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  • 2.3 Converting ERD to a relational model
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2.3. Converting ERD to a relational model ¶

In this chapter we explain the process of creating a relational database from an entity-relationship model. While many steps are largely mechanical, a number of decisions need to be made along the way. We will explore the trade-offs for each decision. We will use the computer manufacturer data model from Chapter 2.2 as our example.

This chapter assumes you are familiar with the basics of the relational model of the database, including tables and primary and foreign key constraints. The necessary foundations are covered in either Part I (Chapters 1.1 and 1.7 ) or Part III ( Chapter 3.1 ).

There are many ways to represent the relational database: logical or physical data models ( Chapter 2.4 ), text or tabular descriptions, or SQL code. Which you use will depend on your development process and needs. In this chapter, we will provide simple text descriptions in tabular format.

We start with the basic conversion rules, converting pieces of our example data model as we go. The full set of tables resulting from our conversion is given at the end of the chapter.

2.3.1. Entities ¶

The first step in building a relational database from an ERD is creating a table from each entity in the data model. Weak entities need slightly different handling than regular entities, so we will address them separately, starting with regular entities.

2.3.1.1. Regular entities ¶

First, decide on a name for the table - this does not have to be the same as the entity name! There are many naming schemes for tables. If you are building a database for a company or organization that has naming standards, you will of course want to follow those. Otherwise, choose a basic approach and be consistent. For example, some databases use plural nouns for tables, while others use singular nouns. In our data model from Chapter 2.2 , the entity employee might become a table named employee or employees . Another naming issue arises with table names containing multiple words; some databases choose to run these together, while others employ underscore characters. For example, the entity assembly line could become a table named assemblyline or assembly_line . In our examples below, we will use singular nouns and underscores.

Most attributes for the entity should be converted to columns in the new table. Do not create columns for derived attributes, as these values are not intended to be stored. Do not create columns for multivalued attributes; we will address these later. For composite attributes, create columns only for the component attributes, not the composite itself. As with entities, you will need to decide on a name for each new column, which does not have to be the same as the attribute name. You will also need to specify a type and any constraints for the column. Determining appropriate types for some columns may require consultation with your data domain experts. Constraints may be added as appropriate. In the descriptions below, we will use simple type and constraint descriptions, rather than SQL syntax.

Choose a key attribute (every regular entity should have at least one) and use the column created from it as the primary key for the new table. If the entity has multiple key attributes, you will need to decide which one makes most sense as a primary key. Simpler primary keys are usually preferred over more complex ones. If desired, you can constrain the columns resulting from other keys to be not null and unique similar to primary key columns. For example, an employee table might use a company generated ID number as its primary key, and also include a column for a government issued ID number which we would want to constrain to prevent duplicates.

Here is our ERD depiction of the employee entity:

Here is a preliminary conversion of the employee entity into a relational table named employee :

This is not yet the final employee table! We will add additional columns to the table when we address the relationships that the employee entity participates in.

2.3.1.2. Weak entities ¶

Weak entities are converted into tables in nearly the same way as regular entities. However, recall that a weak entity has no identifying key attribute. Instead, it has a partial key, which must be combined with the key of the parent entity. In our example, the assembly line entity is weak. Its partial key, the number of the assembly line within a particular factory, must be combined with the factory identity for full identification.

The table created from a weak entity must therefore incorporate the key from the parent entity as an additional column. The primary key for the new table will be composed of the columns created from the parent key and from the partial key. Additionally, the column created from the parent key should be constrained to always match some key in the parent table, using a foreign key constraint.

Here is the ERD of assembly line and its parent entity, factory :

Using the above guidelines, we should create tables factory and assembly_line , and include a column in assembly_line for values from the city column of factory . A good choice of name for these “borrowed” columns is to concatenate the original table and column names together; in our case, this gives us the column factory_city . (We will use the term “borrow” in reference to this process of inserting a column in one table to hold values from the primary key column of a related table.) Here is the preliminary conversion of factory and the final conversion of assembly line :

2.3.2. Relationships ¶

Relationships can be handled using a few different approaches, depending on the cardinality ratio of the relationship. Most generally, we can create a table to represent the relationship. This kind of table is known as a cross-reference table, and acts as an intermediary in a three-way join with the two (or more) tables whose entities participate in the relationship. As we will see, some cardinality ratios permit simpler solutions.

2.3.2.1. Many-to-many ¶

Many-to-many relationships are the most general type of relationship; a database structure accommodating a many-to-many relationship can also accommodate one-to-many or one-to-one relationships, as “one” is just a special case of “many”. The challenge for many-to-many relationships is how to represent a connection from a record in one table to multiple records in the other table. While modern SQL allows array valued columns in tables, not all databases support them. The traditional solution is to create a cross-reference table.

Given a table A and a table B , we create a cross-reference table with columns corresponding to the primary keys of A and B . Each row in the cross-reference table stores one unique pairing of a primary key value from A with a primary key value from B . Each row thus represents a single connection between one row in A with one row in B . If a row in A is related to multiple rows in B , then there will be multiple entries with the same A primary key value, paired with each related B primary key value.

For example, our ERD indicates a many-to-many relationship between the entities vendor and part . A computer part (such as an 8TB hard drive) can come from multiple sellers, while sellers can sell multiple different computer parts:

We create tables vendor and part following the guidelines above, and then create the cross-reference table vendor_part . (It is common to name a cross-reference table using the names of the two tables being related, although other schemes can of course be used.) Note that the supplies relationship also has a relationship attribute, price , which we can incorporate into the cross-reference table. The result, with some fictional data, is pictured below:

Data in the cross-reference table is constrained in several ways. First, we only want to store the relationship between rows once, so we make the combination of primary keys from the related tables into a primary key for the cross-reference table. In our example, the primary key is the combination of vendor_name and part_number . Second, each of the borrowed primary key columns should be constrained to only hold values that are present in the original tables, using foreign key constraints.

Table descriptions for vendor , part , and the vendor_part cross-reference table are given below:

2.3.2.2. One-to-many ¶

As a special case of many-to-many relationships, one-to-many relationships can be implemented in the database using a cross-reference table as above. We have another choice, however. Observing that rows on the “many” side of the relationship can be associated with at most one row from the “one” side, we can choose to capture the relationship by storing the primary key of the “one” side table in the “many” side table.

In our ERD, the employee entity participates in one-to-many relationships with both factory and itself:

There is also a one-to-one relationship between employee and factory , which we will deal with in the next section.

Considering first the works at relationship, we see that each employee works at no more than one factory. Therefore, we can include a column for the factory’s city in the employee table. For consistency with previous choices, we will call this column factory_city . This column should be constrained by a foreign key referencing the factory table.

We also have the supervises relationship to deal with. In the same fashion as above, we should include a column in the employee table containing primary keys from the employee table. However, we should give careful consideration to the name we give this added column; employee_id would be a very misleading choice! A better choice is to consider the role of the employee whose id will be stored, and call the column supervisor_id .

With these changes, the employee table now looks like:

Using a cross-reference table instead of the above scheme is a perfectly valid choice, and may be preferable if there is any chance the data model might change such that the one-to-many relationship becomes many-to-many. In our example ERD, a given computer model is built at only one factory (while factories can build multiple different models); however, it would not be surprising if, at some point, we want to allow for models to be built at multiple locations. We might choose to use a cross-reference table for the relationship between factory and model in anticipation of this possibility.

2.3.2.3. One-to-one ¶

One-to-one relationships can be considered a special case of one-to-many relationships, so you can utilize either approach suitable for one-to-many relationships. In most cases, it will be preferable to borrow the primary key from one table as a foreign key in the other table. Using this approach, you could borrow from either side; however, one choice is often preferable to another.

In our example, we have a one-to-one relationship, manages , between employee and factory . We could therefore add another column to the employee table, this time for the city of the factory that the employee manages. However, most employees do not manage factories, so the column will end up containing many NULL values.

On the other hand, every factory should have a manager (implied by the total participation of factory in the relationship). It makes perfect sense, then, to add a column to the factory table for the employee managing the factory. This is another situation in which it makes sense to name the column for the role of the employee in this relationship, so we will call the new column manager_id .

Here is the completed factory table:

In some rare cases, it may make sense to handle a one-to-one relationship by simply merging the participating tables into one table. This should probably be reserved for situations in which both entities have total participation in the relationship.

