In this article that I wrote for SearchDataManagement.com, I discuss the fact that as relational database management systems handle more and more data, a strategic approach to data modeling has become vital.
Relational database design tips to boost performance
Despite all the hoopla about Hadoop, NoSQL databases and other big data technologies, relational database management systems continue to be the cornerstone of the IT infrastructure for processing, storing and managing data in most organizations. Companies of all sizes, from those with less than $10 million in sales to ones with $10 billion and more, rely on relational databases to handle their growing data volumes. But if the relational database design process isn't handled properly, the systems are at risk of choking on all the data being fed into them -- and that can have severe consequences for business operations and IT teams.
One of the fundamental considerations in designing databases so they can meet performance expectations is what kinds of applications they'll be supporting. There are three broad usage categories to factor into the design planning and data modeling stages:
- Transactional or business workflow applications;
- Business intelligence applications;
- Data integration processes for funneling information into data warehouses, master data management hubs and other systems.