For scalability, partitioning is great solution but needs some care while deciding the key. 1. To consider what is the data/tables that needs to be partitioned as these must be tables with high data volume and ever increasing. 2. Decision on what should be the partition key. a. In banking industry generally for Enterprise data Warehouse tables, we partition first based on region and then further sub partition based on business date or in some cases on business date only. b. In cases where not possible to define the key, tables are hash partitioned 3. From scalability purpose, compressing/archiving the old data is also good option

Actually i agree with divide data into ranges based on a key, such as dates, to simplify access patterns and improve efficiency.

Defining Data partitions for scalability is only the first step to be prepared for an increasing user base. Two more essential steps need to be addressed effectively: 1) Active and passive data partitions need to be defined according to the business and data retention policies. Passive partitions can be compressed or archived so that total number of partitions would stay within a range that can ensure accepted query performance. Having a huge number of partitions would negatively impact query response performance. 2) Partition pruning needs to be considered as much as possible while writing data queries so that only partitions that have the actual needed data would be accessed, scanned and validated avoiding unnecessary data computations.

In many large-scale solutions, data is divided into partitions that can be managed and accessed separately. Partitioning can improve scalability, reduce contention, and optimize performance. It can also provide a mechanism to divide data by usage pattern. For example, you can archive old data in cheaper data storage. However, the partitioning strategy must be carefully chosen to maximize benefits while minimizing adverse effects.