2.3.2.4. Higher arity relationships ¶

For relationships with three or more participants, a cross-reference table incorporating primary keys from each of the participating tables is the best choice.

2.3.2.5. Identifying relationships ¶

Identifying relationships for weak entities are necessarily one-to-many or one-to-one. However, the conversion of the weak entity already incorporates a column containing primary key values from the parent table. This suffices to capture the relationship.

2.3.3. Multivalued attributes ¶

Multivalued attributes can be used to model a few different scenarios. As a result, there are multiple choices for how to store multivalued data in a relational database.

In the simplest case, a multivalued attribute is used when a list of arbitrary values needs to be stored, but there is no particular expectation that the values will be examined in a search of the database. In this case, an array-valued column may be an appropriate choice for databases that support such columns.

When there is a need to query the values associated with a multivalued attribute, or for databases that do not support array-valued columns, the best choice may be to make a simple table with two columns, one for the primary key of the owning table, and one for the values themselves. Each entry in the table associates one value with the instance of the entity.

In our example, computer models can be marketed to customers for different applications, such as gaming, video editing, or business use. This is represented in our data model with the multivalued application attribute:

We might, then, implement the model entity and its attributes using the following two tables:

Many applications also require the values associated with a multivalued attribute to be restricted to a certain list of values. In this case, an additional table is used. The additional table exists just to contain the allowed values, allowing us to constrain the data to just those values. For more complex values, an artificial identifier may be added as a primary key, and the primary key used in the multivalued attribute table instead of the values themselves, in which case the multivalued attribute table becomes a cross-reference table. For small lists of simple values (as in our example), this adds unnecessary complication.

For our example, we will constrain the application column using a foreign key constraint referencing this simple table:

2.3.4. Full model conversion ¶

In this section, we collect together all of the tables produced from our example data model, using the approach outlined above. For each table we include a short explanation of how the table relates to the data model.

The employee table contains columns for the attributes of the employee entity and foreign keys implementing the relationships works at and supervises .

The factory table contains columns for the attributes of the factory entity and a foreign key implementing the relationship manages . The throughput attribute is not reflected in the table, as it is a derived attribute. The throughput of a factory can be computed by summing the throughputs of the assembly lines in the factory.

The assembly_line table implements the assembly line weak entity. It incorporates a foreign key referencing the factory parent entity. Its primary key is composed of the parent entity key ( factory_city ) and the partial key ( number ).

The model table contains columns for the attributes of the model entity. Only the component attributes of the composite attribute designation are included; as designation was also the key attribute for model , the model table has a composite primary key. The table also includes a foreign key implementing the builds relationship. As mentioned in the text above, the builds relationship could alternatively be implemented using a cross-reference table connecting factory and builds , but we have opted for the simpler solution here. We assume that the designation of computer models includes the name of the computer line (e.g. “Orion”) and some particular version of the computer line, which we call the “number” of the model. These versions may contain letters as well as numbers (e.g., “xz450”), which is why a column named “number” is implemented as text.

The model_application table implements the multivalued attribute application of the model entity. Each row of the table contains a single application value describing a particular computer model. Note that, as the model entity has a composite primary key, the model_application table has a composite foreign key referencing its parent ( not two separate foreign keys for each component of the parent key). Additionally, we constrain the values in application to come from a set list of possible values, contained in the application table (below).

The application table contains a simple list of unique values which are available to insert into the model_application table.

The part table contains columns for the attributes of the part entity. The column part_number here, similar to the model “number” above, can contain characters as well as numbers, so again we use a text type column.

The model_part table is a cross-reference table implementing the can use relationship.

The vendor table contains columns for the attributes of the vendor entity. Only the component attributes of the contact info attribute are reflected.

The vendor_part table is a cross-reference table implementing the supplies relationship. In addition to the foreign keys for the tables it relates to, it contains a column for the price attribute of the relationship.

2.3.5. Self-check exercises ¶

This section has some questions you can use to check your understanding of how to convert ERDs to a relational database.

Q-1: Entities in our ERD become tables in our relational database. What do relationships become?

Any relationship can be converted into a cross-reference table. Is that the only possibility?

Foreign keys

One-to-one and one-to-many relationships can be converted into foreign keys in our database. Are those the only cardinality ratios?

Merging of tables

One-to-one relationships can result in merging tables, although this is rare.

All of the above

Each of the methods above can be applied, depending on the cardinality ratio of the relationship and other factors.

Q-2: Consider the ERD below. We create tables a and b , each of which have a primary key column named “id”. (Assume there are additional columns from attributes not shown.) What is the simplest way to convert the relationship between A and B ?

Create a column named “a_id” in table b , and make it a foreign key referencing table a .

Since a row in b could be related to multiple rows in a , we would need to store multiple ID values in column a_id . Some databases would permit this, but it would complicate queries and updates on the database.

Create a column named “b_id” in table a , and make it a foreign key referencing table b .

This is the simplest solution, assuming we do not expect the relationship to change to many-to-many in the future.

Create a cross-reference table, a_b , containing columns a_id and b_id as foreign keys referencing a and b respectively.

This is an allowable conversion. Is it the simplest?

Merge tables a and b into a new table.

This is not a good choice; while such a structure can be made to work, it is not considered good database design and is prone to errors. We would say that this table is not properly normalized . We explore normalization in Part 3 .

Q-3: Consider the ERD below. We create table r with primary key column id . What should table w look like?

The table should have a column partial as primary key. Additionally, create a cross-reference table r_w .

Partial keys cannot become primary keys. They do not represent unique identifiers for the instances of the weak entity.

The table should have columns partial and r_id . The primary key is partial . Add a foreign key constraint on r_id referencing r .

The table should have columns partial and r_id . The primary key is a composite of r_id and partial . Add a foreign key constraint on r_id referencing r .

The table should have columns partial and r_id . The primary key is r_id . Add a foreign key constraint on r_id referencing r .

The parent key is not a sufficient key for the weak entity; there will be multiple rows in w with the same values for r_id . Therefore it cannot be a primary key.

Q-4: Consider the ERD below. We create tables c and d , each of which have a primary key column named “id”. How should we handle the relationship between C and D ?

Borrow the primary key from one table as a foreign key into the other table (either direction is fine). Add a column named “x” into the table with the foreign key column.

Create a cross reference table c_d with columns c_id , d_id , and x . Make a composite primary key using c_id and d_id . Add foreign key constraints on c_id and d_id referencing c and d , respectively.

Create a cross reference table c_d with columns c_id and d_id . Make a composite primary key using c_id and d_id . Add foreign key constraints on c_id and d_id referencing c and d , respectively. Create another table, c_d_x , with columns c_id , d_id , and x . Table c_d_x has primary key x , and a foreign key constraint on c_id and d_id referencing table c_d .

This could almost work (you would need a different primary key for c_d_x ), but it is unnecessarily complicated.

Create a cross reference table c_d with columns c_id and d_id . Make a composite primary key using c_id and d_id . Add foreign key constraints on c_id and d_id referencing c and d , respectively. Add column x to either c or d .

The values for x will differ for different combinations of c and d . There is no good way to capture the dependence of x on d , for example, if we put the column in c .

Q-5: Which of the following statements is false ?

Composite attributes result in columns for each component as well as the composite.

We do not create a column for the composite, just the components.

Multivalued attributes usually require an additional table in the database.

This is true. In some cases it may be possible to use array-valued columns to handle a multivalued attribute, but otherwise we need an additional table or tables.

No column is created for derived attributes.

This is true. Derived attributes are not intended to be stored, as they can be computed from other values in the database.

If an entity has a composite key attribute, the resulting table will have a composite primary key.

This is true.

signature assignment relational database table

Database Design: Entities, Attributes, and Relationships

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About this article

In this article, we talk about entities, attributes, and relationships. We explore how we can discover them in the business domain and how they relate to tables, columns, and relationships in the database domain.

This the second post on a series exploring the subject of relational database design for the full-stack developer.

For quick access, here is the list of all the articles on the topic:

  • Introduction to Relational Databases
  • Database Design: Entities, Attributes, and Relationships ( this article )
  • Database Design: Entity-Relationship Diagrams
  • Database Design: Normalization
  • Database Design: Entity-Relationship Diagram to Structured Query Language
  • Deploying PostgreSQL for development and testing
  • Structured Query Language Cheat Sheet
  • Working with databases from Python

Introduction

As a full-stack developer, you work on many programming projects that required a database. For some of them, you use an existing database. For others, however, you must design and implement a database from scratch.

