MongoDB

How ArrayMatic uses MongoDB

MongoDB powers the document-centric layers of our applications — mobile app backends, product catalogues, user activity feeds, and IoT event ingestion pipelines where the document model maps naturally to the data shape. Unlike relational databases, MongoDB lets us evolve schemas without migrations, which suits early-stage products where the data model is still being discovered through production use.

We use MongoDB Atlas for managed deployments with automated horizontal scaling, built-in backups, and global cluster distribution. Mongoose provides schema validation and middleware hooks in Node.js services. PyMongo serves Python-based data pipelines. We pair MongoDB with Redis for session storage and caching, and add Atlas Search or Elasticsearch when full-text query complexity outgrows what a document store can serve efficiently.

When we choose MongoDB over PostgreSQL

For applications with complex multi-table joins, strict ACID guarantees across entities, or relational reporting requirements, PostgreSQL remains our default. We reach for MongoDB when the data is genuinely document-shaped, when schema flexibility is a real product requirement and not just a convenience, or when the write throughput and horizontal scale targets exceed what a single Postgres instance can serve without significant sharding complexity.

Typical use cases

• Mobile app backends with flexible user profiles and variable data structures
• Real-time activity feeds, event logs, and audit trails
• Product catalogues with variable attributes per category
• IoT telemetry ingestion with rapidly evolving device schemas