Database Options We Use
PostgreSQL is rock-solid for most applications with powerful features like JSON support and full-text search. MongoDB works well for document-heavy products where schema flexibility matters. Redis provides blazing-fast caching and real-time data. Firebase offers quick starts for teams without database operations expertise. Each choice depends on your access patterns, consistency requirements, and scale expectations.
Database Architecture Decisions
We design for scalability from day one with proper indexing so queries stay fast as data grows. Connection pooling prevents database overload. Read replicas distribute query load across multiple servers. Sharding splits large datasets across multiple databases when a single database hits limits. Backup strategies ensure data recovery is minutes away, not hours or days.
Performance Optimization
- Query optimization and indexing prevent slow queries before they hit users
- Data partitioning splits large tables so queries are faster and more efficient
- Caching with Redis eliminates repeated database queries
- Denormalization in specific places trades storage for speed where it matters
- Query monitoring identifies bottlenecks before they become problems
- Caching with Redis eliminates repeated database queries
File Storage & Backups
Large files like images and videos live on S3, Google Cloud Storage, or similar cloud services instead of in the database. CDN integration makes files load fast from anywhere in the world. Automated backups run daily with encryption at rest. Point-in-time recovery means we can restore to any moment if something goes wrong. Archive storage keeps old data accessible but cost-effective.
Database Options We Use
PostgreSQL is rock-solid for most applications with powerful features like JSON support and full-text search. MongoDB works well for document-heavy products where schema flexibility matters. Redis provides blazing-fast caching and real-time data. Firebase offers quick starts for teams without database operations expertise. Each choice depends on your access patterns, consistency requirements, and scale expectations.
Database Architecture Decisions
We design for scalability from day one with proper indexing so queries stay fast as data grows. Connection pooling prevents database overload. Read replicas distribute query load across multiple servers. Sharding splits large datasets across multiple databases when a single database hits limits. Backup strategies ensure data recovery is minutes away, not hours or days.
Performance Optimization
- Query optimization and indexing prevent slow queries before they hit users
- Data partitioning splits large tables so queries are faster and more efficient
- Caching with Redis eliminates repeated database queries
- Denormalization in specific places trades storage for speed where it matters
- Query monitoring identifies bottlenecks before they become problems
- Caching with Redis eliminates repeated database queries
File Storage & Backups
Large files like images and videos live on S3, Google Cloud Storage, or similar cloud services instead of in the database. CDN integration makes files load fast from anywhere in the world. Automated backups run daily with encryption at rest. Point-in-time recovery means we can restore to any moment if something goes wrong. Archive storage keeps old data accessible but cost-effective.
