Introduction to Databases for Back-End Development

We all use data and databases in our daily online lives. For example, uploading photos to our social media feeds, downloading files at work, and playing games online are all examples of database usage. What exactly is data and how does it interact with the database? If you're struggling to answer these questions, don't worry. By the end of this course, you'll be able to describe what a database is at a conceptual level, identify real-world examples of the use of databases, and demonstrate an understanding of how data is organized in a database.

What is Data?

In basic terms, data is facts and figures about anything. For example, if data were collected on a person, then that data might include their name, age, email, and date of birth. Or the data could be facts and figures related to an online purchase, such as the order number, description, order quantity, date, and the customer's email. Data is crucial for individuals as well as organizations.

Where is Data Stored?

In our digital world, data is no longer stored in manual files. Instead, developers use something called databases. A database is a form of electronic storage in which data is organized systematically. It stores and manipulates data electronically to make it more manageable, efficient, and secure.

Real-World Examples of Database Usage

1. Banks: Store data for customers, bank accounts, and transactions.

2. Hospitals: Store patient data, staff data, laboratory data, and more.

What Does a Database Look Like?

A database looks like data organized systematically, typically in a format resembling a spreadsheet or a table.

Understanding Systematic Organization

All data contains elements or features and attributes by which they can be identified. For example, a person can be identified by attributes like their age, height, or hair color. This data is separated and stored in what's known as entities that represent those elements.

Entities and Tables

An entity is like a table. It contains rows and columns that store data relating to a specific element. These are relational elements related to one another. Entities could be physical representations like an employee, a customer, or a product, or they could be conceptual like an order, an invoice, or a quotation. Entities store data in a table-like format against the attributes or features related to the element.

Example: Online Store

• Customer Entity: Attributes like first name, last name, date of birth, and email.

• Product Entity: Attributes like product code, description, price, and availability.

Relations or Tables

In the relational database world, these entities are known as relations or tables. The attributes become the columns of the table. Each table row represents an instance of that entity. For example, an order table in an online store database could have rows that contain a unique order number, the name of the customer who placed the order, the product ordered, and the price of that product.

Types of Databases

As a Database Engineer, you'll work with many different types of databases. Here are a few common examples:

1. Object-Oriented Databases: Data is stored in the form of objects instead of tables or relations. For instance, an online bookstore's database could render authors, customers, books, and publishers as classes. The objects or instances of these classes would then hold the actual data.

2. Graph Databases: Data is stored in the form of nodes. Entities like customers, orders, and products are represented as nodes, and relationships between them are represented as edges.

3. Document Databases: Data is stored as JSON (JavaScript Object Notation) objects. Data is organized into collections like tables, with documents written in JSON that record data. For example, customer documents are held in a customer collection, while order and product documents are stored in order and product collections.

Where are Databases Stored?

A database can be hosted on a dedicated machine within the premises of an organization, or it could be hosted on the Cloud. Cloud databases are currently a more popular choice because they allow you to store, manage, and retrieve data to a Cloud platform and access data through the Internet. They provide a lower-cost option for data management and other similar options.

Summary

You should now understand the concept of a database. You should also be able to identify examples of databases and demonstrate how data is organized within a database. Great start! You'll be storing and managing data in no time.

Establishing Relationships in Databases

Picture yourself in the following scenario. You're managing the database of a large online store. Your database must be able to retrieve the customer's details from one table and then find the order recorded against another table. How does the database establish a relationship between these pieces of data? Over the next few minutes, you'll explore this process, and by the end of this section, you'll be able to explain why data in a database should be related and identify an instance of related data in a database.

Why Should Data Be Related?

Data stored in a database cannot exist in isolation. It must have a relationship with other data so that it can be processed into meaningful information. How do you make sure that all the data in your database is related? Let's explore how data is related by using the online store as our example.

Relating Data in an Online Store Database

In the database of your online store, you could have an order table and a customer table. To locate the details of a customer's order, you will check the order number against the customer ID. In other words, the database establishes a link between the data in the tables.

Customer Table

Let's look at the customer table in more detail. In this table, the columns are Customer ID, FirstName, LastName, and Email. In relational database terms, these are fields. Then there are several rows which contain data for each of these fields. In relational databases, they're known as records of the table. All these fields and rows work together to store information on the customer, also known as the entity. Every row and record in the customer table is an instance of the customer entity.

Example:

• Customer ID: C1

• FirstName: Sarah

• LastName: Hogan

• Email: sarah.hogan@example.com

Sarah Hogan, with a Customer ID of C1, is one customer instance. Katrina Langley, with a Customer ID of C4, is another customer instance.

Unique Identifiers

What's most important is that each of these customer instances or records must be uniquely identifiable. To avoid confusion within the database when two or more customers share similar info, like the same first name or last name, you can use a field that contains only unique values like the Customer ID. This is called a primary key field. It contains unique values that cannot be replicated elsewhere in the table. Even if two customers share the same name, they'll still have separate customer IDs. This means that your database can determine which customer is the required one.

Order Table

Let's look at the order table next. Just like the customer table, the order table also has fields and records. In this table, the primary key field is the Order ID. But there's also a field named Customer ID with the exact same data as the customer table.

Example:

• Order ID: O1

• Customer ID: C1

• Product: Laptop

• Price: $1000

Purpose of Customer ID in Order Table

The Customer ID in the order table helps identify who placed the order. By adding the Customer ID field to the order table, our relationship is established between the customer table and the order table. Because of this relationship, you can pull data in a meaningful way from both tables.

Primary and Foreign Keys

The Customer ID field in the order table is known as the foreign key field. A foreign key is a field in one table that connects to the primary key field in the original table, which in this case is the customer table. The Customer ID is the primary key of the customer table, but it becomes a foreign key in the order table. This way, the relationship is established and the data in these two tables are related.

Summary

You should now be able to explain the relationships between data in a database and identify instances of related data. Great work!

Relational Data Example Charts

Data gets collected and stored in databases from various sources for various reasons. For example, customer orders, student course enrollments, and user interaction and feedback to personalize content and improve services.

It’s important to organize data, process it, and present it efficiently to make it more useful and meaningful to people. The way data is related and presented enables people to form a better understanding of existing data. This understanding can be aided by relevant charts that present data visually using combinations of text, symbols, and graphic elements to illustrate the relationship between data in a meaningful way.

Charts can convey a great deal of information and can capture people’s attention in a way that helps them to make better decisions and take suitable actions. Here, you will learn about basic charts commonly used to relate data together and present it in a simple visual way.

Bar Chart

A bar chart is a graph that presents categorical data with rectangular bars, where the heights of the bars are proportional to the values that they represent.

Example: Business Revenue Statistics Bar Chart

The owner of a bookshop in London wants to know more about their business performance each year from 2019, especially during the COVID-19 lockdown. A bar chart could show how sales revenue has changed over the past few years.

• X-axis: Individual years (2019, 2020, 2021, 2022)

• Y-axis: Sales value

• Bars: Represent the sales achieved each year

The taller the bar, the greater the value of sales. In this case, the tallest bar is in 2018, indicating the most successful year, and the smallest bar is in 2022, indicating the worst year for sales.

Bubble Chart

A bubble chart shows how different values compare to each other in terms of bubble size. The smaller bubbles represent smaller values, and the larger bubbles represent larger values.

Example: Population Bubble Chart

This chart presents information about the 10 largest countries in the world in terms of population in 2015.

• Bubbles: Represent population size

• Large bubbles: China (1.4 billion), India (1.3 billion)

• Medium bubbles: USA (330 million), Indonesia (270 million)

• Small bubbles: Russia (145 million), Egypt (100 million)

The size of each bubble gives a good idea of the difference between the countries regarding population sizes.

Line Chart

A line chart presents information as a series of data points called “markers” connected by straight line segments. Line charts are widely used in most data analytics fields.

Example: Gold Price Line Chart

This chart depicts a company's gold price over the past month.

• Data Points: Start on April 10th, with gold at $62,650 per kg

• Line: Connects the dots to visualize the change in gold price over time

• Movement: Highlights positive and negative changes

Data analysts commonly use this chart to predict future market trends based on overall patterns.

Pie Chart

A pie chart displays how various data make up a whole of 100 percent. Each data point is allocated a "slice" of the pie according to its value.

Example: Sports Pie Chart

This chart depicts the type of sport students prefer in a class.

• Slices: Represent different sports

• Soccer: 50%

• Tennis and Swimming: Each 25%

The size of each slice helps to identify the percentage of students who prefer each sport.

