What are the best ways to scale databases for growth?

Advantages of Horizontal Scaling: It is easy to upgrade It is simple to implement and costs less It offers flexible, scalable tools It has limitless scaling with unlimited addition of server instances Disadvantages of Horizontal Scaling: Any bugs in the code will become more complex to debug and understand The licensing fee The cost of the data center will increase Advantages of Vertical Scaling: The cost of the data center for the space, cooling, and power will be smaller It is a cost-efficient software It is easy to use and implement The resources for this approach are flexible Disadvantages of Vertical Scaling: The cost may be low, but you will need to pay for a license each time you scale up The hardware costs more

In every database choice, it always boils down eventually to the use-case scenario. If you have a steady-state, low to mid-range volume in terms of sizing, HA and manageable SLAs, one can start small and vertically scale-up and as when time demands it. On the other hand, if you're building cloud-native OLTP apps which are catering to real-time OR interactive use-cases, with massive concurrent connections, high volume and low latency scenarios, in such a case, you need to look at NoSQL databases which allows you use SDKs and leverage specific micro-optimizations for high perf low-latency CRUD calls. The choice eventually boils down to end-user experience expected, SLAs for HA, replication and global availability.

My experience says that often the environment in which you are inserted, and how much this environment trusts a database technology, be it SQL or NoSQL, the number of specialists that the company you are in has for this technology, your level of knowledge in the technology you are going to use and putting all this together, it is often safer for you to make the decision based on your environment and not necessarily on what you want to do technically, which is why databases like PostgreSQL that are highly extensible often offer a good way out for different types of solutions, of course PostgreSQL is not a silver bullet but if you have doubts about which type of technology is best for your service, I would play safe.

In my experience, analyze the query patterns and workload of the database to identify the most frequently used and critical queries. Use execution plans to understand how the database management system executes the queries and where the bottlenecks are. Choose the right type of index for the query, such as B-tree, hash, or bitmap, depending on the data distribution and query type and avoid over-indexing, which can increase storage requirements and slow down insert/update operations. Partition the data into smaller and more manageable chunks based on logical criteria, such as date, region, or category. Use buffer cache to store frequently accessed data in memory, which can reduce disk I/O and improve query speed.

An example I have experienced is at high volume call center. The database was full of hundreds of customers being in the system multiple times. This made it difficult to find the correct order being processed. Especially if the name was common. Doing a through search for the customers. When This would happen I didn't have the capability to remove the duplicate. Making many of same name filling our database up. Typing errors would also effect this as when others would look for customers and didn't find them they would then add that name again.

Nowadays organizations are migrating their mission-critical databases from onprem to cloud. Businesses need to ensure they have enough resources for their new setting and their application is compatible with it and also have good methods to safeguard their confidential data in the cloud. Moreover, companies may have to spend more time and money on educating their staff so that they can recognize and handle any possible threats. Before starting their transition, businesses need to thoroughly check all the applicable rules and laws to make sure they stay compliant during the whole process. Also, they should keep monitoring frequently after launching to keep following the regulations and avoid any expensive fines or sanctions later on.

Use partitioning. It can significantly decrease amounts of reads. Be ready to adjust queries in order to use partitioning. If you use RLS (row level security), try to make it more efficient using partitions. Enjoy using partitioned covering filtered indexes, they are fast.