How does DBMS solve the problem of traditional file environment

In this guide, you will learn advantages and disadvantages of DBMS. We will first discuss what is a file processing system and how Database management systems are better than file processing systems.

Drawbacks of File system

• Data redundancy: Data redundancy refers to the duplication of data, lets say we are managing the data of a college where a student is enrolled for two courses, the same student details in such case will be stored twice, which will take more storage than needed. Data redundancy often leads to higher storage costs and poor access time.
• Data inconsistency: Data redundancy leads to data inconsistency, lets take the same example that we have taken above, a student is enrolled for two courses and we have student address stored twice, now lets say student requests to change his address, if the address is changed at one place and not on all the records then this can lead to data inconsistency.
• Data Isolation: Because data are scattered in various files, and files may be in different formats, writing new application programs to retrieve the appropriate data is difficult.
• Dependency on application programs: Changing files would lead to change in application programs.
• Atomicity issues: Atomicity of a transaction refers to “All or nothing”, which means either all the operations in a transaction executes or none.

  For example: Let’s say Steve transfers 100$ to Negan’s account. This transaction consists multiple operations such as debit 100$ from Steve’s account, credit 100$ to Negan’s account. Like any other device, a computer system can fail lets say it fails after first operation then in that case Steve’s account would have been debited by 100$ but the amount was not credited to Negan’s account, in such case the rollback of operation should occur to maintain the atomicity of transaction. It is difficult to achieve atomicity in file processing systems.

• Data Security: Data should be secured from unauthorised access, for example a student in a college should not be able to see the payroll details of the teachers, such kind of security constraints are difficult to apply in file processing systems.

Advantage of DBMS over file system

There are several advantages of Database management system over file system. Few of them are as follows:

1. What are the problems of managing data resources in a traditional file environment, and how are they solved by a database management system?
   Traditional file management techniques make it difficult for organizations to keep track of all of the pieces of data they use in a systematic way and to organize these data so that they can be easily accessed. Different functional areas and groups were allowed to develop their own files independently. Over time, this traditional file management environment creates such problems as data redundancy and inconsistency, program–data dependence, inflexibility, poor security, and lack of data sharing and availability. A database management system (DBMS) solves these problems with software that permits centralization of data and data management so that businesses have a single, consistent source for all their data needs. Using a DBMS minimizes redundant and inconsistent files.
2. What are the major capabilities of DBMS, and why is a relational DBMS so powerful?
   The principal capabilities of a DBMS include a data definition capability, a data dictionary capability, and a data manipulation language. The data definition capability specifies the structure and content of the database. The data dictionary is an automated or manual file that stores information about the data in the database, including names, definitions, formats, and descriptions of data elements. The data manipulation language, such as Structured Query Language (SQL), is a specialized language for accessing and manipulating the data in the database. The relational database is the primary method for organizing and maintaining data today in information systems because it is so flexible and accessible. It organizes data in two-dimensional tables called relations, with rows and columns. Each table contains data about an entity and its attributes. Each row represents a record, and each column represents an attribute or field. Each table also contains a key field to uniquely identify each record for retrieval or manipulation. Relational database tables can be combined easily to deliver data required by users, provided that any two or more tables share a common data element.
3. What are some important database design principles?
   Designing a database requires both a logical design and a physical design. The logical design models the database from a business perspective. The organization’s data model should reflect its key business processes and decision-making requirements. The process of creating small, stable, flexible, and adaptive data structures from complex groups of data when designing a relational database is termed normalization. A well-designed relational database will not have many-to-many relationships, and all attributes for a specific entity will only apply to that entity. It will try to enforce referential integrity rules to ensure that relationships between coupled tables remain consistent. An entity relationship diagram graphically depicts the relationship between entities (tables) in a relational database.
4. What are the principal tools and technologies for accessing information from databases to improve business performance and decision making?
   Powerful tools are available to analyze and access the information in databases. A data warehouse consolidates current and historical data from many different operational systems in a central database designed for reporting and analysis. Data warehouses support multidimensional data analysis, also known as online analytical processing (OLAP). OLAP represents relationships among data as a multidimensional structure, which can be visualized as cubes of data and cubes within cubes of data, enabling more sophisticated data analysis. Data mining analyzes large pools of data, including the contents of data warehouses, to find patterns and rules that can be used to predict future behaviour and guide decision making. Text mining tools help businesses analyze large, unstructured data sets consisting of text. Web mining tools focus on analysis of useful patterns and information from the World Wide Web, examining the structures of Web sites and activities of site users as well as the contents of Web pages. Conventional databases can be linked via middleware to the Web or a Web interface to facilitate user access to an organization’s internal data.
5. Why are information policy, data administration, and data quality assurance essential for managing the firm’s data resources?
   Developing a database environment requires policies and procedures for managing organizational data as well as a good data model and database technology. A formal information policy governs the maintenance, distribution, and use of information in the organization. In large corporations, a formal data administration function is responsible for information policy, as well as for data planning, data dictionary development, and monitoring data usage in the firm. Data that are inaccurate, incomplete, or inconsistent create serious operational and financial problems for businesses because they may create inaccuracies in product pricing, customer accounts, and inventory data and lead to inaccurate decisions about the actions that should be taken by the firm. Firms must take special steps to make sure they have a high level of data quality. These include using enterprise-wide data standards, databases designed to minimize inconsistent and redundant data, data quality audits, and data cleansing software.

How the DBMS help solve these problems?

How are those problems of managing data resources in a traditional file environment solved by a database management system?

How does DBMS resolve the limitation of file based system?

What are the benefits of DBMS over traditional file environment?