Other Types of Charts

In addition to the charts introduced earlier, other charts can be used for different purposes. Examples include:

• Area Chart: Combines the line chart and the bar chart to compare two or more quantities of data.

• Dual Axis Chart

• Gantt Chart

• Heat Map

• Scatter Plot Chart

Choosing the Right Chart

Some charts can serve multiple purposes, whereas others are much better at conveying specific types of information to the audience. The answer to the question of which chart to choose depends on several factors, including:

• The target audience who will use the information

• The idea you intend to present

• The goal you want to achieve

Your choice of chart will be determined by the message you want to deliver to your audience, the type and amount of data you want to load to the graph, and other considerations. Once you have identified the audience and assessed the data, you can experiment with different charts to find the best option. If multiple charts are suitable, choose the one that engages your audience and boosts their interest in the information.

By considering all these factors, you should be able to identify the most appropriate chart that serves your purpose.

Evolution of Databases and Emerging Trends

You've probably heard of terms like big data and cloud databases. Maybe you've even encountered them in this course. But do you know what they mean? In this video, you'll discover more about these terms and you'll be able to identify different types of databases and explain how databases have evolved in response to new trends.

Evolution of Databases

Databases have been around for a long time and have been influenced by many different trends. They've undergone significant changes in recent decades, particularly due to the growth of the internet. Modern databases must be capable of storing ever-increasing amounts of unstructured data, which presents challenges since they traditionally store structured data.

Relational Databases and Their Limitations

Relational databases store structured data using tables, records, and relationships. However, as the need to store unstructured data grows, relational databases face limitations.

NoSQL Databases

To address these limitations, the trend in recent years has been to rely on NoSQL databases. NoSQL databases provide a flexible structure that allows for easy scaling and adaptation without complex data models. They are widely used by:

• Social media platforms

• The Internet of Things (IoT)

• Artificial intelligence (AI) applications

Types of NoSQL databases include:

• Document databases

• Key-value databases

• Graph databases

Big Data

Big data refers to complex data that increases in volume over time, growing exponentially. Sources of big data include:

• Social media platforms

• Online shopping sites

• IoT devices

Big data is characterized by being highly unstructured or semi-structured, making it challenging for traditional database systems to handle. Big data combines structured, semi-structured, and unstructured data from many sources, offering unique insights that can address complex business problems.

Applications of Big Data

Big data is highly valued across many industries for its ability to improve decision-making:

• Manufacturing Sector:

  • Predict equipment failure

  • Assess production processes

  • Respond to customer feedback

  • Anticipate future demands

• Retail Sector:

  • Anticipate customer demand

  • Improve customer experience

  • Analyze customer behavior and spending patterns

  • Identify pricing improvement opportunities

• Telecommunications Sector:

  • Plan for infrastructure investments

  • Design new services

  • Analyze service quality data

  • Plan for customer retention

Cloud Databases

Organizations are increasingly moving to cloud databases to avoid the infrastructure challenges of physical servers, such as maintenance and storage costs. Examples of cloud storage services include:

• Dropbox

• iCloud

Cloud databases provide an affordable solution for storing documents and other data.

Business Intelligence (BI)

Traditionally, databases were used solely for storing data. However, organizations now leverage business intelligence technologies and strategies to analyze data and extract valuable information, aiding in informed decision-making.

Emerging Trends in Database Technology

New trends are constantly emerging in database technology, and they will continue to advance over time. Currently, some of the leading trends include:

• NoSQL databases

• Big data analytics

• Cloud databases

• Business intelligence (BI) technologies

By understanding these trends, you can stay informed about the evolving landscape of database technology.

Interacting with Databases Using SQL

Introduction

At this stage in the course, you're familiar with the basics of databases and how they store and manage data. Now, let's delve into how you can interact with databases effectively using Structured Query Language (SQL).

What is SQL?

SQL, pronounced as "sequel", is the standard language for interacting with databases. It is particularly powerful for working with relational databases, which store data in tables with structured relationships. SQL enables data engineers and developers to perform various operations on databases.

Role of SQL in Databases

SQL plays a crucial role in databases by allowing users to perform the following operations, often referred to as CRUD operations:

• Create: Inserting new data into the database.

• Read: Retrieving data from the database.

• Update: Modifying existing data in the database.

• Delete: Removing data from the database.

These operations are fundamental to managing and manipulating data within a database system.

Interactions Database Engineers Establish with Databases

As a data engineer or web developer, interacting with databases involves executing SQL instructions on a Database Management System (DBMS). The DBMS serves as an intermediary between the SQL commands and the underlying database, ensuring that SQL instructions are executed efficiently and correctly.

SQL and Relational Databases

SQL is particularly well-suited for relational databases such as MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. These databases rely on SQL for querying and managing structured data effectively.

How DBMS Interprets SQL Instructions

A Database Management System (DBMS) interprets SQL instructions by transforming them into commands that the specific database understands. This translation process ensures that data operations are performed accurately and securely.

Conclusion

You now have a basic understanding of what SQL is and its role in interacting with databases. SQL is essential for performing CRUD operations and managing relational databases effectively. As you progress in the course, you'll deepen your knowledge of SQL and its practical applications in database management.

Understanding SQL Operations and Sublanguages

Introduction

Imagine you've just been tasked with creating a database for a college. This involves creating tables to store various aspects of college data, inserting initial data, and maintaining it through updates and deletions. SQL (Structured Query Language) and its operations, particularly CRUD (Create, Read, Update, Delete), will be pivotal in accomplishing these tasks.

Tasks SQL Syntax is Used For

SQL syntax is used for several essential tasks when building a database:

• Creating Database Objects: Using Data Definition Language (DDL) commands like CREATE to define databases, tables, and other schema objects.

• Manipulating Data: Using Data Manipulation Language (DML) commands such as INSERT, UPDATE, and DELETE to manage data within tables.

• Querying Data: Utilizing Data Query Language (DQL), primarily with the SELECT command, to retrieve specific data from tables.

• Controlling Access: Employing Data Control Language (DCL) commands such as GRANT and REVOKE to manage database security and user access privileges.

CRUD Operations

CRUD operations are fundamental in database management:

• Create: Adding new data or objects to the database using INSERT and CREATE.

• Read: Retrieving data from tables using SELECT.

• Update: Modifying existing data within tables using UPDATE.

• Delete: Removing data from tables using DELETE.

SQL Sublanguages

SQL is divided into several sublanguages or subsets:

• DDL (Data Definition Language): Used to define and manage database objects like tables (CREATE, ALTER, DROP).

• DML (Data Manipulation Language): Manipulates data within tables (INSERT, UPDATE, DELETE).

• DQL (Data Query Language): Retrieves data from tables (SELECT).

• DCL (Data Control Language): Manages access to data (GRANT, REVOKE).

Understanding Each Sublanguage

Data Definition Language (DDL)

DDL commands are used to define the structure of the database:

• CREATE: Creates new database objects like tables.

• ALTER: Modifies the structure of existing database objects.

• DROP: Deletes database objects like tables.

Data Manipulation Language (DML)

DML commands manage data within tables:

• INSERT: Adds new data into tables.

• UPDATE: Modifies existing data within tables.

• DELETE: Removes data from tables.

Data Query Language (DQL)

DQL focuses on retrieving data from tables:

• SELECT: Retrieves specific data based on defined criteria from one or multiple tables.

Data Control Language (DCL)

DCL commands manage database security and access:

• GRANT: Provides users with specific privileges to database objects.

• REVOKE: Removes previously granted privileges from users.

Conclusion

You now understand the essential SQL operations and sublanguages required to interact with databases effectively. SQL serves as the bridge between users and databases, enabling the creation, manipulation, querying, and security management of data. As you continue learning, you'll deepen your proficiency in using SQL to manage and optimize databases.

Advantages of SQL in Database Interactions

Introduction

By now, you're familiar with the basics of databases and might have encountered simple SQL syntax. SQL (Structured Query Language) is widely used by developers to interact with relational databases due to its numerous advantages. Let's explore these advantages and see how they facilitate database tasks.

Advantages of SQL

1. Simplicity and Ease of Use

SQL requires minimal coding skills:

• It consists of a concise set of keywords for performing CRUD operations (Create, Read, Update, Delete) on databases.

• Developers can quickly write complex queries with SQL, making it highly user-friendly and efficient.

2. Standardization and Compatibility

SQL is a standardized language:

• It can be used with all relational databases such as MySQL, PostgreSQL, Oracle, and SQL Server.

• The standardized nature ensures widespread support, abundant resources, and community knowledge.

3. Portability

SQL code is portable across different platforms:

• Once written, SQL queries can be executed on any hardware or operating system where the database software is installed.