The quality of your database design has a direct impact on the quality of your final application. A well-designed database that accurately models the business domain entities results in software that is sturdy, reliable, and fast.

In this post, we use an example to illustrate how to discover entities, attributes, and relationships from a problem statement provided by a client. This discovery is a necessary first step for designing a relational database for a full-stack project.

This article relies on the process described by Fidel A. Captain in his excellent book “Six-Step Relational Database Design.” You should reference this book for a more in-depth look at relational database design.

Problem Statement

Before any database design takes place, you must obtain from your client a problem statement. The statement should clearly express the business problem you are solving and the data your application should track.

It is crucial to get the problem statement right. It should be concise while avoiding to omit important details.

Case Study: Atelier-M Store

Atelier-M Store sells personalized leather accessories on Instagram. The store posts product pictures and customers place orders following a link to a Google Form.

Sales are good, and the current method for taking and tracking orders is limiting further growth. Atelier-M hired you to build an online store.

The problem statement follows:

A small shop wants an online store to sell personalized leather accessories. The store application must keep track of the following:

  • The customers
  • The orders placed by customers and the details of the orders
  • The products
  • The personalizations requested on the products
  • The packages requested for each product in the orders
  • The payments received for the orders
  • If the order is a gift, we must track the cards selected and the personalized messages that should be handwritten on the cards
  • The staff that can access the store’s admin dashboard
  • The delivery options
  • The delivery addresses for the orders

Discover Entities and Assign Attributes

Entity identification.

Once you have the problem statement, the first step is to identify the entities the app must track. Entities are objects of interest in the business domain. They map to tables in the database and classes in code.

You can find entities in the problem statement by spotting nouns or collective nouns. Applying this technique in our case study, we get:

  • The orders placed by customer sand the details of the orders
  • The packages requested for each product in the orders.

This exercise produces an initial list of entities as follows: customers , orders , order details , products , personalizations , packages , payments , cards , messages , staff , delivery options , and addresses .

Refining the list, you could argue that  customers  and  staff  are application users with different roles and permissions. So, you can replace customers and staff with  users  and add entities for  roles  and  permissions .

Now, let’s take a look at cards and  messages . A customer places an order as a gift. In that case, the order would include a card with a handwritten message.

There are several types of cards to select from (i.e., birthday, Christmas, and so on), and your client could add new card types in the future. To accurately track them, cards should be an entity.

On the other hand, a gift order has one message. The customer enters the message text when placing the order. In this case, you should track the messages as attributes of orders.

Regarding  addresses , you may think of them as attributes of  users . However, users can place orders for delivery to different addresses (i.e., home, work, and so on), and attributes should describe one and only one characteristic of an entity. It is clear then that you must track addresses as an entity.

Pro Tip: About Naming Conventions

In a future article, we implement the database design from this series in PostgreSQL. For this reason, it is helpful to use PostgreSQL naming conventions for tables and columns while discovering entities and entity attributes. The naming rules we follow are:

  • All identifiers are lowercase words (i.e., username ).
  • If an identifier consists of several words, we use an underscore to separate each word in it (i.e., last_name ).
  • Identifiers for table names are plural nouns (i.e., order_details ).
  • Identifiers for table columns are singular nouns (i.e., quantity ).

If you are implementing your design in other RDBMS, use the naming conventions for that system.

The final list of entities after applying our naming convention is users , roles , permissions , orders , order_details , products , personalizations , packages , payments , cards , delivery_options , and addresses .

Attribute Assignment

Attributes are properties that describe an entity’s characteristics. Attributes map to database table columns, and as mentioned in Introduction to Relational Databases , both table columns and attributes should describe precisely one property of the entity.

The process of identifying attributes can be long and tedious. Meetings with the client and client’s staff are necessary to capture relevant attributes.

Reviewing the client’s processes, primarily process documentation, forms, and records kept either on paper or by electronic means, is essential for complete identification of all attributes.

In our case study, customers place orders by filling a Google Form. A Google Sheet captures the results from each form’s submission. From the study of this Google Sheet’s structure and meetings with your client, you come up with the following list of attributes for each of the discovered entities.

This image show the discovered entities and attributes for Atelier-M online store.

We used Excel to build list of entities and attributes for Atelier-M store’s database

Once you finish identifying attributes for the entities, you should search for candidate attributes for primary keys. A primary key is an attribute or a set of attributes that uniquely identifies an instance of an entity.

For example, in your entity  users , you could select  email  as a candidate primary key. After all, emails are unique and identify a particular user.

There is a problem, however, with that primary key. People can, and often do, change emails. If a user changes the email, records related to the old email become orphaned.

A common practice for assigning primary keys is to add an  id  attribute to each entity. The id auto-increments with each new instance of the entity, ensuring its uniqueness.

For the case study, you use precisely this technique and end up with the following diagram:

This image shows the discovered entities and attributes for the online store after adding surrogate primary keys

Entities and Attributes with Primary Keys for Atelier-M store’s database

Derive Relationships

Now that you have a clear picture of the entities and their attributes, you can proceed with exploring the relationships between entities. For this task, you use an Entity-Entity Matrix.

An Entity-Entity Matrix is just a table where both the column headings and the row headings are the entities discovered in the previous step.

For the case study, using Excel, you build the following Entity-Entity Matrix:

This image shows the Entity-Entity matrix we can use to discover relationships.

We used Excel to build this Entity-Entity Matrix

It is important to note that you only use the bottom half of the matrix. Since each half of the matrix mirrors the other, using both would be redundant.

Each cell in the matrix holds the relationship between the entities in its row and column. You should identify these relationships and write in each cell a verb that represents it.

If there is no relationship between two entities, leave the cell blank.

The resulting Entity-Entity Matrix for our case study follows:

This image shows the completed Entity-Entity matrix.  We added verbs to the entity intersections to represent those relationships.

Finished entity-entity matrix for Atelier-M database

Going over the matrix cell by cell, you can state:

  • Users have roles
  • Roles have permissions
  • Users place orders
  • Order details belong to orders
  • Order details contain products
  • Products have personalizations
  • Orders use packages
  • Orders have payments
  • Orders use cards
  • Orders have delivery options
  • Addresses belong to users

Pro Tip: Unary Relationships

Note that in our exercise, no entity is related to itself. Keep in mind that those relationships (known as unary relationships) sometimes exist. One example would be the users’ entity in a social media application where users follow users.

In this article, you discovered the entities, attributes, and relationships for the database design of an online store. In the next article , you will use this information to create an entity-relationship diagram for implementation in PostgreSQL.

  • “Six-Step Relational Database Design.” by Fidel A. Captain
  • Difference Between Entity and Attribute in Database
  • PostgreSQL naming conventions
  • Relational database design question – Surrogate-key or Natural-key?
  • N-ary relationship types

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Javier Feliu

I am a full-stack developer from Panama. I enjoy programming in Python and JavaScript. My current interests include database design, building RESTful APIs, workflow automation, containerization, and cloud deployments.

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signature assignment relational database table

Tables Relations in SQL Server: One-to-One, One-to-Many, Many-to-Many

It is important to understand and design relationships among tables in a relational database like SQL Server. In a relational database, each table is connected to another table using the Primary-Foreign Key constraints.

Table relationships in SQL Server database are of three types:

  • One-to-Many
  • Many-to-Many

One-to-One Relation

In One-to-One relationship, one record of the first table will be linked to zero or one record of another table. For example, each employee in the Employee table will have a corresponding row in EmployeeDetails table that stores the current passport details for that particular employee. So, each employee will have zero or one record in the EmployeeDetails table. This is called zero or one-to-one relationship.

signature assignment relational database table

Above, EmployeeID column is the primary key as well as foreign key column in the EmployeeDetails table that linked to EmployeeID of the Employee table. This forms zero or one-to-one relation.

The following query will display data from both the tables.

The following is the result of the above queries that demonstrate how each employee has none or just one corresponding record in EmployeeDetails table.

signature assignment relational database table

One-to-Many Relation

One-to-Many is the most commonly used relationship among tables. A single record from one table can be linked to zero or more rows in another table.

Let's take an example of the Employee and Address table in the HR database. The Employee table stores employee records where EmployeeID is the primary key. The Address table holds the addresses of employees where AddressID is a primary key and EmployeeID is a foreign key. Each employee will have one record in the Employee table. Each employee can have many addresses such as Home address, Office Address, Permanent address, etc.

