The Overview of a Database Life Cycle

February 4, 2021

Introduction

It is frustrating when there is tons of data being used by many people within an organization and there is no place to store it. It becomes difficult to manage such data, secure it, and store it accurately. This is where a database come into play.

A database prevents duplication and loss of data. While designing any information system, a database is a basic component. This article will outline the stages of a database development life cycle (DBLC). The reader will understand the need for a database and the database life cycle.

Table of contents

The need for a database

A database is a collection of data that is organized. Data from the database is accessed from computer systems electronically. The data represents real-world entities in the form of text, numbers, audio, and images. A database is created to solve a problem.

A few problems that a database solves can include:

  • The size of data.
  • Ease of updating data.
  • Redundancy.
  • Data security.
  • Incomplete data.
  • Accuracy.

A database handles the storage of data, retrieval, and the updating of data.

Large and small organizations depend heavily on databases to manage and organize their data. A good database is important to any company. Having a centralized location to store data enables you to know what is occurring within your business.

A database is important because it:

  • Manages large amounts of data.
  • Enables data security.
  • It is accurate.
  • Easy to update.
  • Easy to access data.
  • It ensures data integrity.

Data modeling

A data model is a visual representation of entities, attributes, and relationships between different entities.

A developer uses a data model to outline the data a database will hold and how it is organized. Data modeling is the first phase of the database design process.

Data modeling is the practice of designing a data model that will be used to store data in a database. Data modeling allows developers to define and organize the company’s business processes efficiently.

Data modeling is a crucial step when creating a database for any information system. It helps developers understand the system’s set up and organize the work appropriately.

Here are a few benefits of effective data modeling:

  • Reduces the cost of building an application and helps achieve fast time to value.
  • High quality – Developers should consider data modeling before building a system. Various projects fail due to premature coding. A data model helps illustrate the problem, enabling the developer to consider different approaches and chose the best option.
  • Good documentation – Data models document vital concepts, by providing a foundation for endless maintenance.
  • Reduced errors – The systems development begins with a clear understanding of the problem. Although the developers can still make errors while developing the system, it will be easier to resolve the errors and conflicts with a data model in place. This is because data modeling enables the developers to understand the system well.
  • Successful design and implementation of databases.
  • Improves staff collaboration – The technical team can easily collaborate with the non-technical team. With the use of data models, the non-technical team can easily understand how the system works. Both teams gather enough information on how they will create the database.

Database design

The second stage of DBLC focuses on the design of the database. Database design is the most critical phase in the database life cycle. At this phase, the developer makes sure that the final product meets the user and the system requirements. This phase focus on the operational and business requirements of the organization.

Phases in database design include creating a conceptual design, a logical design, and a database system’s physical design. Conceptual design is created based on the needs analyzed at the data modeling stage. Conceptual design typically involves creating an entity-relationship diagram (ERD). An ERD presents data in the form the tables. This data includes entities, attributes, primary keys, and how they relate to one another.

The tables outlined in the ERD are then normalized. Database normalization resolves problems associated with the database design. Therfore a developer can access data quickly and efficiently.

The conceptual design is then transformed into a logical design. The logical design phase gives a more detailed vision of a database than that of a conceptual design. The logical design phase is then converted to physical design.

Physical design is the last phase of the database design. The physical design optimizes the operations, performance, ensures data integrity, and prevents redundancy in the database. Approaches used in database design is a top-down approach and a bottom-up approach.

Database implementation

Database implementation involves the construction of a database according to how it was designed.

In this phase, you implement all the design specifications. First, you have to select the database management system (DBMS) to use. Database implementation is influenced by your choice of DBMS, database tools, and the operating environment. Data then has to be migrated to the new database system. Migrating data is a critical step, for it requires a lot of care, consideration, and planning.

Database testing

At this phase, the database administrator (DBA) conducts tests. This is to ensure that the database is working as expected. Testing gives the DBA assurance that the components interact properly to meet the requirements.

The DBA also makes sure the database maintains integrity and data security. The DBMS applies data integrity through the appropriate use of primary and foreign keys. In database testing, database security involves password security, authentication, and data encryption.

Operation and integrating the database with the application

Once the database takes place and works effectively, it is considered operational. Data can be inserted, selected, updated, retrieved, and deleted from the database.

Database integration is the process of combining information from multiple sources such as data warehouses, databases. Database integration makes data accessible to multiple users without having to move or duplicate the data.

For instance, organizations store customer data in Amazon servers (AWS) and sales data in salesforce. By integrating the databases, users can access combined data from one database or a data warehouse.

Database maintenance and monitoring

The DBA must regularly maintain and monitor the database. The DBA makes sure that all tasks run properly to improve database performance.

There should be enough storage space, no data errors, and the database should be up to date. Here are database maintenance activities:

  • Preventive maintenance (backing up data).
  • Corrective (restoring).
  • Adaptive maintenance (inserting data, updating data, checking data integrity, and enhancing performance).

Database monitoring tracks the database performance. This is to maintain its high performance, data availability, and security.

Conclusion

The Database Life Cycle is a sequence of stages for developing a database system. The stages of DBLC logically follow each other. Developers have to follow the cycle while working on the database.

In this article, we have learned about database development and the functionalities of each stage. I hope this article will give you an understanding of the database life cycle.


Peer Review Contributions by: Adrian Murage


About the author

Judy Nduati

Judy is a student pursuing Business Information Technology. She is passionate, self-motivated, and a solution-oriented technology enthusiast. She is also a front-end web developer and a web designer. She loves technical writing and contributing to open-source projects.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.