• This portability allows seamless deployment from development environments to production servers without modifications.

4. Comprehensive Functionality

SQL covers all aspects of database management:

• DDL (Data Definition Language): Defines and manages database structure (CREATE, ALTER, DROP).

• DML (Data Manipulation Language): Manipulates data within tables (INSERT, UPDATE, DELETE).

• DQL (Data Query Language): Retrieves data from tables (SELECT).

• DCL (Data Control Language): Manages access and permissions to data (GRANT, REVOKE).

5. Efficiency in Data Processing

SQL allows for efficient processing of large volumes of data:

• Queries are optimized for performance, ensuring quick execution and retrieval of data.

• This efficiency is crucial for handling real-time data processing and complex analytical tasks.

Conclusion

SQL stands out as a simple, standardized, portable, comprehensive, and efficient language for interacting with relational databases. Its capabilities empower developers to manage databases effectively, from creating structures to manipulating data and ensuring security. As you continue your journey with SQL, you'll discover its versatility in handling diverse database tasks across different environments.

Interacting with Databases Using SQL: Basics and Subsets

Introduction

Interacting with databases using SQL (Structured Query Language) involves understanding its subsets: Data Definition Language (DDL), Data Manipulation Language (DML), and Data Query Language (DQL). These subsets are essential for creating databases, managing data, and querying information. Let's explore each subset in the context of developing a college database.

Creating a Database with DDL

Firstly, SQL's DDL subset allows you to create and manage database structures:

• Use CREATE DATABASE to initiate a new database. For instance, CREATE DATABASE college; establishes a database named "college".

Creating Tables and Populating Data with DML

Next, utilize DDL to create tables and DML to manage data within them:

• CREATE TABLE student defines a table structure for storing student information.

To insert data into the student table:

• Employ INSERT INTO student (ID, first_name, last_name, date_of_birth) VALUES (1, 'John', 'Murphy', '2000-01-01'); to add a student's details.

To update existing data, like correcting a date of birth:

• Use UPDATE student SET date_of_birth = '2001-05-15' WHERE ID = 1; to modify specific records.

To delete data, such as removing a student's record:

• Execute DELETE FROM student WHERE ID = 3; to delete the data of the student with ID 3.

Querying Data with DQL

Finally, utilize DQL to retrieve specific data from tables:

• Use SELECT * FROM student WHERE ID = 1; to retrieve all information about the student with ID 1, returning "John Murphy".

Conclusion

SQL, with its subsets DDL, DML, and DQL, provides a powerful framework for interacting with relational databases. You've now grasped the fundamentals of SQL syntax and its subsets, setting the stage for deeper exploration in this specialization. As you progress, you'll gain hands-on experience to further enhance your SQL proficiency.

Understanding Database Tables

Introduction

At this stage of the course, you're probably familiar with the basics of how databases store and interact with data. But how do they store all this data and present it logically? The answer lies in the structure of tables. By the end of this reading, you'll be able to outline what a database table is conceptually and explain how data is structured within it.

Database Tables: The Basics

A database table is composed of rows and columns which hold data. In a database, multiple tables are stored, and these tables are known as relations because they relate to one another. Conceptually, a table can also be referred to as an entity. In object-oriented databases (OODBs), an entity is an object with attributes, similar to columns or fields in a table. Thus, the terms table, entity, and object all refer to the same concept.

Columns (Fields or Attributes)

Each column in a table has a unique name and a specified data type. For example, in a table containing employee data, columns might include ID and Role. Each column can hold different types of data such as numeric or string values. The data type of a column defines what type of value it can hold, such as integer, character, date and time, etc. This serves as a guideline for SQL, indicating what data type to expect in each column and how to interact with the underlying data.

Rows (Records)

A row, also known as a record, is a combination of columns that contain data. In the employee table example, each row represents a single employee record. Rows are collections of related data organized in columns.

Data Types

The data type of a column defines what type of value it can hold. Data types can vary depending on the database system (e.g., MySQL, SQL Server, Access). Always refer to the documentation of the relevant database system to check supported data types. Common data types include:

• String Data Types: For storing characters and strings of characters.

• Numeric Data Types: For storing exact or whole numbers and approximate numbers.

• Date and Time Data Types: For storing information on dates and times.

• Binary Data Types: For storing images, files, and other binary data.

Domains

A domain is the set of legal values that can be assigned to an attribute. This ensures that the values a field can hold are well-defined. For example, a numerical domain can only contain numbers, while a string domain can only contain characters or strings of characters. Domains include length values and other relevant rules that define their function.

Primary Keys

Each row or record in a table is uniquely identified by a primary key. A primary key is a column (or combination of columns) that contains unique values. In the employee table, the ID column serves as the primary key since each ID is unique. Other columns may contain repeating values (e.g., two employees sharing the same name or role). If no single column has unique values, a combination of columns can be used as the primary key.

Conclusion

You should now be familiar with the structure of a database table and the key concepts such as columns, rows, data types, domains, and primary keys. Understanding these elements is crucial for effectively managing and interacting with database tables. Great work!

Tables Overview

Introduction

In this reading, you will explore tables in a relational database in more depth. You have already been introduced to the concept of a table within a database and reviewed a few examples. You also gained a basic understanding of what a primary key does in a table. This reading aims to examine tables in terms of their structure, data types, primary and foreign keys, and table constraints.

Structure of a Table

A table is the most basic type of database object in relational databases. It stores data in rows and columns, similar to a spreadsheet. Rows run horizontally and represent each record, while columns run vertically and define each field. Each column has a name that describes the data stored in it, such as FirstName, LastName, ProductID, and Price. The intersection of a row and a column is a cell, where an item of data is stored.

Data Types

Every column in a table has a data type, defined by SQL (Structured Query Language). A data type specifies the type of value that can be stored in a column. Common data types include:

• Numeric Data Types: INT, TINYINT, BIGINT, FLOAT, REAL

• Date and Time Data Types: DATE, TIME, DATETIME

• Character and String Data Types: CHAR, VARCHAR

• Binary Data Types: BINARY, VARBINARY

• Miscellaneous Data Types:

  • Character Large Object (CLOB): For storing large text blocks.

  • Binary Large Object (BLOB): For storing binary data like images.

Example Table: Student Table

Consider a student table that stores data about students, with columns such as:

• studentID

• firstName

• lastName

• dateOfBirth

• homeAddress

• faculty

Each row in this table represents a student's record, and each cell contains a piece of data, such as studentID = 1, firstName = Emily, lastName = Williams, etc. The data types for the columns might be INT for studentID, VARCHAR for firstName and lastName, and DATE for dateOfBirth.

Tables in a Relational Database

In a relational database, multiple tables represent the structure of the backend of a software system. For example, in a Student Information System, tables might include Student, Teacher, Class, and Subject. In relational database terminology, a table is also known as a relation, and a row (or record) is known as a tuple. Each table or relation has its own schema, which includes the table's name, its attributes, and their data types.

Primary Key

A primary key is a column (or combination of columns) that uniquely identifies a particular row in a table. For example, in the student table, the studentID uniquely identifies each row. Other columns, like firstName and lastName, might contain duplicate values, so they cannot be used as a primary key. In some cases, a primary key can be a composite key, comprising more than one column when a single column cannot uniquely identify a record.

Example: Composite Primary Key

Consider a table where the EMP_ID values are not unique. The combination of EMP_ID and DEPT_ID can uniquely identify each record, forming a composite primary key.

Foreign Key

Tables in a database are often linked through relationships. A foreign key is a column in one table that is the primary key of another table. For example, the studentID in the student table can be a foreign key in the department table, establishing a relationship between the two tables.

Integrity Constraints

Integrity constraints are rules that ensure the accuracy and consistency of data within a database. There are three main types of integrity constraints:

Key Constraints

A primary key constraint specifies that a column (or columns) must uniquely identify each row in a table. The primary key should never be NULL or duplicated. For example, in the student table, the studentID serves as the primary key.

Domain Constraints

Domain constraints define the permissible values for a column. For example, a contact number cannot exceed ten digits, and an address cannot be stored in the firstName column.

Referential Integrity Constraints

Referential integrity constraints ensure that a foreign key value in one table matches a primary key value in another table. This maintains the consistency of relationships between tables. For instance, the studentID in the department table must exist in the student table.

Conclusion

In this reading, you learned more about tables in a relational database, including their structure, data types, primary and foreign keys, and integrity constraints. Understanding these elements is crucial for effectively managing and interacting with relational databases.

