Variable, nested records
Product catalogues with very different attributes per category, form submissions or content blocks whose shape changes over time.
Technologies · MongoDB
MongoDB development services, starting with the data model
MongoDB rewards a schema designed around how the application reads data, and punishes one copied from a relational diagram. We design for the queries first, and tell you plainly when PostgreSQL would serve you better.
What is MongoDB?
MongoDB development services cover designing, building and operating applications on MongoDB, a document database that stores records as flexible JSON-like documents instead of rows in fixed tables. Related data that is read together can be embedded in one document, fields can vary between documents, and collections scale through replica sets and sharding. MongoDB is available as a managed cloud service, MongoDB Atlas, or as self-hosted server software.
Where a document database fits
Event and activity data
High write volumes of logs, IoT readings or user activity, where time-series collections and TTL indexes keep storage manageable.
Node.js-first products
JavaScript and TypeScript teams that want data shaped like their objects, with Mongoose or the native driver.
Horizontal scale on one dataset
Workloads expected to outgrow a single server, where built-in sharding distributes data across machines.
When we recommend something else
- Highly relational data — invoices, ledgers, stock and orders with many joins belong in PostgreSQL, where integrity is enforced by the database.
- ERP and accounting systems — Odoo requires PostgreSQL, and accounting data needs strict constraints more than flexible schemas.
- A mostly relational application with a few flexible fields — a JSONB column in PostgreSQL usually covers that without a second database.
- Teams choosing it only to avoid schema design — the schema still exists, it just moves into application code and becomes harder to change safely.
MongoDB work we take on
Document schema design
Embedding versus referencing decided per relationship, based on read patterns, document growth and the 16 MB document limit.
APIs and back-ends on MongoDB
Node.js or Python services with validation rules on collections, so bad data is rejected at write time.
Query and index tuning
Compound indexes ordered for equality, sort and range, aggregation pipelines reviewed with explain(), and slow-query profiling.
Atlas and replica set operations
Cluster sizing, backups, alerts and network access on Atlas, or replica sets with backups and monitoring on your own servers.
How a MongoDB application is layered
L1
Application and ODM
Node.js with Mongoose or the native driver, or Python with PyMongo, using connection pooling and sensible timeouts.
L2
Schema validation
JSON Schema validators on collections, so required fields and types are enforced by the database as well as the code.
L3
Indexes and query layer
Indexes designed per query pattern, with aggregation pipelines for reporting and Atlas Search where full-text search is needed.
L4
Replica set or sharded cluster
Three-member replica sets for failover, with sharding added only when one replica set can no longer hold the data or the writes.
L5
Backups and monitoring
Atlas continuous backups or scheduled snapshots and mongodump for self-hosted, with alerts on replication lag, slow operations and disk.
Ecosystem we work with
- MongoDB Atlas
- Mongoose
- Aggregation pipeline
- Change streams
- Atlas Search
- Atlas Vector Search
- Time-series collections
- MongoDB Compass
- mongodump / mongorestore
- Node.js driver
- PyMongo
What a well-modelled MongoDB gives you
Reads in one round trip
When related data is embedded correctly, a page loads from a single document read instead of several joins.
Schema changes without migrations
New optional fields can be rolled out gradually, with validation rules tightened once the data is consistent.
Failover built in
A replica set elects a new primary automatically when one node fails, and drivers reconnect without code changes.
Change streams for integrations
Applications can subscribe to inserts and updates to sync search indexes, caches or other systems in near real time.
MongoDB or PostgreSQL with JSONB?
| Consideration | MongoDB | PostgreSQL with JSONB |
|---|---|---|
| Data shape | Whole dataset is document-oriented | Mostly relational, with some flexible fields |
| Joins and integrity | $lookup for occasional joins; integrity mostly in application code | Full SQL joins, foreign keys and constraints |
| Transactions | Multi-document transactions supported, best kept short | Transactions are the default for everything |
| Horizontal scaling | Built-in sharding | Read replicas; sharding through extensions such as Citus |
| Managed option | MongoDB Atlas on AWS, Azure or Google Cloud | Amazon RDS, Cloud SQL, Azure and others |
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
MongoDB questions
When should I use MongoDB instead of PostgreSQL?
Use MongoDB when most of your data is naturally document-shaped, varies in structure, and is read as whole records, or when you expect to shard one large dataset. If your data is mostly relational with joins and strict integrity, PostgreSQL, optionally with JSONB columns, is usually the better choice.
Does MongoDB support transactions?
Yes. MongoDB supports multi-document ACID transactions on replica sets and sharded clusters. They are best kept short, and a schema that needs them constantly is often a sign that the data is relational.
Should we use MongoDB Atlas or host MongoDB ourselves?
Atlas is the simpler choice for most teams because it handles provisioning, backups, upgrades and failover, and it adds features such as Atlas Search. Self-hosting can cost less at steady scale and keeps data on your own servers, but you take on patching, monitoring and backup testing, and the Community Server is licensed under the SSPL.
Can you fix a slow MongoDB database?
Usually, once the slow operations are identified. Common causes are missing or badly ordered compound indexes, unbounded arrays inside documents, aggregation pipelines that filter late, and collection scans; the profiler and explain() show which applies.
Can MongoDB data be migrated to PostgreSQL?
Yes, but it is a redesign, not a copy. Documents have to be mapped to normalized tables or JSONB columns, embedded arrays become child tables, and application queries are rewritten, so we plan it as a project with a parallel-run period.
Next step
Deciding on MongoDB, or untangling an existing one?
Describe your data and the queries your app runs most. We'll tell you whether MongoDB fits, how we'd model it, or what is slowing your current cluster.