1. Data dictionary: is used to store metadata about the structure of the database, the schema of the database. A Data Dictionary is a collection of names, definitions, and attributes about data elements that are being used or captured in a database, information system, or part of a research project. For example, a bank can create a data dictionary for a group functions like account holder and available credit.
2. Metadata: this is information about the data collected. Metadata “includes information about technical and business processes, data rules and constraints, and logical and physical data structures.” For example, a wrapper that has an item that describes it like how the packaging tells about the food in a box.
3. Data mining refers loosely to the process of semi automatically analyzing large databases to find useful patterns. Data mining refers to the process of analyzing data to discover patterns to make business decisions. For example, when you buy a product on Amazon you are shown a list of recommended products, this is done through the study of past data and behaviors.
4. Data Model: “A data model is a set of data specifications and related diagrams that reflect data requirements and designs.” A data model can be used to explain the design of the database and they are categorized into three levels which are physical, logical and view levels. For instance, when a university builds a data model for a student and staff record system to provide help to students studying online.
5. Normalization: this is another way for designing a relational database. It helps us to store information in a way that avoids redundancy and allows us to get information easily. The approach is to design schemas that are in an appropriate normal form. Normalization is the process of organizing a database to reduce redundancy and improve data integrity.
6. Physical Data Independence: This mean the physical schema can be changed without it impacting the conceptual and logical schema for optimization purposes. Conceptual structure of the database would not be affected by any change in storage size of the database system server. For example, the storage system can be changed but it should not affect the logical data
7. Schema: is the overall structure of the database. There are many schemas in a database system that can be partitioned based on abstraction level. There are two types of schema, which are: physical schema (the physical structure of the database) and logical schema. Schemas are rarely changed, if at all.
8. Database Administrator: is the person that plays an important role in managing the database. The Database Administrator needs to ensure that the server is always up and make sure that users have access to the information they need at any time. The DBA makes sure that the database is protected and that any chance of data loss is minimized.
9. Data inconsistency: This is when there are different versions of the same data are in different places which makes it not to agree. For example, there is data inconsistency between the files if the same data is stored in different formats in two files or if data must be matched between files.
10. Two and three tier database architectures: Two-tier database architecture includes an Application layer between the user and the DBMS, which is responsible to communicate the user’s request to the database management system and then send the response from the DBMS to the user. Three-tier database is an extension of two-tier database architecture, an additional Presentation or GUI Layer is added, which provides a graphical user interface for the End user to interact with the DBMS.