Understanding Keys in Relational Databases

Introduction

At this stage of the course, you're likely familiar with the relational database model. However, to fully grasp how a relational database model works, you need to understand how tables within a database are related. Essentially, relationships are established between tables using keys. By the end of this video, you'll be able to identify the main keys used in tables in a relational database and explain the relationship between keys in a table.

Relational Database Model

The relational database model is based on two main concepts:

1. Entities: Defined as tables.

2. Relations: Connections between related tables.

To understand how this model works, you need to be familiar with the different key attributes that exist in a relational database. Let's use the example of a sports competition that uses three tables to keep track of the league: the league table, the teams table, and the points table.

Example: Sports Competition Database

• League Table: Tracks each team's position in the league, their name, and the state they represent.

• Teams Table: Tracks the team name, the team captain, and the team coach.

• Points Table: Records the team's position in the league, the team's name, and how many points the team has this season.

Notice that the team's table includes the team name, which also belongs to the league table. These attributes can be of a simple attribute type that holds a single value, or a multi-value attribute that can hold multiple values. However, multi-value attributes should be avoided in relational database design.

Key Attributes in Relational Databases

Let's explore some examples of attribute keys using the staff table.

Key Attribute

A key attribute is a value used to uniquely identify an individual record of data in a table. For example, in the staff table, the key attribute is staff ID. This attribute has a unique value in each row, making it perfect for uniquely identifying each record of data.

Candidate Key

A candidate key is any attribute that contains a unique value in each row of the table. In the case of the staff table, both the staff ID and contact numbers are examples of candidate keys. Each has a unique value in each row, while other columns can contain repeated information, making them non-key attributes.

Composite Key

A composite key is composed of two or more attributes to form a unique value in each row. For example, in the staff table, a combination of staff name and staff title could be a composite key, assuming there isn't another instance of the same combination elsewhere in the table. Composite keys are used when a single attribute key can't be identified.

Primary Key

A primary key is a key attribute that uniquely identifies each record in a table. In the staff table, the staff ID is the primary key.

Alternate Key (Secondary Key)

An alternate key, also known as a secondary key, is a candidate key that was not selected to be the primary key. It also contains a unique value in each field. For the staff table, the contact number is a secondary key.

Foreign Key

A foreign key is an attribute in a table that references a unique key in another table, typically the primary key. For example, the staff ID might also be a foreign key in one or more tables within the college database. The relationship between primary and foreign keys will be discussed in more detail later in the course.

Conclusion

You are now familiar with the different types of keys in a relational database. Understanding these keys is crucial for managing relationships and ensuring data integrity within a relational database.

Understanding Numeric Data Types in Databases

Introduction

You probably know that database tables store data in the form of columns and rows. But how do you ensure that each column accepts the correct type of data? For instance, how do you make sure that your cost column stores values in decimal format while your product quantity column accepts only positive numbers? This is where data types come into play. Data types determine what kind of data is accepted by each field in your table. Over the next few minutes, you'll learn to explain the numeric data type in a database and differentiate between integer and decimal data types.

What are Data Types?

When you create a table in a database, you need to define column names and the data type of the content that will reside in those columns. A data type tells the database management system (DBMS), such as MySQL, how to interpret the value of the column. Data types maintain data in the right format and ensure that the value of each column is as expected. The most commonly used data types are numeric, string, and date and time data types.

Example: Online Store Database

Consider a table from the database of an online store that collects information on customers in the form of columns called customer name, order price, product quantity, and total price. Each of these columns must store data in a suitable data type:

• Customer Name: String data type.

• Order Price: Date data type.

• Product Quantity and Total Price: Numeric data type.

Focus on Numeric Data Types

Numeric data types are the generic term used for all specific data types that let a column store data as numbers in the database. The two most common numeric data types used in databases are the integer data type, used for whole number values, and the decimal data type, used for numbers with fractional values.

Integer Data Type

The integer data type is used for columns that hold whole numbers only. For example, in the product quantity column, defined as an integer data type, fractional numbers can be inserted, but they will always be automatically rounded up or down to the nearest whole number by the database.

Decimal Data Type

The decimal data type is used for columns that hold fractional numbers. For instance, in the total price column, defined as a decimal type, an item that costs $80.90 is a fractional value: 80 is the whole number, and 90 is the decimal. Whole numbers can also be inserted, and the database will add a decimal point along with a fractional value of zero.

Variants of Numeric Data Types

In most database management systems, you'll find different types of integer and decimal data types, each intended to store specific minimum and maximum number values. For example, in MySQL DBMS:

• TINYINT: Used for very small integer values, with a maximum possible value of 255.

• INT: Used to store very large numbers, with a maximum value of over four billion.

These data types can accept both negative and positive values. In some DBMSs, you can also force columns to accept only positive numbers, increasing the maximum value they can store.

Conclusion

You should now be able to explain the numeric data type in a database and differentiate between integer and decimal data types. Understanding these data types is crucial for ensuring data integrity and proper functioning of your database.

Ensuring Data Accuracy with Database Constraints

Introduction

To ensure the accuracy and reliability of the data in your database, you must limit the type of data that can be entered into your database tables. In this overview, you'll learn how to describe the purpose of constraints in a database and identify default constraints to set default values in a table.

Purpose of Database Constraints

Database constraints are used to limit the type of data that can be stored in a table, ensuring that all data inserted is accurate and reliable. If a data operation violates a constraint, the database will abort the operation. For example, if there is an attempt to insert invalid data into a table, the database will recognize the invalid data and reject it. Constraints can be applied at the column level or the table level. For instance, a foreign key constraint can prevent actions that would destroy links between tables.

Common Database Constraints

Two of the most used database constraints are:

• NOT NULL: Prevents empty value fields.

• DEFAULT: Assigns default values to columns.

NOT NULL Constraint

The NOT NULL SQL constraint ensures that data fields are always completed and never left blank.

Example: Online Store Customer Table

Consider a table from an online store that records the IDs and names of customers. The table has two columns: customer_id and customer_name. These columns must always contain data. If there's no data inserted into either column, the creation of a new customer record is aborted.

Implementing NOT NULL

The NOT NULL constraint is implemented using a SQL statement. A typical NOT NULL SQL statement begins with the creation of a basic table in the database:

CREATE TABLE customer (
    customer_id INT NOT NULL,
    customer_name VARCHAR(50) NOT NULL
);

This ensures that neither column will accept null values. Any operation that attempts to place a null value in these columns will fail, such as inserting or updating data.

DEFAULT Constraint

The DEFAULT constraint sets a default value for a column if no value is specified. This means that if no data is entered for a specific field within a column, the table will automatically insert a default value instead.

Example: Football Club Player Table

Consider a table that holds player records for a football club's database. The table, named player, contains two columns: player_name and city. Most players in this club are from Barcelona, so a default value of "Barcelona" can be specified for the city column. This way, you don't have to repeatedly enter "Barcelona" for each new player.

Implementing DEFAULT

The DEFAULT constraint is incorporated into a SQL statement as follows:

CREATE TABLE player (
    player_name VARCHAR(50) NOT NULL,
    city VARCHAR(50) DEFAULT 'Barcelona'
);

Now, when adding data into the table for a new player, "Barcelona" will be inserted automatically for players from that city if no value is provided.

Conclusion

You should now be familiar with the importance of using database constraints to ensure data accuracy and reliability. You should also be able to explain database constraints as methods for enforcing rules on a column or table level. Good work!

Notes for Introduction to Databases for Back-End Development

Creating and Dropping Databases with SQL

Overview

• You've been hired by an online bookstore to build and maintain databases for millions of books and customers.

• The task involves creating, altering, and maintaining databases that can handle large amounts of data and transactions.

• SQL (Structured Query Language) commands are used to create and manage databases.

Creating a Database

1. Purpose: Before creating a database, understand its purpose. For example, an online bookshop database should record data like book titles, authors, customers, and sales.

2. Syntax: Use the CREATE DATABASE command to create a database.

   • Example:

       CREATE DATABASE bookstore2_db;
     

   • Ensure the database name is meaningful, relevant, unique, and has a maximum of 63 characters.

   • If the database name doesn't meet these requirements, an error message will appear.

Dropping a Database

1. Purpose: Sometimes, you need to remove or delete a database.

2. Syntax: Use the DROP DATABASE command to delete a database.

   • Example:

       DROP DATABASE bookstore_db;
     

   • This command will remove the specified database from the system.

Steps in SQL Interface

1. Creating a Database:

   • Open your SQL interface.

   • Type the CREATE DATABASE statement followed by the database name.

   • End the statement with a semicolon (;) and run the command.

   • The new database should appear in the sidebar or database list.

2. Dropping a Database:

   • Open your SQL interface.