The Employee and Address tables are linked by the key column EmployeeID . It is a foreign key in the Address table linking to the primary key EmployeeID in the Employee table. Thus, one record of the Employee table can point to multiple records in the Address table. This is a One-to-Many relationship.

signature assignment relational database table

The following is the result of the above queries to demonstrate how the data is related in one-to-many relationship.

signature assignment relational database table

In the above data, each record in the Employee table associated with zero or more records in the Address table, e.g. James Bond has zero address, John King has three addresses.

Many-to-Many Relation

Many-to-Many relationship lets you relate each row in one table to many rows in another table and vice versa. As an example, an employee in the Employee table can have many skills from the EmployeeSkill table and also, one skill can be associated with one or more employees.

The following figure demonstrates many-to-many relation between Employee and SkillDescription table using the junction table EmployeeSkill .

signature assignment relational database table

Every employee in the Employee table can have one or many skills. Similarly, a skill in the SkillDescription table can be linked to many employees. This makes a many-to-many relationship.

In the example above, the EmployeeSkill is the junction table that contains EmployeeID and SkillID foreign key columns to form many-to-many relation between the Employee and SkillDescription table. Individually, the Employee and EmployeeSkill have a one-to-many relation and the SkillDescription and EmployeeSkill tables have one-to-many relation. But, they form many-to-many relation by using a junction table EmployeeSkill .

The following query will display data from all the tables.

The following is the result of the above queries that demonstrate how the data is related in many-to-many relationship.

signature assignment relational database table

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  • Data fundamentals

Database table relationships

Understanding how tables in a database can relate to each other., example one-to-one relationship, example one-to-many relationship, many-to-many relationship, further reading.

Relationships are meaningful associations between tables that contain related information — they’re what make databases useful. Without some connection between tables in a database, you may as well be working with disparate spreadsheet files rather than a database system.

As we covered in our short overview of databases , databases are collections of tables, and those tables have fields (also known as columns ). Every table contains a field known as an entity (or primary) key , which identifies the rows within that table. By telling your database that the key values in one table correspond to key values in another, you create a relationship between those tables; these relationships make it possible to run powerful queries across different tables in your database. When one table’s entity key gets linked to a second table, it’s known as a foreign key in that second table.

Identifying the connections you’ll need between tables is part of the data modeling and schema design process — that is, the process of figuring out how your data fits together, and how exactly you should configure your tables and their fields. This process often involves creating a visual representation of tables and their relationships, known an entity relationship diagram (ERD) , with different notations specifying the kinds of relationships. Those relationships between your tables can be:

Giving some thought to how your tables should relate to each other also helps ensure data integrity, data accuracy, and keeps redundant data to a minimum.

One-to-one relationship

In a one-to-one relationship, a record in one table can correspond to only one record in another table (or in some cases, no records). One-to-one relationships aren’t the most common, since in many cases you can store corresponding information in the same table. Whether you split up that information into multiple tables depends on your overall data model and design methodology; if you’re keeping tables as narrowly-focused as possible (like in a normalized database ), then you may find one-to-one relationships useful.

Let’s say you’re organizing employee information at your company, and you also want to keep track of each employee’s computer. Since each employee only gets one computer and those computers are not shared between employees, you could add fields to your Employee table that hold information like the brand, year, and operating system of each computer. However, that can get messy from a semantic standpoint — does computer information really belong in a table about employees? That’s for you to decide, but another option is to create a Computers table with a one-to-one relationship to the Employee table, like in the diagram below:

A one-to-one relationship between an Employee and a Computers table. PK indicates a primary key, and FK indicates a foreign key.

In this case, the entity key from our Employee table serves as the foreign key for the Computers table. You may have computers that aren’t yet assigned to employees, and this modeling ensures that you can still keep records for them the Computer table. And if an employee leaves the company, you’ll only need to update one field, and you can easily link a computer to a new employee.

The exact formatting of the lines used to connect tables in an ERD (known as crow’s foot notation) varies; sometimes you’ll see plain lines indicating a one-to-one relationship, other times those lines will have crosshatches.

One-to-one relationships can be also useful for security purposes, like if you want to store sensitive customer information in a separate table, which you link to your main Customers table with a foreign key.

One-to-many relationship

One-to-many relationships are the most common type of relationships between tables in a database. In a one-to-many (sometimes called many-to-one) relationship, a record in one table corresponds to zero, one, or many records in another table.

For example, think about tables for customers and their orders, like in Metabase’s Sample Database, where one record from the People table can be linked to many records in the Orders table. In this case, one customer can place many orders, and those multiple order records will all link back to a single record in the People table. That connection is codified through the User_ID field, which is a primary key in the People table and a foreign key in the Orders table. The diagram below shows how these two tables relate to each other:

A one-to-many relationship between the Sample Database

While the one person can be linked to many orders, the reverse is not true — orders are only linked to a single record in the People table, and don’t have many customers.

A many-to-many relationship indicates that multiple records in a table are linked to multiple records in another table. Those records may only be associated with a single record (or none at all) but the key is that they can and often are linked to more than one. Many-to-many relationships aren’t very common in practical database use cases, since adhering to normalization often involves breaking up many-to-many relationships into separate, more focused tables.

In fact, your database system may not even allow for the creation of a direct many-to-many relationship, but you can get around this by creating a third table, known as a join table , and create one-to-many relationships between it and your two starting tables.

In this sense, the Orders table in Metabase’s Sample Database acts as a join table, creating an intermediate link between People and Products . An ERD of the Sample Database would look something like the image below, where each relationship is specified by the type of line used to connect the tables:

An ERD showing Metabase

Technically speaking the Products and Orders tables have a one-to-many relationship, in that one product can be associated with many orders. But according to our fake company’s database, people seem to only order a single product (they’ll buy like five Lightweight Wool Computers for whatever reason). A real-world (and perhaps more business-savvy) implementation of this database would probably include a join table between the two, making it so orders could contain many different products.

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Database Star

A Guide to the Entity Relationship Diagram (ERD)

Learn all about Entity Relationship Diagrams (ERDs), what they are used for, how to understand them, how to create them, and more in this guide.

Table of Contents

What is an Entity Relationship Diagram (ERD)?

An Entity Relationship Diagram (ERD) is a type of diagram that lets you see how different entities (e.g. people, customers, or other objects) relate to each other in an application or a database.

They are created when a new system is being designed so that the development team can understand how to structure the database. They can also be created on an existing system to help the team understand how the system works and to find and resolve any issues.

Entity Relationship Diagrams use a specific set of symbols, such as shapes and arrows, to depict the system and database.

Here’s an example of an ERD:

Logical ERD

Components of an ERD

An Entity Relationship Diagram is made up of many different components:

Relationship

An entity is a thing that can have data stored about it. It can be a physical object (e.g. car, person), a concept (e.g. address) or an event (e.g. student enrolment in a course). They represent nouns.

They are usually represented as rectangles on an ERD with the entity name inside the rectangle.

An entity can also be a strong entity or a weak entity. What’s the difference?

A strong entity has an identifier (a primary key) and does not depend on any other entities for it to exist. For example, a student may be a strong entity, as it can have a primary key and does not depend on any other entities for it to exist.

A weak entity is one that depends on a strong entity for existence. This means it has a foreign key to another entity. For example, an enrolment of a student may be a weak entity, as an enrolment cannot exist without a student.

A relationship in an ERD defines how two entities are related to each other. They can be derived from verbs when speaking about a database or a set of entities.

Relationships in ERDs are represented as lines between two entities, and often have a label on the line to further describe the relationship (such as “enrols”, “registers”, “completes”).

There are several types of relationships that are represented on an ERD:

  • One to one: One record of an entity is directly related to another record of an entity
  • One to many: One record of an entity is related to one or more records of another entity.
  • Many to many: Many records of one entity can be related to many records of another entity.

An attribute is a property of an entity or something that can be used to describe an entity. They are often represented as ovals, or as entries inside an entity.

There are several different types of attributes represented on an ERD:

  • Simple: an attribute that cannot be split into other attributes, such as a first name.
  • Composite: an attribute that can be split into other attributes, such as name being split into first, middle, and last name.
  • Derived: an attribute that is calculated or determined from another attribute, such as the age of record being calculated from the created date.

