mongodb

MongoDB supports two main data modeling patterns: embedding documents within a single collection for related data, or referencing documents across collections for looser relationships. Choose embedding for frequently accessed related data and references when data is large, updated independently, or shared across many documents. Schema validation can enforce data integrity on either pattern.

Create indexes matching your query patterns—single-field, compound, text, geospatial, or TTL indexes depending on your use case. Use the `explain()` method to analyze query execution and identify missing indexes or inefficient scans. Index strategy directly impacts query speed and resource consumption.

Sharding distributes data across multiple servers using a shard key. Design your shard key to ensure even data distribution and support your most frequent queries. Configure replica sets for each shard to maintain high availability while scaling read and write capacity.

Aggregation pipelines process documents through multiple stages—matching, grouping, projecting, sorting, and joining—to transform and analyze data within MongoDB. Pipelines enable real-time analytics, complex computations, and data reshaping without moving data to your application.

Enable authentication to verify user identity, implement Role-Based Access Control (RBAC) to grant specific permissions, encrypt data in transit and at rest, and audit database operations. Combine these layers whether deploying on MongoDB Atlas, self-managed servers, or Kubernetes.

MongoDB provides time-series collections optimized for storing and querying timestamped data with automatic compression. Change streams enable applications to subscribe to real-time database modifications, supporting event-driven architectures and live data synchronization.