Schema Design Patterns
We choose between embedding and referencing and apply patterns such as bucket, subset, and computed, so documents stay a sensible size and common queries touch as few as possible.
Database Technologies
MongoDB Consulting & Support
Flexible Schemas That Still Perform at Scale
MongoDB lets developers ship quickly, but schema, index, and shard key choices decide how well it performs later. Our consultants design, deploy, tune, and support MongoDB on Atlas and on MongoDB Enterprise Advanced across AWS, Azure, and Google Cloud.
What We Deliver
Where We Make the Difference
Flexible schemas can drift into oversized documents, unbounded arrays, and missing indexes that only hurt once data grows. We shape documents around your application's queries, index for real workloads, and set up replication and sharding before scale forces the issue.
Indexes and Aggregations
We study query plans and profiler output, add compound and partial indexes, and restructure aggregation pipelines so filtering happens early and memory use stays low.
Replica Sets and Sharding
We build replica sets for availability, then shard collections with keys that spread writes evenly and keep the most common queries targeted to a single shard.
Search on Your Own Data
Atlas Search and Atlas Vector Search add full-text and semantic search directly on your MongoDB collections, so you avoid syncing data into a separate search engine.
Our Approach
How We Deliver MongoDB Consulting & Support
Step 1: Model Around Queries
We catalog the queries your application runs, then design documents, indexes, and validation rules that serve them efficiently as data grows.
Step 2: Stand Up the Deployment
We deploy Atlas clusters or self-managed replica sets with authentication, encryption, network controls, monitoring, and backups configured from day one.
Step 3: Profile and Optimize
We load test, examine slow queries and execution plans, and refine indexes, schema, and pipelines until performance holds under production traffic.
Step 4: Operate and Upgrade
We monitor health and capacity, plan version upgrades and scaling, and support the deployment directly or train your team to run it.
Should we use MongoDB Atlas or run MongoDB ourselves?
Atlas removes most operational work and adds features like Atlas Search, while self-managed MongoDB gives more control over infrastructure and data location. We compare them against your compliance needs, team size, and budget.
Can you move a relational database to MongoDB?
Yes, when the application benefits from a document model. We redesign the schema for MongoDB rather than copying tables, and can use MongoDB Relational Migrator to plan the mapping and move the data.
When does sharding make sense?
When a single replica set can no longer handle your data size or write volume. Sharding adds complexity and a shard key is costly to change later, so we plan it carefully and only when it is needed.
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