An attribute can also be single-value or multi-value:

  • Single-value: an attribute that is only captured once for
  • Multi-Value: an attribute that can be captured more than once for an entity, such as multiple phone numbers.

What is Cardinality?

Cardinality represents the number of instances of an entity that exist in a relationship between two entities. This is often expressed as a number but could also be a symbol, depending on the style of diagram used. Common cardinality values are zero, one, or many.

We’ll see some examples of cardinality later in this guide.

Natural Language

When we are creating an ERD we often have an idea of what we want to capture. This can usually be expressed in words, or using “natural language”.

Some examples are:

  • “Record students, courses they enrol in, and teachers who teach the course”
  • “Capture the customer orders, customer details, and where the orders are being sent”
  • “Capture patient data and the operations they had”

These sentences include a few different types of words, which can be used as a starting point for an ERD. They are represented in a few different ways:

  • Noun: a “thing”, such as a student or customer. Represented as an entity.
  • Verb: an action, such as enrol or send. Represented as a relationship between two entities.
  • Adjective: a describing word, such as residential or advanced. Represented as an attribute on an entity.

This can help you translate a description of what you need to diagram into an actual diagram.

Symbols and notations

When creating an ERD, it can be simple to create boxes and lines between them. But, like many things in software development, there are a few different methods and standards that are available. For ERDs, there are several notation standards, which define the symbols used.

Here’s an example of the symbols used with the Chen notation style.

Chen Notation

Here’s an example ERD using Chen notation:

Chen Notation Example ERD

Crow’s Foot

Here’s an example of the symbols used with the Crow’s foot notation style. This is the style you’ll see the most on Database Star as it’s the one I’m most familiar with. It’s called “crow’s foot’ as the symbol for a many relationship looks like the foot of a crow with its three prongs.

Crows Foot

Here’s an example ERD using Crow’s Foot notation:

Crow's Foot Examples

Here’s an example of the symbols used with the Bachman notation style.

Bachman ERD

Here’s an example ERD using Bachman notation:

Bachman Example ERD

Here’s an example of the symbols used with the IDEF1X notation style.

IDE1FX Notation

Here’s an example ERD using IDEF1X notation:

IDE1FX Example ERD

Here’s an example of the symbols used with the Barker notation style.

Barker ERD Notation

Conceptual, logical, physical

An Entity Relationship Diagram can be drawn at three different levels: conceptual, logical, or physical.

Each of these levels has a different level of detail and are used for a different purpose.

Let’s see some examples.

Conceptual Data Model

The conceptual data model shows the business objects that exist in the system and how they relate to each other.

It defines the entities that exist, which are not necessarily tables. Thinking of tables is too detailed for this type of data model.

An example of a conceptual data model is shown here. It shows students, courses, and how they relate.

Conceptual ERD

Logical Data Model

A logical model is a more detailed version of a conceptual data model. Attributes are added to each entity, and further entities can be added that represent areas to capture data in the system.

Here’s an example of the student and course data model created as a logical data model.

Physical Data Model

The physical data model is the most detailed data model in this process. It defines a set of tables and columns and how they relate to each other. It includes primary and foreign keys, as well as the data types for each column.

These diagrams can be created manually in a data modelling tool. They are also often generated by an IDE from an existing database.

Here’s an example of the student and course physical data model.

Physical ERD

This table outlines the differences between a conceptual, logical, and physical model:

How to Create an Entity Relationship Diagram

So how do you create a data model or an Entity Relationship Diagram?

I’ve detailed this in my Relational Database Design course, and I’ve also explained it in my guide to designing a database .

The process to create an ERD is:

  • Write a sentence or two about what you are storing data about
  • List what you’re storing data about – the nouns/objects
  • List the information you want to store for each object
  • Describe the relationships between each object
  • Draw the diagram

The steps listed here are brief but the process can take some time, depending on how familiar you are with the system and how experienced you are with creating Entity Relationship Diagrams.

Tools to Use

What tool should you use to create a data model or an Entity Relationship Diagram?

There are a lot of applications available to help you create one. I’ve written a post that lists them all with a comparison of major features and price, which you can find here: 79 Data Modeling Tools Compared .

Personally, I use LucidChart as it’s simple to use and web-based. But in the past, I’ve used Visio and even the data modelling capabilities inside an IDE.

That page above includes a range of tools and some recommendations if you’re not sure what to use.

You can also start with a pen and paper to get an understanding of how a diagram can be created. I often start by drawing one in my notebook. It gets messy eventually, but I transfer it to an electronic copy anyway.

Tips for Creating an Entity Relationship Diagram

Here are some tips for creating an Entity Relationship Diagram:

  • Determine the right level of detail based on the purpose of the diagram. Development teams often find the logical model the most useful, but others might find the conceptual model more valuable.
  • Review the entities and attributes to see if they include everything you want to store in the system.
  • Name all entities and attributes.
  • If you have a large diagram, consider using colours to highlight different sections, or breaking it up into smaller diagrams.
  • Be consistent with your naming and the symbols you use.

An Entity Relationship Diagram is a great tool to help you define, understand, and communicate the requirements of a system. It can be modelled at a high level (conceptual data model), a detailed level (physical data model) or a level in between (logical data model).

There is a range of modelling notations or types of symbols which define how entities, attributes, and relationships are captured.

If you want to get started with creating an ERD for your system, consider drawing one on paper, or use one of the many tools available.

15 thoughts on “A Guide to the Entity Relationship Diagram (ERD)”

signature assignment relational database table

Hi, may I congratulate you on a job very well done. Your comparative diagram notation are clear, you don’t use too much jargon. Kudo

signature assignment relational database table

Very clearly explained, Thank You

signature assignment relational database table

excellent job with the explanation. Thanks, it really helps👍👍

signature assignment relational database table

Great insight will be coming can you help me solve my question from my class? To identify a problem of an organization and develop ER-Diagram and a database management system to solve it.

signature assignment relational database table

You need to give lecture to our doctor at the university. I am glad that English is the science language not German or any other language,

signature assignment relational database table

Very well explained, as I was struggling of what is a ERD, how do I design it. But now you have explained clearly as I understand very much. I like to say thank you for your hard effort.

signature assignment relational database table

Nice post!I will read more post in this site! Thank you!

signature assignment relational database table

Great article, really clear and well-written -thank you!

Just one minor point; I think the second Bachman diagram should actually be a Barker example?

signature assignment relational database table

Dear mister genius, thank you.

signature assignment relational database table

The notes are very clear and understandable.

signature assignment relational database table

i support you dear.

signature assignment relational database table

Thats the best description of data modelling I’ve seen in my quest to understand the concept

signature assignment relational database table

Excellent Explain

signature assignment relational database table

help me a lots, tyvm

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HomeWork-Tutorial

Dat 210 wk 1 – apply: signature assignment: relational database table.

DAT 210 Wk 1 - Apply: Signature Assignment: Relational Database Table

  • Description

Imagine you work for an independent grocery store with 20 employees. The business owner has tasked you with creating a relational database that will track employee names, IDs, positions (e.g., cashier, manager, clerk, or night crew), and salaries.

Create a relational database table in Microsoft® Excel® for the grocery store using the provided data points.

Apply the universal rules (unique primary keys, unique column names, no duplicate rows) to your table.

Include all necessary null values.

Display in the table the requested database queries:

  • INSERT INTO Account: VALUES (188820, ‘Wendall Glass’, $12/h)
  • UPDATE Account: Set salary = $12/h WHERE ID = 128872
  • DELETE FROM Account WHERE ID = 244332

Calculate the grocery store’s SUM salary and the AVG salary per position.

Submit your assignment.

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How to design an effective relational database

Big and small organizations alike use relational databases to store, manage, and analyze critical information. But what makes a database “relational?” A relational database organizes data in predefined relationships, letting you easily understand how your data is connected.

A well-designed database provides several benefits:

  • The database structure is easy to modify and maintain. Workflows rarely stay the same forever–you’ll likely have to make some adjustments to your core relational data model in the future. Fortunately, a well-designed database ensures that any modifications you make to fields in one table will not adversely affect other tables.
  • It’s easier to find the information that you need. With a consistent, logical database structure (that avoids duplicate fields and tables), it’s much easier to query your database.
  • You can avoid redundant, duplicate, and invalid data. This data can undermine the validity of your database, but you can design your relational database to minimize the risks posed by low-quality data.
  • You can avoid situations where you are missing required data. If you can identify ahead of time which types of data are most critical to your workflow, you can structure your database in such a way that it enforces proper data entry, or alerts users when records are missing critical data.

