Database Caching – Utilize caching mechanisms like Redis or Memcached to store frequently accessed data, reducing the need for repetitive queries. Partitioning and Sharding – Distribute data across multiple servers or partitions to improve query efficiency and reduce bottlenecks. Optimize Schema Design – Normalize where necessary, but also consider denormalization for read-heavy workloads to reduce complex joins. Monitor Performance Metrics – Use database monitoring tools to identify slow queries, lock contention, or resource bottlenecks. Regular Maintenance – Perform routine database maintenance tasks like vacuuming (for PostgreSQL), analyzing statistics, and reindexing.

First thing first as a dba/dev you should prioritize a good and efficient Entity–relationship model, I'll enum some factors to have in mind: 1. Use Normalization to Avoid Redundancy 2. Define Clear Relationships 3. Use Proper Indexing for Faster Queries 4. Implement Constraints to Ensure Data Validity 5. Design for Future Scalability Applying the previous rules we can achieved the next: 1. Data Integrity and Consistency 2. Avoids Data Redundancy and Duplication 3. Supports Scalability and Maintainability 4. Enforces Business Rules and Constraints Also we have to keep in mind we should have to perform refactors to older queries, maybe revisited the architecture to have a good approach when are needed a non relational and relational sql

If any organization is expanding rapidly and their database is slowing down, here are some ways how we can quickly fix it: •Identify slow queries and fine-tune them by improving indexes, joins, and overall query structure. •Add read replicas or shard the database to distribute the load more efficiently. •Reduce repetitive queries by leveraging caching solutions like Redis or Memcached. •Adjust memory allocation, indexing, and connection pooling for better performance. •Use connection pooling to handle concurrent requests smoothly. • If needed, migrate to a cloud-based or distributed database like Amazon Aurora or DynamoDB that can scale with your growth.

To quickly resolve database slowdowns, it's important you review hardware resource utilization which includes ensuring that the database server has sufficient CPU, memory and disk resources. And also implement caching for frequently accessed data using tools like Memcached. Besides you can partition large tables to improve query performance and maintenance.