As the database increases in size, make sure it grows the same as the company grows, aka by region, that way each region can be operated like its own entity. Each region/database can be managed to support the company in that region. One size does not fit all.

Create read-only copies of your database to handle high read traffic. This can reduce the load on the primary database and improve performance for read-heavy applications. Proper indexing can improve query speed, and materialized views can help pre-compute frequently accessed data, reducing computation time for repetitive queries. Break up large databases into smaller, manageable segments called "shards" based on certain criteria. This helps distribute data across multiple servers, making large datasets easier to handle.

?? Optimize Key Queries: Fine-tune high-impact queries, adding strategic indexes to reduce load and prevent latency spikes, making your database more scalable. ?? Horizontal Scaling: Distribute workloads across multiple nodes or servers to improve capacity and mitigate single-point bottlenecks. ?? Implement Caching Layers: Use caching solutions like Redis or Memcached to store frequently accessed data, reducing repetitive database calls. ?? Utilize Elastic Cloud Services: Choose managed cloud databases with autoscaling features to handle demand fluctuations efficiently. ?? Real-Time Monitoring & Alerts: Enable detailed monitoring and alerting on performance metrics to identify and address issues proactively.

Obtenha os pré-requisitos de hardware com o fornecedor da solu??o, ninguém melhor do que eles para estimar o crescimento da base de dados. Obtenha uma estimativa de pelo menos 3 anos. Se a base já está implementada e em curso, busque por solu??es que diminuem o consumo de recursos da máquina, como particionamento de objetos (tabelas e indices), e se existir uma base cópia (failover), tente direcionar os processos pesados de leitura para essa base.