When you faced a VLDB database , you should consider some important notes: -Make a preferred Archiving method base on your business and table relationships such as In-Archiving in Oracle -Always take a standby database that synchronized with primary, because backup from Vldb database has many challenges. -if possible keep only live data in primary and release older partitions.

In my experience if suddenly the database is expanding rapidly we need to discuss with development team, - if it's normal for example because of our userbase suddenly explode because viral promotion, we need to discuss with development team the behavior of data and apply scalable strategies if possible. - if it's not normal for example because of some new feature, is it really need to be save in database and is it in correct format maybe it's better in non relational database, maybe if it's for communicate between it can be better as messaging queue, or instead of saving BLOB put it as file in Cloud Storage like in my post.

To innovate while ensuring stability during rapid database expansion: 1. Scalability: Use distributed databases, sharding, and caching. 2. Reliability: Implement backups, replication, and high-availability architectures. 3. Safe Rollouts: Use feature flags, staging environments, and blue-green deployments. 4. Optimization: Regularly optimize queries, schema design, and indexing. 5. Automation: Automate deployments, testing, and scaling via CI/CD pipelines. 6. Monitoring: Use tools to track performance and set alerts for anomalies. 7. Security: Enforce encryption, RBAC, and regular patching. 8. Collaboration: Promote cross-team knowledge sharing and planning. These steps ensure robust, scalable growth while enabling innovation.

Sometimes your database might grow more than the forecast due to ever changing business needs. As a member of the technology team, you also need to align with this. In database level following steps will be helpful: 1. Query performance - increasing volume of data will inversely impact your query performance. Tweak your query, execution plan, index etc regularly. 2. Hardware - monitor your CPU, memory and storage regularly. Ensure there is no bottle neck here. 3. Optimize database tables. Remove historical data from your most transactional tables. 4. Implementing data caching at application level where feasible. 5. Ensure regular database backup and restore process.