   • Type the DROP DATABASE statement followed by the database name you want to delete.

   • Run the command to delete the database.

Practical Example

1. Creating a Database:

   • Command:

       CREATE DATABASE bookstore2_db;
     

   • After running the command, bookstore2_db will be created and visible in your database management system.

2. Dropping a Database:

   • Command:

       DROP DATABASE bookstore_db;
     

   • After running the command, bookstore_db will be deleted from your database management system.

Conclusion

• You have learned how to create and delete databases using SQL syntax.

• These basic commands are essential for managing databases effectively.

Notes for Introduction to Databases for Back-End Development

Creating Tables with SQL

Overview

• Organizing substantial amounts of data is crucial for managing databases effectively.

• SQL (Structured Query Language) allows you to create tables within your database to hold and organize data.

Creating a Table

1. Syntax: Use the CREATE TABLE command to create a new table.

   • Example:

       CREATE TABLE table_name (
         column1_name datatype,
         column2_name datatype,
         ...
       );
     

   • Begin with CREATE TABLE followed by the table name.

   • Within parentheses, list each column name followed by its datatype.

   • Each column definition is separated by a comma.

Practical Example

1. Prerequisite: Ensure a database exists on the server before creating tables.

2. Creating a Table:

   • Example: Creating a customers table in a bookstore database.

   • Command:

       CREATE TABLE customers (
         customer_name VARCHAR(255),
         phone_number INT
       );
     

   • Steps:

     • Write the CREATE TABLE command followed by the table name customers.

     • Add an open parenthesis (.

     • Define the first column customer_name with datatype VARCHAR(255) (to hold string data up to 255 characters).

     • Add a comma and define the second column phone_number with datatype INT (to hold whole numbers).

     • Add a closing parenthesis ) and a semicolon ;.

     • Execute the statement to create the table.

Steps in SQL Interface

1. Creating a Table:

   • Open your SQL interface.

   • Type the CREATE TABLE statement as shown in the example.

   • Run the command to create the customers table.

   • The table will be stored in the database and ready for data insertion.

Conclusion

• You have learned how to create tables in a database using SQL syntax.

• These fundamental commands are essential for structuring and organizing data within your database.

Notes for Introduction to Databases for Back-End Development

Altering Tables with SQL

Overview

• Database developers frequently restructure tables by adding, deleting, or modifying columns.

• SQL (Structured Query Language) allows you to alter tables and modify their attributes efficiently.

Altering a Table

1. Syntax: Use the ALTER TABLE command to alter an existing table.

   • Example:

       ALTER TABLE table_name
       ADD (column_name datatype, ...);
     

   • Begin with ALTER TABLE followed by the table name.

   • Use the ADD keyword to add new columns.

   • Within parentheses, list each new column name followed by its datatype.

Practical Example

1. Prerequisite: Ensure you have a database and table with data.

2. Adding Columns:

   • Example: Adding age, nationality, and country columns to a students table in the college database.

   • Command:

       ALTER TABLE students
       ADD (age INT,
            nationality VARCHAR(255),
            country VARCHAR(50));
     

   • Steps:

     • Write the ALTER TABLE command followed by the table name students.

     • Use the ADD keyword to indicate new columns.

     • Define the age column with datatype INT.

     • Define the nationality and country columns with datatype VARCHAR, setting character limits.

Removing Columns

1. Syntax: Use the DROP COLUMN command to remove a column.

   • Example:

       ALTER TABLE table_name
       DROP COLUMN column_name;
     

   • Begin with ALTER TABLE followed by the table name.

   • Use the DROP COLUMN keyword to specify the column to be deleted.

2. Example:

   • Removing the nationality column from the students table.

   • Command:

       ALTER TABLE students
       DROP COLUMN nationality;
     

   • Steps:

     • Write the ALTER TABLE command followed by the table name students.

     • Use the DROP COLUMN keyword followed by the column name nationality.

     • Run the statement and confirm the deletion if prompted.

Modifying Column Attributes

1. Syntax: Use the MODIFY command to change column attributes.

   • Example:

       ALTER TABLE table_name
       MODIFY column_name datatype(new_value);
     

   • Begin with ALTER TABLE followed by the table name.

   • Use the MODIFY keyword to indicate changes to a column.

2. Example:

   • Changing the character limit of the country column from 50 to 100 in the students table.

   • Command:

       ALTER TABLE students
       MODIFY country VARCHAR(100);
     

   • Steps:

     • Write the ALTER TABLE command followed by the table name students.

     • Use the MODIFY keyword followed by the column name country and new datatype VARCHAR(100).

     • Execute the statement to update the column attributes.

Conclusion

• You have learned how to alter and modify tables in a database using SQL syntax.

• These commands are essential for maintaining and updating the structure and integrity of your database tables.

Notes for Introduction to Databases for Back-End Development

Inserting Data into Tables with SQL

Overview

• Adding new rows and columns to existing tables or creating new tables is a common task.

• SQL allows you to insert data quickly using the INSERT INTO statement.

Inserting Data

1. Syntax:

   • The basic structure of an INSERT INTO statement:

       INSERT INTO table_name (column1, column2, ...)
       VALUES (value1, value2, ...);
     

   • Begin with INSERT INTO followed by the table name.

   • Specify the columns within parentheses, separated by commas.

   • Use the VALUES keyword followed by the list of values to insert, also within parentheses.

2. Multiple Rows:

   • You can insert multiple rows at once:

       INSERT INTO table_name (column1, column2, ...)
       VALUES (value1, value2, ...),
              (value3, value4, ...);
     

   • Separate each row of values with a comma.

Practical Example

1. Single Row Insertion:

   • Example: Inserting a new player into the players table in a sports club database.

   • Command:

       INSERT INTO players (ID, Name, Age, Start_date)
       VALUES (1, 'Yuval', 25, '2020-10-15');
     

   • Steps:

     • Write the INSERT INTO command followed by the table name players.

     • Specify the columns: ID, Name, Age, and Start_date.

     • Use the VALUES keyword and provide the corresponding values.

     • Ensure non-numeric values are within quotation marks.

     • Execute the statement to insert the data.

2. Multiple Rows Insertion:

   • Example: Inserting data for two new players, Mark and Karl.

   • Command:

       INSERT INTO players (ID, Name, Age, Start_date)
       VALUES (2, 'Mark', 27, '2020-10-12'),
              (3, 'Karl', 26, '2020-10-07');
     

   • Steps:

     • Write the INSERT INTO command followed by the table name players.

     • Specify the columns: ID, Name, Age, and Start_date.

     • Use the VALUES keyword and provide the corresponding values for each player, separated by commas.

     • Execute the statement to insert the data for both players.

Retrieving Data

• You can show existing data in the table using the SELECT statement.

  • Example:

      SELECT * FROM players;
    

  • Steps:

    • Write the SELECT clause followed by an asterisk (*) to return all columns.

    • Use the FROM keyword and specify the table name.

    • Execute the statement to retrieve and display all data from the players table.

Conclusion

• You have learned how to identify and understand the INSERT SQL syntax.

• You can now insert data into tables using the INSERT INTO clause.

• You also know how to retrieve existing data using the SELECT statement.

Good luck with your database development tasks!

Notes on Creating Tables in Databases

Overview

Introduction

• Creating a table in a database is essential for organizing and storing data effectively.

• SQL provides commands such as CREATE DATABASE for database creation and CREATE TABLE for table creation.

Objective

• This reading aims to provide a practical understanding of the CREATE TABLE statement in SQL.

• It emphasizes correct usage of syntax and highlights important considerations when creating a database table.

Important Points on Creating a Table

1. Meaningful Naming:

   • Always assign meaningful names to tables and their columns or fields.

2. Data Types:

   • Data types vary across different database systems (e.g., NUMBER in Oracle vs INT in MySQL).

   • Refer to the specific database system’s documentation for supported data types.

3. Specifying Lengths:

   • Specify appropriate lengths for data types where applicable.

   • Example: Use VARCHAR for text-based data to save space (VARCHAR(100) allows up to 100 characters).

Using the CREATE TABLE Statement

Syntax Example

• Example of creating a customers table in a sample database:

    CREATE TABLE customers (
        CustomerId INT,
        FirstName VARCHAR(40),
        LastName VARCHAR(20),
        Company VARCHAR(80),
        Address VARCHAR(70),
        City VARCHAR(40),
        State VARCHAR(40),
        Country VARCHAR(40),
        PostalCode VARCHAR(10),
        Phone VARCHAR(24),
        Fax VARCHAR(24),
        Email VARCHAR(60),
        SupportRapid INT
    );
  

• Explanation:

  • Begin with CREATE TABLE followed by the table name (customers).

