We’ve probably heard the term “database” a lot. This word places a strong focus on its arms. It is more frequently used with non-tech groups or communities than with developer groups or communities. A database, technically, is more of a storage phrase used to express the relationship with many types of data that are consolidated in a single location. As a result, we can define a database as an ordered collection of data that is typically stored and retrieved electronically using computer systems. This article is heavily focused on database design, and its association with citable terms and approaches was widely considered. To comprehend the bits and pieces, we’ll go over those database design terms.
Database design is a set of tasks or processes that improve the design, development, deployment, and maintenance of enterprise data management systems. The cost-effective methods in terms of disk storage space are strongly influenced by designing a suitable database, which reduces maintenance costs while enhancing data consistency. As a result, there must be a wonderful idea for constructing a database. The designer must adhere to the limitations and determine how the elements must correlate and what type of data must be kept.
The primary goals of database design are to create physical and logical models of the proposed database system.
To elaborate, the logical model is primarily focused on data requirements, and considerations must be made in terms of monolithic considerations, and thus the stored physical data must be stored independently of physical conditions. The physical database design model, on the other hand, involves a translation of the logical database design model by maintaining control over physical media using hardware resources and software systems such as Database Management System (DBMS).
A well-designed relational database will assure the accuracy, consistency, and dependability of your team’s data. With these four design tips, you can get the most out of your database.
Perhaps you need to streamline the operations of your team or ensure that numerous teams are working from a single source of truth. Maybe you’re in charge of data quality in your department, or you have another data-related task. Whether you’re new to databases or an experienced hand at SQL, a refresher course on relational database architecture is likely to be beneficial.
Relational databases are used by both large and small enterprises to more efficiently store, manage, and analyze essential information for applications as diverse as customer relationship management, content generation, product planning, UX research, and many more.
However, not all relational databases are made equal. A poorly built database may make it more difficult to retrieve the information you require or compromise the accuracy of your data; on the other hand, a well-planned database offers various advantages:
• You can avoid data that is redundant, duplicate, or invalid. Problematic data can jeopardize the validity of your database, but you can build your relational database to reduce the dangers provided by bad data.
• You can avoid instances in which needed data is missing. If you can establish which types of data are most important to your process ahead of time, you may build your database so that it enforces appropriate data entry or warns users when records are lacking crucial data.
• The database structure is simple to change and maintain. Workflows rarely remain static indefinitely, thus you will almost certainly need to make changes to your basic relational data architecture in the future.
Fortunately, a well-designed database ensures that any changes you make to fields in one table do not have an undesirable effect on other tables.
• The data is simple to edit. Similarly, a well-designed relational database ensures that changes to the values of a given field in one table do not have an unfavorable effect on other fields in that table.
• It is easier to find the information you require. It is considerably easier to query your database if you have a consistent, logical database structure (that avoids duplicate columns and tables).
• You can devote less time to database maintenance and more to other tasks. The finest database is one that requires no maintenance.
• Domain integrity (or field-level integrity) assures that each field’s purpose is obvious and identifiable, as well as that the values in each field are valid, consistent, and correct.
• Referential integrity (or relationship-level integrity) assures that the relationships between tables are sound, ensuring that the records in the tables are synchronized whenever data is inserted, changed, or deleted from either table.
Every organization approaches its work differently, and as a result, each organization has its own set of data needs, often known as business rules. If your organization reaches the size of Microsoft, you will use databases differently than a small video production company. Even within video providers, though, business regulations are rarely the same. Assume your video company requires millisecond timestamps for video files, and each video client is assigned an eight-character alphanumeric code.
Ideally, your team’s database should be able to enforce your specific business rules, ensuring that the data entering your database is both correct and helpful. For example, if the video files in the above example were recorded in seconds rather than milliseconds and then entered into the company database, it could cause a huge stumbling block in video production—as well as a mess for a database administrator to clear up afterward.
Step-by-step instructions for creating an effective database
If this all sounds new or intimidating, don’t worry—there is a methodical approach you can take to verify your relational database management system (or RDBMS) adheres to excellent design principles, is well-suited to the objectives of your organization, and avoids common hazards.
Before you begin your database design journey, it’s vital to take a step back and answer a critical question: “Why am I developing this database?”
“Are you creating this database to manage commercial transactions?” To keep track of customer IDs? To address a specific organizational issue? In any instance, it’s important to consider the intended function of the database you’ll be developing. You might even want to collaborate with managers, leadership, and end users to create a mission statement for your database, such as “The purpose of the Mingei International Museum database is to maintain data for our art collection,” or “Zenbooth’s database will store all of the data for our manufacturing resource planning.”
Furthermore, you should specify the database’s end users’ objectives: which precise actions will the end users need to undertake in order to complete their work? Creating a specific list of goals, such as “always know the status and location of each piece of art in our collection,” or “keep a complete customer table that shows records for each of our clients.”
As you proceed through the design process, this will assist you in determining a suitable structure, or database schema, for your information.
Before you begin constructing your database, you must first examine the data fundamentals and requirements of your firm. This may sound overwhelming, but all it really means is that you’ll be evaluating how your team currently accomplishes its work and determining what types of data are most relevant to that work. This can be accomplished by thoroughly evaluating existing processes and interviewing team members—both management and end users. Here are some questions to consider while you conduct your research:
• How does your company now collect data? Do you use spreadsheets? What about paper templates? Is there another database?