If you build a house without finalizing the blueprints, odds are when it’s finally built the house will have questionable structural integrity. Similarly, taking time to think carefully about the design of your relational database before implementing it can save you a lot of trouble in the long run. In this tutorial, we’ll cover several design principles that you can follow to help you build better databases.

What is a "well-designed" database?

Good database design is important. But what does it actually mean for a database to be well-designed?

Fast fact: The first person to devise an abstract model for database management was Edgar Frank Codd, an English computer scientist who joined IBM in the 1940s.

A well-designed database enforces data integrity

Data integrity refers to the accuracy, completeness, and consistency of the data in your database.

Data integrity includes three specific technical aspects of a relational database’s structure:

  • Entity integrity (or table-level integrity) ensures that a table has no duplicate records, and that the values of the table’s primary keys are all unique and not null.
  • Domain integrity (or field-level integrity) ensures that the purpose of every field is clear and identifiable, and that the values in each field are valid, consistent, and accurate.
  • Referential integrity (or relationship-level integrity) ensures that the relationships between pairs of tables are sound, so that the records in the tables are synchronized whenever data is entered into, updated in, or deleted from either table.

A well-designed database maintains data integrity by implementing the processes and standards proposed during the design phase.

A well-designed database enforces relevant business rules

Every organization does its work a little differently, and as such, each organization has its own business rules. Ideally, your team’s database should enforce your unique business rules.

Let’s say your content marketing team requires that timestamps for video files be noted in milliseconds, and each asset is assigned an eight-character alphanumeric code. If the video files were noted in seconds instead of milliseconds and then entered into the company database, it could create a major snag in video production—and a mess for a database administrator to clean up later.

How to design your relational database, step by step

If this all sounds unfamiliar or overwhelming, don’t worry—there’s a systematic process you can follow so your relational database management system (or RDBMS) follows good design principles, is well-suited to your organization’s needs, and avoids common pitfalls.

Step 1: Define your purpose and objectives

Before beginning your database design journey, understand why you’re making it.

Are you making this database to manage transactions? To store customer IDs? To solve a specific organizational problem? Whatever the case, it’s worth taking the time to identify the exact purpose of the database you’ll be creating.

You may even want to work with stakeholders, executive leadership, and end users to jointly write out a mission statement for your database, like: “The purpose of the Mingei International Museum database is to maintain the data for our art collection,” or “Zenbooth’s database will store all of the data for our manufacturing resource planning.”

Additionally, you should define the objectives that the end users of the database will have: which specific tasks will the end users need to perform to accomplish their work? Develop an explicit list of objectives—like “Know the status and location of each of the pieces of art in our collection at all times,” or “Maintain a complete customer table that shows records for each of our clients.” This will help you determine an appropriate structure, or database schema, for your information as you work through this design process.

Step 2: Analyze data requirements

Before you design your database, you’ll need to assess how your team currently does its work, and identify what kind of data is most important to that work.

You can do this by closely examining existing processes and by interviewing team members—both management and end users. Some questions to ask as you conduct your research:

  • How is your organization currently collecting data? Are you using spreadsheets? Paper templates? Another database? Find the most complete samples of work that you can, and look through them to find as many different attributes as you can. For example, your editorial calendar might currently be living in a spreadsheet, and have columns for “Author,” “Due Date,” “Editor,” and so on.
  • How is your organization currently presenting data? What kinds of reports does your organization use? PDFs? Slide decks? Web pages? Carefully examine any types of presentations that incorporate data from your current data collection methods and use them to identify potential fields.
  • How are your team members currently using data? Talk to team members—both management and end users—to identify their current data use patterns and case studies, as well as any gaps in the current system. You can ask questions like, “What types of data are you currently using?” and have them review the samples you collected. These interviews can also illuminate plans for the future growth of the organization, which will give you insight into the type of relational database model that would be the best fit.

Step 3: Create a list of entities and a list of attributes

The next steps are to extract a list of entities and a list of attributes from the research you’ve compiled.

In the context of relational databases, an entity is an object, person, place, event, or idea—like “clients,” “products,” “projects,” or “sales reps.” These entities will eventually turn into your tables later on in the design process.

Start by picking out entities from your research and putting them in a list. For example, if you were developing a talent database for a big record label, your entities list might look something like this:

Next, create a separate list containing the relevant attributes for each of the entities you’ve identified. Attributes are the defining characteristics of those entities, like “name,” “quantity,” “address,” “phone number,” or “genre.” These attributes will become the fields for your tables.

Think of entities as nouns, and attributes as the adjectives that describe those nouns. Again, for the talent database example, your attributes list might look something like this:

  • Artist Name
  • Agent Phone Number
  • Agent Email Address
  • Venue Address
  • If multiple attributes have different names but actually represent the same concept, consolidate them into one. For example, if you have both “Product No.” and “Product Number” on your list, you should remove one of them.
  • If multiple attributes have similar names but actually represent different concepts, rename the attributes to be more specific. For example, you could rename two different “Name” attributes into the more specific “Artist Name” and “Venue Name.”

After refining your lists, it’s a good idea to review them with the team members you interviewed to confirm that you’ve accounted for every necessary type of data.

Step 4: Model the tables and fields

After listing your entities and attributes, use them to design the structure of your relational database. Your list of entities will become separate tables in your base, and the list of attributes will become the fields for these tables.

> Learn more about creating tables in Airtable

Take your lists and assign each of the attributes to your tables. For example, after we finish assigning our listed attributes to our new tables, our talent management database-in-planning might look something like this:

signature assignment relational database table

Next, you want to figure out how to name your records in each table. This requires that you pick an appropriate primary field .

A primary field is a major component of ensuring data integrity, as it uniquely identifies each record within a table and is used to establish different types of relationships between tables. And in Airtable, the primary field is always the first column in any table (and it can't be deleted, moved, or hidden).

Each table’s primary field should meet the following criteria:

  • It must contain unique identifiers. This will prevent you from creating duplicate records and redundancy within a table.
  • It cannot contain null values. A null value is the absence of a value, and as such, you cannot use a null value to identify a record.
  • It should not be a value that will need to be modified often. Ideally, primary field values will remain relatively static over time and only be changed under rare circumstances.
  • Ideally, it uses the table name as part of its own name. While not strictly necessary, having the table name in the primary field name can make it easier to identify the table from which the primary field originated. For example, “Employee Name” would be obviously identifiable as coming from the related table, “Employees.”

Let’s return to our talent management database example. For the “Artists” table, the “Artist name” field is already a pretty good candidate for the primary field, as it’s pretty unlikely that your record label will sign two artists with the same name. We can also pick “Venue name” as the primary field for the “Venues” table.

For the other tables, however, it would probably be better to make new fields that concatenate values from existing fields. In the “Agents” table, we might make a new field—“Agent full name”—that concatenates the values of the “Agent given name” and “Agent surname” fields.

For the “Gigs” table, an artist could perform at the same venue on multiple occasions, so we should make a new field that gives a unique name to the specific combination of an artist at a venue on a specific date.

You could potentially concatenate the name of the artist, the venue, and the date to create values like “2 Linkz at the Gotham City Metro Club 02/13/2019,” but that can get long and unwieldy fast. Alternatively, you could try creating a new field—“Gig code”—with unique alphanumeric code values (like “E0023”).

signature assignment relational database table

A poorly designed database makes it difficult to access the information you need and can jeopardize the accuracy of your data. If you take the time to define your goals, research how your organization collects data, and identify what you want to store, you can create a structured database that anyone in your org can navigate and use.

Ready to design a relational database in Airtable? Check out our guide to bases, then jump in and start building .

All your interface designer layout questions, answered.

What’s new in airtable: july 2023.

On a screen with a blue background are numbers and letters, representing a cumbersome database

A relational database is a type of database that organizes data into rows and columns, which collectively form a table where the data points are related to each other.

Data is typically structured across multiple tables, which can be joined together via a primary key or a foreign key. These unique identifiers demonstrate the different relationships which exist between tables, and these relationships are usually illustrated through different types of  data models . Analysts use SQL queries to combine different data points and summarize business performance, allowing organizations to gain insights, optimize workflows, and identify new opportunities.

