an evolving schema, or already running MongoDB and facing slowness, cost or consistency issues.
Use cases
SaaS backend with Node.js
Per-tenant documents, schemas validated on both the application and database side, and compound indexes for the main screens' queries.
Catalogues with variable attributes
Products, equipment or assets whose characteristics differ from one category to another, without multiplying join tables.
Logging and events
High-volume event collections with TTL indexes, time series collections and aggregations for dashboards.
Content and semi-structured data
Dynamic forms, partner API responses and business documents stored as-is, then queried through indexes.
Audit and optimisation of an existing database
Review of the model, unnecessary or missing indexes, collection-scanning queries and cluster sizing.
Project types
- Database for a Node.js application
- Flexible-schema SaaS backend
- Product or equipment catalogue
- Event and log storage
- MongoDB performance audit
- Migration to or from MongoDB
- Vector search with Atlas
Integration with the rest of the stack
MongoDB + Node.js (Mongoose, native driver)
Mongoose schemas or database-side JSON Schema validation, TypeScript typing of documents and integration tests on a containerised instance.
MongoDB + Atlas on AWS
Managed cluster in your application's AWS region, network peering, continuous backups and alerts on load metrics.
MongoDB + Docker and Kubernetes
Containerised instance for development and testing, or a Kubernetes operator when self-hosting is required.
MongoDB + PostgreSQL
Mixed architecture: transactional data in PostgreSQL, large or variable documents in MongoDB, synchronised through events.
Examples of problems solved
Queries scanning the whole collection (COLLSCAN)
Execution plan analysis, creation of compound indexes following the ESR rule and removal of redundant indexes that slow down writes.
Documents exceeding the size limit
Model redesign: unbounded arrays moved to a dedicated collection with references and the bucket pattern for series.
Slow aggregation for a dashboard
Reordering stages to filter early, covering indexes, precomputation in a materialised collection refreshed periodically.
Inconsistencies between collections
Using multi-document transactions where necessary, or remodelling to group what must be updated together.
Rising Atlas costs
Reducing index and document sizes, archiving cold data, adjusting the tier and removing orphaned collections.
Frequently asked questions
When should we choose MongoDB over PostgreSQL?
When the data really consists of documents with a variable structure, reads mostly fetch whole documents and the team works in JavaScript or TypeScript. For strongly relational data, PostgreSQL remains preferable.
Does MongoDB support transactions?
Yes, multi-document transactions have existed since MongoDB 4.0 on replica sets. They have a cost and should remain the exception; a good document model limits the need for them.
Atlas or self-hosting?
Atlas greatly simplifies operations: backups, upgrades, monitoring and scaling. Self-hosting is justified by sovereignty constraints or cost at very large volumes.
How do you secure MongoDB?
Mandatory authentication, least-privilege roles, encryption in transit and at rest, private networking with no public exposure and access auditing.
Can you migrate from MongoDB to a relational database?
Yes. We design the target schema, write idempotent transformation scripts and use dual writes or synchronisation to cut over without data loss.
Related services
MongoDB expertise
Document modeling, indexes, aggregations, migration and performance on MongoDB and Atlas.
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Design, optimization, administration and backups for your PostgreSQL, MySQL, MongoDB and Redis databases.
See this serviceData migration
Migration between databases, engines, versions or systems, with ETL, validation and zero-downtime cutover.
See this serviceSaaS platform
Multi-tenant SaaS platforms with subscriptions, billing and infrastructure ready to scale.
See this serviceRelated expertise
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