Whatever approach you use, find the fullest samples of work you can and go through them to find as many different attributes as possible. For example, your editorial calendar may be currently housed in a spreadsheet with columns labeled “Author,” “Due Date,” “Editor,” and so on.
• How does your company now present data? What types of reports does your company use? PDFs? What are slide decks? What are web pages? Examine and use any forms of presentations that use data from your current data collection methods to identify potential fields.
• What data are your team members currently using?
The easiest way to address this question is to speak with team members—both management and end users—to discover current data use patterns and case studies, as well as any holes in the current system. You can inquire, “What types of data are you currently using?” ” and have them look at the samples you gathered. Importantly, these interviews can provide light on the organization’s future growth objectives, providing insight into future information requirements and the type of relational database model that would be the greatest fit.
Following the determination of your organization’s purpose and objectives, as well as the analysis of your data requirements, the next stages are to extract a list of entities and a list of attributes from the body of research you’ve accumulated. An entity in relational databases is an object, person, location, event, or idea, such as “customers,” “products,” “projects,” or “sales reps.” Attributes, such as “name,” “quantity,” “address,” “phone number,” or “genre,” are defining qualities of those entities. One way to think about this is that entities are nouns, and attributes are adjectives that describe those nouns.
Begin by making a list of the entities you discovered during your investigation. These entities will eventually act as a guide to assist you in defining your tables later in the design process, but they will also assist you in identifying the qualities required to generate your list of fields. For instance, if you were creating a talent database for a record company, your entity list may look like this:
Then, make a separate list with the relevant properties for each of the entities you’ve discovered, as well as any dependencies the attributes may have on one another. These attributes will be used to define the fields in your tables. Again, using the talent database as an example, your attributes list may look like this:
- Artist Name
- Artist Phone Number
- Agent Name
- Agent Phone Number
- Agent Email Address
- Venue Name
- Venue Address
- Gig Dates
After you’ve compiled a preliminary list of attributes, read over it and edit it to ensure that it appropriately represents your organization’s informational needs.
• If many qualities have distinct names but convey the same concept, combine them into one. For example, if your list has both “Product No.” and “Product Number,” you should eliminate one of them.
• If numerous attributes have the same name but represent different concepts, rename them to be more explicit. You may, for example, rename two separate “Name” properties to the more descriptive “Artist Name” and “Venue Name.”
After you’ve refined your lists, it’s a good idea to check over them with some of the team members you interviewed to ensure that you’ve included every necessary sort of data. Take their suggestions into account and tweak your listings as needed.
After you’ve created your lists of entities and characteristics, it’s time to use them to build the structure of your relational database. Your list of entities will be separated into different tables in your database, and your list of attributes will be used as fields in these tables.
Take your lists and give each attribute to one of your tables. For example, once we’ve finished assigning our mentioned attributes to our new tables, our planned talent management database would look like this:
Then, for each table, select a suitable primary key field. A primary key is an important component of data integrity because it uniquely identifies each record within a database and is used to construct various types of links across tables.
The primary key field in each table should match the following requirements:
• It must include unique identifiers. This prevents the creation of duplicate records and redundancy inside a table.
• It cannot have any null values. Because a null value is the lack of a value, it cannot be used to identify a record.
• It should not be a value that needs to be changed frequently. Primary key values should ideally remain relatively constant throughout time, changing only in exceptional circumstances.
• It should not contain any sensitive personal information. Although social security numbers, passwords, personal health information (PHI), and other sensitive data types are unique, they should not be utilized as primary keys since doing so makes keeping this data securely secured in a single location much more difficult.
• Ideally, it incorporates the table name into its own name. While it is not absolutely necessary, including the table name in the primary key field name can help identify the table from which the main key field originated.
“Employee Name,” for example, is clearly identified as originating from the associated database “Employees.”
It’s possible that your preliminary list of fields for a specific table does not include a single field that matches all of these requirements. You may, however, be able to merge two or more fields to create a separate, unique field that meets all of these criteria. For example, you could use a concatenation formula to merge the data in a “Given Name” and a “Surname” column to create a third, calculated “Full Name” field.
If this technique does not work for you, you may always create a new field designated particularly for unique identification codes to serve as your primary key field. For this purpose, fields such as “Product ID” or “Sales Invoice Number” are frequently generated.
Let’s go back to the example of the talent management database. The “Artist name” field is already a solid option for the primary key in the “Artists” table, as it’s unlikely that your record company will sign two artists with the same name. The primary key for the “Venues” database can also be “Venue name.” However, for the other tables, it is usually better to create new fields that concatenate values from existing fields. We could add a new field to the “Agents” database called “Agent complete name” that concatenates the values of the “Agent given name” and “Agent surname” fields.
Because an artist may play at the same venue on several occasions for the “Gigs” table, we should create a new field that offers a unique name to the specific combination of an artist at a venue on a given date. You might conceivably concatenate the artist’s name, the venue’s name, and the date to construct values like “2 Linkz at the Gotham City Metro Club, 02/13/2019,” but this can quickly become long and awkward. You could also try adding a new field called “Gig code” with unique alphanumeric code values (like “E0023”). It is up to you and your team’s individual needs to determine which method to adopt.