Five Concepts in Database Systems
- Database Modeling
- Data Management
- Integrity Rules
- Backup and Recovery
- Data Security
Database systems and management govern an extraordinary amount of everyday life. Database systems house healthcare and banking data, marketing and demographic data, and a vast amount of other types of information that inform our day to day lives, both personal and professional.
As a result, the creation and maintenance of database systems have become an enormous demand – and more and more people are studying data science and database management to take advantage of the hundreds of thousands of jobs available in this arena. According to the Bureau of Labor Statistics, the field of data science and database administration is growing faster than ever.
For data science students who want to specialize in database systems, a number of unifying concepts and areas of study are introduced in scholastic programs. Here are five principles of database systems.
1. Database Modeling
Database modeling determines the structure of a database and methods of storage and fetching data housed within it. Database models also determine how information is organized. There are a number of different database models used in contemporary data science for different purposes. Logical internal data structures include the hierarchical (tree-based) model, star schema, and document models, among many others. Physical data structures include the inverted index and flat-file models.
2. Data Management
The administrative end of database systems mostly comprises data management. Methods of data management indicate how to acquire and store data, process it effectively, validate it, and ensure its security. Data management umbrellas a large array of topics across many disciplines, including data privacy, ethics, system architecture, flows, and business planning.
3. Integrity Rules
The practice of establishing integrity rules informs database system managers on how to securely store, process, and retrieve data while simultaneously ensuring its accuracy and format over the lifetime of its storage and usage. Both the physical and logical integrity of the data is vital, which means database managers who establish integrity rules must have ample competence with both logical and physical data structures.
4. Backup and Recovery
Data backup and recovery is part and parcel to data management. Its necessity means that challenges in data security and storage are presented, as the backup must carry the same integrity as the original datasets, and having secure data in more than one place presents an additional vulnerability. Backup and recovery specialists must have high levels of skill in logical and physical data structures, data integrity and security, and data privacy and access methods.
5. Data Security
Last but certainly not least, data security is of the utmost importance in data systems. Data security analysts must work across a broad array of tools and best practices to secure stored data, including limiting access to stored data with front-end interfaces, flow monitoring, storage monitoring, and much more. Because database security is of such high importance, many jobs in database management that involve cybersecurity are very highly paid.
These database systems principles, among others, each embody different specializations and different areas of skill, and each represents different challenges. No matter the adeptness and interest of the database student, each of these principles are worth exploring for specialization in professional practice.