Operational databases
Schema design, indexing, and migrations for PostgreSQL and Azure Database for PostgreSQL / Flexible Server.
Data
Data platforms that applications can trust.
We design the storage, pipelines, and access paths that keep product data consistent — from operational databases to retrieval layers used by AI systems.
Data Engineering
CloudCubit works on operational data stores, caching, and the pipelines that move data between systems. Typical work includes PostgreSQL, Azure Database for PostgreSQL, Cosmos DB, MongoDB, Redis, and the interfaces that applications and AI features consume.
Document & NoSQL stores
MongoDB and Azure Cosmos DB when the access pattern is document-oriented or needs multi-region distribution.
Caching & session state
Redis for hot paths, rate limits, and ephemeral state that should not live in the primary database.
Pipelines & sync
Reliable movement of data between services, warehouses, and search indexes with retry and idempotency in mind.
AI retrieval data
Chunking, embeddings, and vector search stores used by RAG and agent systems, kept in sync with source documents.
Access and retention
Role-aware access, backup expectations, and retention that match the sensitivity of the data being stored.
How we approach the work
- 01
Model the access path
We design around how the application reads and writes data, not around a preferred database brand.
- 02
Separate hot and analytical paths
Transactional systems stay lean. Heavier reporting and retrieval workloads get their own stores and pipelines.
- 03
Protect the source of truth
Migrations, backups, and ownership are explicit so product data does not become an unowned side effect of features.
Technologies we use
Selected for the problem, not as a partnership claim.
- PostgreSQL
- Azure Database for PostgreSQL
- PostgreSQL Flexible Server
- MongoDB
- Redis
- Azure Cosmos DB
- NoSQL
- Vector Search