  • Within parentheses, list each column with its name and data type.

  • Specify the length for VARCHAR columns where appropriate.

  • Use INT for numeric columns (CustomerId and SupportRapid).

  • End the statement with a semicolon (;).

Conclusion

• The CREATE TABLE statement in SQL allows for the creation of structured tables within a database.

• It is crucial to adhere to syntax rules and best practices for naming and defining data types and lengths.

• Refer to database system documentation for specific details on supported data types and their usage.

Good luck with creating and structuring your database tables!

SQL SELECT Statement: Querying Data from Tables

Overview

Introduction

• Querying data from tables is a fundamental aspect of working with databases.

• SQL provides the SELECT statement for retrieving specific data from tables based on defined criteria.

Objectives

• This reading focuses on understanding and utilizing the SQL SELECT statement effectively.

• You will learn how to retrieve data from tables, perform calculations, and utilize functions like date and time queries and concatenation.

Syntax and Examples

Basic SQL SELECT Statement

• The basic syntax of the SELECT statement:

    SELECT column_name
    FROM table_name;
  

• Example: Retrieve player names from a soccer club database table named players:

  • SELECT name FROM players;

  • Explanation:

    • SELECT retrieves data from specified columns (name in this case).

    • FROM identifies the source table (players).

Retrieving Data from Multiple Columns

• Example of retrieving name and level columns:

  • SELECT name, level FROM players;

Retrieving All Data from a Table

• Methods to retrieve all columns from the players table:

  • Explicitly listing column names: SELECT ID, name, age, level FROM players;

  • Using asterisk (*) shorthand: SELECT * FROM players;

Conclusion

• The SELECT statement in SQL is versatile and allows for precise querying of data from tables.

• It supports retrieving specific columns or all columns (*) from tables.

• Understanding SELECT statement syntax and its variations empowers efficient data retrieval in SQL databases.

Next time you need to query data in your database, apply these methods to retrieve the information you need effectively.

SQL INSERT INTO SELECT Statement: Populating Tables from Another Table

Overview

Introduction

• In database management, there are scenarios where you need to retrieve data from one table and insert it into another table. SQL provides the INSERT INTO SELECT statement for this purpose.

• This reading explores the syntax and application of the INSERT INTO SELECT statement to transfer data between tables efficiently.

Objectives

• Understand the syntax of the INSERT INTO SELECT statement.

• Learn how to use the statement to insert data from a source table into a target table.

• Apply the INSERT INTO SELECT statement with an example scenario.

Syntax and Examples

SQL INSERT INTO SELECT Statement Syntax

• The basic syntax of the INSERT INTO SELECT statement:

    INSERT INTO target_table (column1, column2, ...)
    SELECT columnX, columnY, ...
    FROM source_table;
  

• Example: Populate the countryName column in the country table using data from the players table:

      INSERT INTO country (countryName)
      SELECT country
      FROM players;
  

  • Explanation:

    • INSERT INTO specifies the target table (country) and the column (countryName) where data will be inserted.

    • SELECT retrieves data (country) from the players table, acting as the source table.

Example Scenario

• Assume the players table contains player data and the country table requires country names.

• Using the INSERT INTO SELECT statement, populate the countryName column in the country table with data from the players table.

Conclusion

• The INSERT INTO SELECT statement is used to copy data from one table into another.

• It efficiently transfers data while maintaining database integrity and structure.

• Understanding and applying INSERT INTO SELECT enhances your ability to manage and manipulate data across tables in SQL databases.

Now you can confidently utilize the INSERT INTO SELECT statement to populate tables with data from other tables in your database.

Updating Data in SQL Tables: Understanding the UPDATE Statement

Overview

Introduction

• The SQL UPDATE statement is used to modify existing records in a table.

• This reading explores the syntax and application of the UPDATE statement to update specific columns in a table based on certain conditions.

Objectives

• Understand the syntax of the UPDATE statement.

• Learn how to use the statement to update data in a table.

• Explore examples of updating single and multiple records using the UPDATE statement.

Syntax and Examples

SQL UPDATE Statement Syntax

• The basic syntax of the UPDATE statement:

    UPDATE table_name
    SET column1 = value1, column2 = value2, ...
    WHERE condition;
  

• Example: Update the home_address and contact_number columns for the student with ID = 3 in the student_table:

    UPDATE student_table
    SET home_address = 'New Home Address', contact_number = '1234567890'
    WHERE ID = 3;
  

• Explanation:

  • UPDATE specifies the table (student_table) to be updated.

  • SET specifies the columns (home_address and contact_number) to be updated along with their new values ('New Home Address' and '1234567890').

  • WHERE specifies the condition (ID = 3) to identify the record(s) to be updated.

Example Scenarios

Updating Information for One Student

• Update the home address and contact number for a student with ID = 3:

    UPDATE student_table
    SET home_address = 'New Home Address', contact_number = '1234567890'
    WHERE ID = 3;
  

Updating Information for Multiple Students

• Update the college address to 'Harper Building' for all engineering students:

    UPDATE student_table
    SET college_address = 'Harper Building'
    WHERE department = 'engineering';
  

Updating Multiple Columns in Multiple Records

• Update both home address and college address for specific records:

    UPDATE student_table
    SET home_address = 'New Address', college_address = 'New College'
    WHERE department = 'engineering';
  

Conclusion

• The UPDATE statement in SQL allows for the modification of existing data in tables.

• It is used with SET to specify the columns and new values, and WHERE to specify the condition for updating specific records.

• Understanding how to use UPDATE enhances your ability to manage and maintain data integrity in SQL databases effectively.

Now you can confidently use the UPDATE statement to make targeted changes to data within your SQL tables.

Deleting Records in SQL Tables: Using the DELETE Statement

Overview

Introduction

• The SQL DELETE statement is used to remove one or more records from a table in a database.

• This guide explores the syntax and examples of using the DELETE statement to delete specific records or all records from a table.

Objectives

• Understand the syntax of the DELETE statement.

• Learn how to delete single and multiple records based on specific conditions.

• Explore examples of deleting records from a table using SQL.

Syntax and Examples

SQL DELETE Statement Syntax

• The basic syntax of the DELETE statement:

    DELETE FROM table_name
    WHERE condition;
  

• Example: Delete a record for a student with last name 'Miller' from the student_table:

    DELETE FROM student_table
    WHERE last_name = 'Miller';
  

• Explanation:

  • DELETE FROM specifies the table (student_table) from which records will be deleted.

  • WHERE specifies the condition (last_name = 'Miller') to identify the record(s) to be deleted.

Example Scenarios:

Deleting a Single Record

• Delete the record for a student with last name 'Miller':

DELETE FROM student_table
WHERE last_name = 'Miller';

Deleting Multiple Records

• Delete records for all students in the 'engineering' department:

DELETE FROM student_table
WHERE department = 'engineering';

Deleting All Records

• Delete all records from the student_table:

DELETE FROM student_table;

• Note: Be cautious when using DELETE FROM table_name; as it deletes all records from the table.

Conclusion

• The DELETE statement in SQL allows for the removal of records from a table based on specified conditions.

• It is used with WHERE to specify which records to delete based on certain criteria.

• Understanding how to use DELETE effectively ensures proper management and maintenance of data integrity in SQL databases.

Now you have a solid understanding of how to use the DELETE statement to delete records from your SQL tables.

Introduction to Databases for Back-End Development

SQL Arithmetic Operators

Overview

• Operators in SQL are essential tools for manipulating data within a database.

• Arithmetic operators specifically perform mathematical calculations on data.

Types of Arithmetic Operators

• Addition (+): Adds two operands together.

• Subtraction (-): Subtracts the second operand from the first.

• Multiplication (*): Multiplies two operands.

• Division (/): Divides the first operand by the second.

• Modulus (%): Provides the remainder of a division operation.

Examples of SQL Arithmetic Operators

• Addition: SELECT 10 + 15;

  • Result: 25

• Subtraction: SELECT 20 - 5;

  • Result: 15

• Multiplication: SELECT 5 * 5;

  • Result: 25

• Division: SELECT 10 / 2;

  • Result: 5

• Modulus: SELECT 100 % 10;

  • Result: 0

Practical Use of SQL Arithmetic Operators

• These operators can be used to calculate salaries, allowances, or any numeric computations needed within a database.

Conclusion

• SQL arithmetic operators are fundamental for performing mathematical operations in a database environment.

• Understanding these operators allows for efficient data manipulation and computation.

• Practice using these operators to perform various calculations enhances your proficiency in SQL for back-end development tasks.

