Here's how you can enhance system performance through creative database design.

The most famous DBA answer for this is. It depends, is this a reporting db? Is it a transactional app db? Is it a data warehouse? The overall effectivness of all of these tips will depend on the databases intention. It will also depend on usage. Databases are constant gardening, you are always pruning and controlling and these decisions change based on data use. For perf in a heavily transactional db RCSI might be the best choice but if it’s a reporting db read only db with tuned indexes for reads might be best. And over time these might be revisited constantly as usage changes and needs arise.

We can use Normalization Techniques to reduce data redundancy. using of indexes will improve DB performance a lot. using clustered indexes for frequently accessing columns and non-clustered indexes for columns used for updations. Usage of Stored Procedures,it will improve performance by retaining query execution plan.

Effective database design can significantly boost system performance. Normalize your data to reduce redundancy and improve integrity, but avoid over-normalization that can slow queries. Use indexes smartly; they speed up retrieval but add overhead to data modifications. Partition large databases to improve performance and simplify maintenance, and optimize queries to minimize response times. Implement caching to reduce database load and access times. Finally, consider NoSQL databases for unstructured or rapidly changing data. Balancing these strategies will enhance your database's efficiency and scalability.

Database partitioning is a technique used to split data in a large database into smaller chunks called partitions. Types of partitioning: 1. Vertical partitioning - columns of a table are divided into partitions with each partition containing one or more columns from the table. Usage: This approach is useful when some columns are accessed more frequently than others. 2. Horizontal Partitioning - by splitting the table into rows based on the partition key. Partition made based on Range-based (Eg: date range), List-based (Eg: geo-based), Hash-based Usage: While handling large datasets, horizontal partitioning is prefered Advantages: => Improve scalability => Improve availability => Improve performance

In my experiences, there are ways to optimize the SELECT statement: limit SELECT * and always add WHERE in the SELECT statement. If not necessary, do not use DISTINCT

Consider to upgrade the technology. In rdbms for example, along its history, oracle was once index is heavily needed and you need to wisely select which column to be indexed and later on, it evolved and improved the way it access the data where index is not really needed. Because it has the intelligence to wisely managing it. Same thing with partition etc. So you always need to test and find out it's effectiveness. Is partition more efficient than without? Same thing with indexing, you need to test and user experience is always the best to judge.