For example, imagine your company maintains a database table with customer information, which contains company data at the account level. There may also be a different table, which describes all the individual transactions that align to that account. Together, these tables can provide information about the different industries that purchase a specific software product.

The columns (or fields) for the customer table might be  Customer ID ,  Company Name ,  Company Address ,  Industry  etc.; the columns for a transaction table might be  Transaction Date ,  Customer ID ,  Transaction Amount ,  Payment Method , etc. The tables can be joined together with the common  Customer ID  field. You can, therefore, query the table to produce valuable reports, such as a sales reports by industry or company, which can inform messaging to prospective clients.

Relational databases are also typically associated with transactional databases, which execute commands, or transactions, collectively. A popular example that is used to illustrate this is a bank transfer. A defined amount is withdrawn from one account, and then it is deposited within another. The total amount of money is withdrawn and deposited, and this transaction cannot occur in any kind of partial sense. Transactions have specific properties. Represented by the acronym, ACID, ACID properties are defined as:

  • Atomicity:  All changes to data are performed as if they are a single operation. That is, all the changes are performed, or none of them are.
  • Consistency:  Data remains in a consistent state from state to finish, reinforcing data integrity.
  • Isolation:  The intermediate state of a transaction is not visible to other transactions, and as a result, transactions that run concurrently appear to be serialized.
  • Durability:  After the successful completion of a transaction, changes to data persist and are not undone, even in the event of a system failure.

These properties enable reliable transaction processing.

Relational database vs. relational database management system

While a relational database organizes data based off a relational data model, a relational database management system (RDBMS) is a more specific reference to the underlying database software that enables users to maintain it. These programs allow users to create, update, insert, or delete data in the system, and they provide:

  • Data structure
  • Multi-user access
  • Privilege control
  • Network access

Examples of popular RDBMS systems include MySQL, PostgreSQL, and IBM DB2. Additionally, a relational database system differs from a basic database management system (DBMS) in that it stores data in tables while a DBMS stores information as files.

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Invented by Don Chamberlin and Ray Boyce at IBM, Structured Query Language (SQL) is the standard programming language for interacting with relational database management systems, allowing database administrator to add, update, or delete rows of data easily. Originally known as SEQUEL, it was simplified to SQL due to a trademark issue. SQL queries also allows users to retrieve data from databases using only a few lines of code. Given this relationship, it’s easy to see why relational databases are also referred to as “SQL databases” at times.  

Using the example from above, you might construct a query to find the top 10 transactions by company for a specific year with the following code:

SELECT  COMPANY_NAME, SUM(TRANSACTION_AMOUNT)

FROM  TRANSACTION_TABLE A

LEFT JOIN  CUSTOMER_TABLE B

ON  A.CUSTOMER_ID = B.CUSTOMER_ID

WHERE  YEAR(DATE) = 2022

GROUP BY  1

ORDER BY  2 DESC

The ability to join data in this way helps us to reduce redundancy within our data systems, allowing data teams to maintain one master table for customers versus duplicating this information if there was another transaction in the future. To learn more, Don details more of the history of SQL in his paper  here  (link resides outside IBM).

Before relational databases, companies used a hierarchical database system with a tree-like structure for the data tables. These early database management systems (DBMS) enabled users to organize large quantities of data. However, they were complex, often proprietary to a particular application, and limited in the ways in which they could uncover within the data. These limitations eventually led IBM researcher, Edgar F. Codd, to publish a  paper  (link resides outside IBM) (PDF, 1.5 MB) in 1970, titled "A Relational Model of Data for Large Shared Data Banks,” which theorized the relational database model. In this proposed model, information could be retrieved without specialized computer knowledge. He proposed arranging data based on meaningful relationships as tuples, or attribute-value pairs. Sets of tuples were referred to as relations, which ultimately enabled the merging of data across tables.

In 1973, the San Jose Research Laboratory—now known as the Almaden Research Center—began a program called System R (R for relational) to prove this relational theory with what it called “an industrial-strength implementation.” It ultimately became a testing ground for SQL as well, enabling it to become more widely adopted in a short period of time. However, Oracle’s adoption of SQL also didn’t hurt its popularity with database administrators.

By 1983, IBM introduced the DB2 family of relational databases, so named because it was IBM’s second family of database management software. Today, it is one of IBM’s most successful products, continuing to handle billions of transactions every day on cloud infrastructure and setting the foundational layer for machine learning applications.

While relational databases structure data into a tabular format, non-relational databases do not have as rigid of a database schema. In fact, non-relational databases organize data differently based on the type of database. Irrespective of the type of non-relational database, they all aim to solve for the flexibility and scalability issues inherent in relational models which are not ideal for unstructured data formats, like text, video, and images. These types of databases include:

  • Key-value store:  This schema-less data model is organized into a dictionary of key-value pairs, where each item has a key and a value. The key could be like something similar found in a SQL database, like a shopping cart ID, while the value is an array of data, like each individual item in that user’s shopping cart. It’s commonly used for caching and storing user session information, such as shopping carts. However, it's not ideal when you need to pull multiple records at a time. Redis and Memcached are examples of open-source databases with this data model.
  • Document store:  As suggested by the name, document databases store data as documents. They can be helpful in managing semi-structured data, and data are typically stored in JSON, XML, or BSON formats. This keeps the data together when it is used in applications, reducing the amount of translation needed to use the data. Developers also gain more flexibility since data schemas do not need to match across documents (e.g. name vs. first_name). However, this can be problematic for complex transactions, leading to data corruption. Popular use cases of document databases include content management systems and user profiles. An example of a document-oriented database is MongoDB, the database component of the MEAN stack.
  • Wide-column store:  These databases store information in columns, enabling users to access only the specific columns they need without allocating additional memory on irrelevant data. This database tries to solve for the shortcomings of key-value and document stores, but since it can be a more complex system to manage, it is not recommended for use for newer teams and projects. Apache HBase and Apache Cassandra are examples of open-source, wide-column databases. Apache HBase is built on top of Hadoop Distributed Files System that provides a way of storing sparse data sets, which is commonly used in many big data applications. Apache Cassandra, on the other hand, has been designed to manage large amounts of data across multiple servers and clustering that spans multiple data centers. It’s been used for a variety of use cases, such as social networking websites and real-time data analytics.
  • Graph store:  This type of database typically houses data from a knowledge graph. Data elements are stored as nodes, edges and properties. Any object, place, or person can be a node. An edge defines the relationship between the nodes. Graph databases are used for storing and managing a network of connections between elements within the graph. Neo4j (link resides outside IBM), a graph-based database service based on Java with an open-source community edition where users can purchase licenses for online backup and high availability extensions, or pre-package licensed version with backup and extensions included.

NoSQL databases  also prioritize availability over consistency.

When computers run over a  network , they invariably need to decide to prioritize consistent results (where every answer is always the same) or high uptime, called "availability." This is called the "CAP Theory," which stands for Consistency, Availability, or Partition Tolerance. Relational databases ensure the information is always in-sync and consistent. Some NoSQL databases, like Redis, prefer to always provide a response. That means the information you receive from a query may be incorrect by a few seconds—perhaps up to half a minute. On social media sites, this means seeing an old profile picture when the newest one is only a few moments old. The alternative could be a timeout or error. On the other hand, in banking and financial transactions, an error and resubmit may be better than old, incorrect information.

For a full rundown of the differences between SQL and NoSQL, see " SQL vs. NoSQL Databases: What's the Difference? "

The primary benefit of the relational database approach is the ability to create meaningful information by joining the tables. Joining tables allows you to understand the  relations  between the data, or how the tables connect. SQL includes the ability to count, add, group, and also combine queries. SQL can perform basic math and subtotal functions and logical transformations. Analysts can order the results by date, name, or any column. These features make the relational approach the single most popular query tool in business today.

Relational databases have several advantages compared to other database formats:

Ease of Use

By virtue of its product lifespan, there is more of a community around relational databases, which partially perpetuates its continued use. SQL also makes it easy to retrieve datasets from multiple tables and perform simple transformations such as filtering and aggregation. The use of indices within relational databases also allows them to locate this information quickly without searching each row in the selected table.

While relational databases have historically been viewed as a more rigid and inflexible data storage option, advances in technology and DBaaS options are changing that perception. While there is still more overhead to develop schemas compared to NoSQL database offerings, relational databases are becoming more flexible as they migrate to cloud environments.