Introduction to Databases for Back-End Development

SQL Arithmetic Operator Examples

Overview

Arithmetic operators in SQL are essential for performing mathematical operations on numerical data within database tables. They are utilized in both the SELECT and WHERE clauses to manipulate and filter data effectively.

Addition Operator

The addition operator (+) adds values from two columns in a table.

SELECT salary + allowance FROM employee;

Output:

26000
56000
53000
31000

Using the addition operator in the WHERE clause:

SELECT * FROM employee WHERE salary + allowance = 25000;

Output

employee_ID | employee_name | salary | allowance
------------|---------------|--------|----------
1           | Alex          | 24000  | 1000
4           | Sam           | 24000  | 1000

Subtraction Operator

The subtraction operator (-) subtracts values of one column from another.

SELECT salary - tax FROM employee;

Output:

23000
53000
50000
23000

Using the subtraction operator in the WHERE clause:

SELECT * FROM employee WHERE salary - tax = 50000;

Output:

employee_ID | employee_name | salary | allowance | tax
------------|---------------|--------|-----------|------
3           | James         | 52000  | 1000      | 2000

Multiplication Operator

The multiplication operator (*) multiplies values of two columns.

SELECT tax * 2 FROM employee;

Output:

2000
4000
4000
2000

Using the multiplication operator in the WHERE clause:

SELECT * FROM employee WHERE tax * 2 = 4000;

Output:

employee_ID | employee_name | salary | allowance | tax
------------|---------------|--------|-----------|------
2           | John          | 55000  | 1000      | 2000
3           | James         | 52000  | 1000      | 2000

Division Operator

The division operator (/) divides values of one column by another.

SELECT allowance / salary * 100 FROM employee;

Output:

4.1667
5.4545
5.7692
4.1667

Using the division operator in the WHERE clause:

SELECT * FROM employee WHERE allowance / salary * 100 >= 5;

Output:

employee_ID | employee_name | salary | allowance | tax
------------|---------------|--------|-----------|-----
2           | John          | 55000  | 1000      | 2000
3           | James         | 52000  | 1000      | 2000

Modulus Operator

The modulus operator (%) gives the remainder when one column value is divided by another.

SELECT hours % 2 FROM employee;

Output:

0
1
1
1

Using the modulus operator in the WHERE clause:

SELECT * FROM employee WHERE hours % 2 = 0;

Output:

employee_ID | employee_name | salary | hours | allowance | tax
------------|---------------|--------|-------|-----------|-----
1           | alex          | 24000  | 10    | 1000      | 1000

In this reading, you've learned how to effectively use arithmetic operators in SQL to perform calculations and filter data based on specific

Practical Use of SQL Arithmetic Operators

Overview

In this section, you will learn how to apply SQL arithmetic operators in practical scenarios using a corporate employee table with data such as ID, name, and salary. We'll explore adding bonuses, deducting amounts, doubling salaries, calculating monthly salaries, and determining even or odd employee IDs.

Adding a Bonus

To add a $500 bonus to each employee's salary:

SELECT salary + 500 FROM employee;

Output:

salary + 500

(Each employee's salary increased by 500)

Deducting an Amount

To deduct $500 from each employee's salary:

SELECT salary - 500 FROM employee;

Output:

salary - 500

(Each employee's salary decreased by 500)

Doubling the Salary

To double each employee's current annual salary:

SELECT salary * 2 FROM employee;

Output:

salary * 2

(Each employee's salary multiplied by 2)

Calculating Monthly Salary

To calculate the monthly salary of each employee by dividing the annual salary by 12:

SELECT salary / 12 FROM employee;

Output:

salary / 12

(Each employee's salary divided by 12)

Determining Even or Odd IDs

To check if each employee ID is an even or odd number using the modulus operator:

SELECT ID % 2 FROM employee;

Output:

ID % 2

(0 denotes an even ID, 1 denotes an odd ID)

In this section, you've learned how to use SQL arithmetic operators to perform various operations on employee data. Keep practicing to solidify your understanding and skills.

Using SQL Comparison Operators

Overview

In this section, you will learn about SQL comparison operators and how to use them in practical scenarios. Comparison operators are used to compare two values or expressions, resulting in either true or false. These operators can filter, include, or exclude data.

Types of Comparison Operators

SQL uses the following common mathematical comparison operators:

• Equal to: =

• Less than: <

• Greater than: >

• Less than or equal to: <=

• Greater than or equal to: >=

• Not equal to: <> or !=

Practical Examples

Equal To Operator

To identify all employees receiving a salary equal to $18,000 per year:

SELECT * FROM employee WHERE salary = 18000;

Output:

ID | Name | Salary
-------------------
1  | Carl | 18000
2  | John | 18000

Less Than Operator

To find employees receiving a salary less than $24,000 per year:

SELECT * FROM employee WHERE salary < 24000;

Output:

ID | Name | Salary
-------------------
1  | Carl | 18000
2  | John | 18000

Less Than or Equal To Operator

To determine which employees receive a salary less than or equal to $24,000 per year:

SELECT * FROM employee WHERE salary <= 24000;

Output:

ID | Name | Salary
-------------------
1  | Carl | 18000
2  | John | 18000
3  | Alice | 24000
4  | Bob | 24000

Greater Than or Equal To Operator

To find employees with a salary greater than or equal to $24,000 per year:

SELECT * FROM employee WHERE salary >= 24000;

Output

ID | Name | Salary
-------------------
3  | Alice | 24000
4  | Bob | 24000
5  | Eve | 30000

Not Equal To Operator

To find employees with a salary not equal to $24,000 per year:

SELECT * FROM employee WHERE salary <> 24000;

Output:

ID | Name | Salary
-------------------
1  | Carl | 18000
2  | John | 18000
5  | Eve | 30000

In this section, you've learned how to use SQL comparison operators to filter and retrieve data based on specific conditions. Keep practicing to enhance your SQL skills and effectively manage your database.

Using SQL ORDER BY Clause

Overview

In this section, you will learn about the SQL ORDER BY clause and its usage for sorting data in a table. The ORDER BY clause allows you to sort data either in ascending (ASC) or descending (DESC) order based on one or more columns.

Purpose of ORDER BY Clause

The ORDER BY clause is used to reorder the data retrieved from a SELECT statement. It sorts the result set in either ascending or descending order based on one or more columns.

Syntax of ORDER BY Clause

The basic syntax of the ORDER BY clause is as follows:

SELECT column1, column2, ...
FROM table_name
ORDER BY column_name [ASC | DESC];

• column1, column2, ...: Columns selected from the table.

• table_name: Name of the table from which data is retrieved.

• ORDER BY column_name: Specifies the column(s) by which the data should be sorted.

• [ASC | DESC]: Optional. ASC sorts data in ascending order (default), DESC sorts data in descending order.

Examples

Ordering by a Single Column

To order student data by nationality in ascending order:

SELECT ID, first_name, last_name, nationality
FROM student_table
ORDER BY nationality ASC;

Output:

| ID | first_name | last_name | nationality |
|----|------------|-----------|-------------|
| 3  | Alice      | Smith     | Canada      |
| 1  | John       | Doe       | USA         |
| 2  | Bob        | Johnson   | USA         |
| 4  | Eve        | Williams  | UK          |

To order in descending order:

SELECT ID, first_name, last_name, nationality
FROM student_table
ORDER BY nationality DESC;

Output:

| ID | first_name | last_name | nationality |
|----|------------|-----------|-------------|
| 4  | Eve        | Williams  | UK          |
| 1  | John       | Doe       | USA         |
| 2  | Bob        | Johnson   | USA         |
| 3  | Alice      | Smith     | Canada      |

Ordering by Multiple Columns

To order students by nationality (ascending) and date of birth (descending):

SELECT ID, first_name, last_name, date_of_birth, nationality
FROM student_table
ORDER BY nationality ASC, date_of_birth DESC;

Output:

| ID | first_name | last_name | date_of_birth | nationality |
|----|------------|-----------|---------------|-------------|
| 3  | Alice      | Smith     | 1995-03-15    | Canada      |
| 1  | John       | Doe       | 1990-08-21    | USA         |
| 2  | Bob        | Johnson   | 1988-12-05    | USA         |
| 4  | Eve        | Williams  | 1985-06-30    | UK          |

Conclusion

You have now learned how to use the SQL ORDER BY clause to sort data in a table based on specified columns and order preferences. Practice these examples to enhance your SQL skills for data manipulation and sorting.