Reduced redundancy 

Relational databases can eliminate redundancy in two ways. The relational model itself reduces data redundancy via a process known as normalization. As noted earlier, a customer table should only log unique records of customer information versus duplicating this information for multiple transactions.

Stored procedures also help to reduce repetitive work. For example, if database access is restricted to certain roles, functions or teams, a stored procedure can help to manage access-control. These reusable functions free up coveted application developer time to tackle high impact work.

Ease of backup and disaster recovery 

Relational databases are transactional—they guarantee the state of the entire system is consistent at any moment. Most relational databases offer easy export and import options, making backup and restore trivial. These exports can happen even while the database is running, making restore on failure easy. Modern, cloud-based relational databases can do continuous mirroring, making the loss of data on restore measured in seconds or less. Most cloud-managed services allow you to create Read Replicas, like in  IBM Cloud® Databases for PostgreSQL . These Read Replicas enable you to store a read-only copy of your data in a cloud data center. Replicas can be promoted to Read/Write instances for  disaster recovery  as well.

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signature assignment relational database table

DAT 210 Week 1 – Apply: Signature Assignment: Relational Database Table

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Assignment Content

Imagine you work for an independent grocery store with 20 employees. The business owner has tasked you with creating a relational database that will track employee names, IDs, positions (e.g., cashier, manager, clerk, or night crew), and salaries.

Create a relational database table in Microsoft® Excel® for the grocery store using the provided data points.

Apply the universal rules (unique primary keys, unique column names, no duplicate rows) to your table.

Include all necessary null values.

Display in the table the requested database queries:

INSERT INTO Account: VALUES (188820, ‘Wendall Glass’, $12/h) UPDATE Account: Set salary = $12/h WHERE ID = 128872 DELETE FROM Account WHERE ID = 244332

Calculate the grocery store’s SUM salary and the AVG salary per position.

Submit your assignment.

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  1. All about Relational Databases

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  2. What Is An Entity In A Relational Database

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  3. Relational Database (Model, Operations & Constraints)

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  4. What are Relational Databases?

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  5. Relational and non relational databases

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  6. Creating a database

    signature assignment relational database table

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COMMENTS

  1. 2.3. Converting ERD to a relational model

    The first step in building a relational database from an ERD is creating a table from each entity in the data model. Weak entities need slightly different handling than regular entities, so we will address them separately, starting with regular entities. 2.3.1.1. Regular entities ¶

  2. Designing a Relational Database and Creating an Entity Relationship

    This is part 1 of a 3-part series taking you through the process of designing, coding, implementing and querying a relational database, starting from zero. See part 2 (Coding and Implementing a Relational Database using MySQL) here, and part 3 (Data Analysis in MySQL — Operators, Joins and More in Relational Databases) here.

  3. Coding and Implementing a Relational Database using MySQL

    This is part 2 of a 3-part series taking you through the process of designing, coding, implementing and querying a relational database, starting from zero. See part 1 (Designing a Relational Database and Creating an Entity Relationship Diagram) here, and part 3 (Data Analysis in MySQL — Operators, Joins and More in Relational Databases) here.

  4. Database Design: Entities, Attributes, and Relationships

    Attribute Assignment. Attributes are properties that describe an entity's characteristics. Attributes map to database table columns, and as mentioned in Introduction to Relational Databases, both table columns and attributes should describe precisely one property of the entity. The process of identifying attributes can be long and tedious.

  5. Tables Relations: One-to-One, One-to-Many, Many-to-Many

    Tables Relations in SQL Server: One-to-One, One-to-Many, Many-to-Many. It is important to understand and design relationships among tables in a relational database like SQL Server. In a relational database, each table is connected to another table using the Primary-Foreign Key constraints. Table relationships in SQL Server database are of three ...

  6. Database table relationships

    Every table contains a field known as an entity (or primary) key, which identifies the rows within that table. By telling your database that the key values in one table correspond to key values in another, you create a relationship between those tables; these relationships make it possible to run powerful queries across different tables in your ...

  7. PDF Relational Database Design: Part I

    A database is a collection of relations (or tables) Each relation has a set of attributes (or columns) Each attribute has a name and a domain (or type) Each relation contains a set of tuples (or rows) Keys A set of attributes In no instance of is a key for a relation if will two different tuples agree on all attributes of

  8. PDF Relational Database Design: Part I

    Case study 1. Design a database representing cities, counties, and states. For states, record name and capital (city) For counties, record name, area, and location (state) For cities, record name, population, and location (county and state) Assume the following: Names of states are unique. Names of counties are only unique within a state.

  9. Practical SQL: Designing and Creating a Relational Database

    Below is the schema of the sales database. It contains 4 relational tables. The first line is the name of table. The other lines represent the columns in table. Schema of sales database (image by author) Customer: Contains customer data and the id of the store that a customer most frequently goes.

  10. ASSIGNMENT 2 CREATING TABLES AND FIELDS

    Assignment 2. DESIGNING A RELATIONAL DATABASE. Tables. are used to store all of the data in a database. For example, the Clever Acme . Corp. database is divided into three tables: orders, customers, and products (Figure 6). Before creating a database, you must give thought to the database design, which has . four major steps: 1.

  11. PDF THE RELATIONAL DATABASE MODEL: INTRODUCTION

    Explain why the relational database model became practical in about 1980. Define such basic relational database terms as relation and tuple. Describe the major types of keys including primary, candidate, and foreign. Describe how one-to-one, one-to-many, and many-to-many binary relation-ships are implemented in a relational database.

  12. PDF Assignment 3

    relationships. 9. Insert the record data described below into each table in sequence: Job, Employee, Project, Assign. The table on the [1] side of the relationship must be entered before the [Many ∞] side because Referential Integrity is being enforced. If you have a value mismatch with the primary key it will not let you continue.

  13. A Guide to the Entity Relationship Diagram (ERD)

    An Entity Relationship Diagram is a great tool to help you define, understand, and communicate the requirements of a system. It can be modelled at a high level (conceptual data model), a detailed level (physical data model) or a level in between (logical data model). There is a range of modelling notations or types of symbols which define how ...

  14. Apply: Signature Assignment: Relational Database Table

    DAT 210 Wk 1 - Apply: Signature Assignment: Relational Database Table Imagine you work for an independent grocery store with 20 employees. The business owner has tasked you with creating a relational database that will track employee names, IDs, positions (e.g., cashier, manager, clerk, or night crew), and salaries.

  15. DAT 210 Wk 1

    DAT 210 Wk 1 ­ Apply: Signature Assignment: Relational Database Table Imagine you work for an independent grocery store with 20 employees. The business owner has tasked you with creating a...

  16. Relational Database: Definition, Examples, and More

    A relational database is a type of database that stores and allows access to data. These types of databases are referred to as "relational" because the data items within them have pre-determined relationships with one another. Data in a relational database is stored in tables. The tables are connected by unique IDs or "keys."

  17. How to Design a Relational Database

    Step 3: Create a list of entities and a list of attributes. The next steps are to extract a list of entities and a list of attributes from the research you've compiled. In the context of relational databases, an entity is an object, person, place, event, or idea—like "clients," "products," "projects," or "sales reps.".

  18. What is a Relational Database?

    A relational database is a type of database that organizes data into rows and columns, which collectively form a table where the data points are related to each other. Data is typically structured across multiple tables, which can be joined together via a primary key or a foreign key.

  19. Relational database design

    Some example tables (simplified): TABLE people ( id INT PRIMARY KEY, first_name VARCHAR (50), last_name VARCHAR (50) ) TABLE group_assignments ( id INT PRIMARY KEY person_id INT, organiation_id INT, -- This is the problem. How to create the proper relation. assignment_date DATETIME, assignment_end_date DATETIME ) -- First group of organizations ...

  20. Analytics 103

    In this course, you learned about relational database models and data normalization, covering concepts like the relationship between tables and first and third normal forms. ... Assignment Data ...

  21. Database Assignment

    Student's Signature: Date: ( Unit 4: Database Design & Development Unit 4: Database Design & Development (Data base system for the SmartMovers Transport Company) Part 1: The submission is in the form of an individual written report.

  22. DAT 210 Week 1

    Home / DAT 210 / DAT 210 Week 1 - Apply: Signature Assignment: Relational Database Table DAT 210 Week 1 - Apply: Signature Assignment: Relational Database Table $ 40.00

  23. signature assignment relational database table

    In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation. HomeWork-Tutorial. Dat 210 wk 1 - apply: sign