Using SQL WHERE Clause for Filtering Data

Overview

In this section, you will learn about the SQL WHERE clause and its usage for filtering data from tables based on specified conditions. The WHERE clause allows you to retrieve records that meet certain criteria.

Purpose of WHERE Clause

The WHERE clause is used to filter records in a SQL SELECT statement. It specifies a condition that must be met for records to be included in the result set.

Syntax of WHERE Clause

The basic syntax of the WHERE clause in a SELECT statement is as follows:

SELECT column1, column2, ...
FROM table_name
WHERE condition;

• column1, column2, ...: Columns selected from the table.

• table_name: Name of the table from which data is retrieved.

• WHERE condition: Specifies the condition that must be met. Only rows that satisfy this condition are retrieved.

Operators Used in WHERE Clause

SQL provides a variety of operators that can be used in the WHERE clause to formulate conditions:

• Comparison Operators: =, <, >, <=, >=, <> (not equal to)

• Logical Operators: AND, OR, NOT

• Special Operators: BETWEEN, LIKE, IN

Examples

Filtering by Single Condition

To retrieve details of students in the engineering faculty:

SELECT *
FROM student_table
WHERE faculty = 'engineering';

Output:

| ID | first_name | last_name | date_of_birth | faculty     |
|----|------------|-----------|---------------|-------------|
| 1  | John       | Doe       | 1990-08-21    | engineering |
| 2  | Jane       | Smith     | 1991-05-15    | engineering |
| 3  | Bob        | Johnson   | 1992-02-10    | engineering |

Using BETWEEN Operator

To find students eligible for financial aid based on date of birth:

SELECT *
FROM student_table
WHERE date_of_birth BETWEEN '2010-01-01' AND '2010-05-30';

Output:

| ID | first_name | last_name | date_of_birth | faculty |
|----|------------|-----------|---------------|---------|
| 4  | Alice      | Williams  | 2010-03-25    | science |
| 5  | Eve        | Brown     | 2010-04-18    | arts    |
| 6  | Michael    | Davis     | 2010-05-12    | science |
| 7  | Sarah      | Miller    | 2010-01-20    | science |

Using LIKE Operator

To retrieve students in the science faculty using a pattern:

SELECT *
FROM student_table
WHERE faculty LIKE 'Sc%';

Output:

| ID | first_name | last_name | date_of_birth | faculty |
|----|------------|-----------|---------------|---------|
| 4  | Alice      | Williams  | 2010-03-25    | science |
| 6  | Michael    | Davis     | 2010-05-12    | science |
| 8  | Olivia     | Garcia    | 2009-11-08    | sports  |
| 9  | Sophia     | Rodriguez | 2008-07-31    | science |

Using IN Operator

To find students from specific countries:

SELECT *
FROM student_table
WHERE country IN ('USA', 'UK');

Output:

| ID | first_name | last_name | date_of_birth | faculty |
|----|------------|-----------|---------------|---------|
| 1  | John       | Doe       | 1990-08-21    | engineering |
| 2  | Jane       | Smith     | 1991-05-15    | engineering |
| 5  | Eve        | Brown     | 2010-04-18    | arts    |
| 7  | Sarah      | Miller    | 2010-01-20    | science |

Conclusion

You have now learned how to use the SQL WHERE clause to filter data based on specified conditions using various operators. Practice these examples to become proficient in filtering data from SQL databases.

Understanding the SQL WHERE Clause

Overview

The WHERE clause in SQL is essential for filtering records based on specified conditions in SELECT, UPDATE, and DELETE statements. This section explains its usage, various operators, and examples.

Purpose of WHERE Clause

The WHERE clause filters records based on conditions specified in the SQL statement, allowing retrieval or manipulation of specific data that meets criteria.

Comparison Operators

SQL provides several comparison operators for formulating conditions in the WHERE clause:

• \=: Equal to

• != or <>: Not equal to

• \>: Greater than

• <: Less than

• \>=: Greater than or equal to

• <=: Less than or equal to

• !<: Not less than

• !>: Not greater than

Logical Operators

Logical operators combine multiple conditions within the WHERE clause:

• AND: Requires all conditions to be true

• OR: Requires at least one condition to be true

• NOT: Negates the condition's result

Examples

Single Condition Example

To fetch invoices with a total value greater than $2:

SELECT *
FROM invoices
WHERE Total > 2;

Multiple Conditions with AND Operator

To retrieve invoices with a total over $2 and billing country as USA:

SELECT *
FROM invoices
WHERE Total > 2 AND BillingCountry = 'USA';

Multiple Conditions with OR Operator

To fetch invoices with billing country as USA or France:

SELECT *
FROM invoices
WHERE BillingCountry = 'USA' OR BillingCountry = 'France';

Combined AND and OR Operators

To get invoices where the total is over $2 and billing country is USA or France:

SELECT *
FROM invoices
WHERE Total > 2 AND (BillingCountry = 'USA' OR BillingCountry = 'France');

Conclusion

The SQL WHERE clause is crucial for filtering data based on specified conditions. It allows for precise retrieval and manipulation of records in SQL queries. Practice using different operators and combinations to master the filtering capabilities of SQL.

Understanding the SQL SELECT DISTINCT Statement

Overview

The SQL SELECT DISTINCT statement is used to retrieve unique (distinct) values from a specified column or combination of columns in a table. This section explains its purpose, usage, and examples.

Purpose of SELECT DISTINCT

The SELECT DISTINCT statement eliminates duplicate records from the result set. It returns only unique values, making it useful when you need to retrieve a list of unique entries from a column.

Basic Syntax

The basic syntax of the SELECT DISTINCT statement is straightforward:

SELECT DISTINCT column_name
FROM table_name;

This query retrieves unique values from the specified column_name in the table_name.

Example: Single Column

To fetch a list of unique countries represented by students:

SELECT DISTINCT country
FROM student_table;

This query ensures that each country appears only once in the result set, eliminating duplicate entries.

Example: Multiple Columns

You can use SELECT DISTINCT with multiple columns to retrieve unique combinations:

SELECT DISTINCT faculty, country
FROM student_table;

This query returns unique combinations of faculty and country from the student_table.

Example: Handling NULL Values

SELECT DISTINCT also handles NULL values as unique:

SELECT DISTINCT faculty, country
FROM student_table
WHERE faculty IS NULL;

Here, NULL values for faculty are treated as distinct, so each NULL entry is included in the result set.

Conclusion

The SELECT DISTINCT statement is essential for retrieving unique values from one or more columns in SQL queries. It helps in filtering out duplicates and presenting a concise, unique list of values based on specified criteria.

Understanding Database Schemas

Overview

Before developing a database or software application, it's essential to plan how your data will be organized. This plan is known as a schema. In this guide, you'll learn about the concept of a database schema, its various meanings across different database systems, and the advantages it offers.

What is a Database Schema?

A schema is a blueprint of how data is structured and organized within a database. It defines the tables, fields, relationships, and other components that comprise the database. Essentially, a schema is an abstract design of the database.

Schema in Different Database Systems

• MySQL: In MySQL, a schema is synonymous with a database. It represents how data is organized and related within the database.

• SQL Server: Here, a schema is a collection of components such as tables, fields, data types, and keys.

• PostgreSQL: In PostgreSQL, a schema acts as a namespace containing database objects like views, indexes, and functions.

• Oracle: Each user is assigned a single schema, and Oracle names each schema after its respective user.

Despite these differences, the core concepts of organizing data in tables and defining relationships between tables are consistent across all systems.

Components of a Database Schema

A SQL Server schema includes the following objects:

• Tables: Store data in rows and columns.

• Columns: Define the type of data stored in each table.

• Relationships: Show how tables are related to one another.

• Data Types: Specify the kind of data stored in each column.

• Keys: Ensure data integrity and uniqueness.

For example, a music database might have separate tables for artists, albums, and genres, with relationships defined by keys.

Advantages of a Database Schema

• Logical Grouping: Schemas provide a logical organization for database objects, making them easier to manage.

• Enhanced Security: They offer greater security by allowing permissions to be granted based on user access rights.

• Ease of Access and Manipulation: Schemas simplify the process of accessing and manipulating database objects.

• Transfer of Ownership: Ownership of schemas and their objects can be transferred between users and schemas.

Conclusion

A database schema is a vital structure representing the organization of data within a database. While the definition of a schema varies across different database systems, its primary role in organizing data and defining relationships remains the same. Understanding and utilizing schemas can greatly enhance database management, security, and accessibility.

Exploring Database Schema

Introduction

In this guide, you'll explore the concept of a database schema in more detail. Having been introduced to the basics of a database schema, this reading will delve deeper into its structure and types. Understanding database schemas is crucial for working with relational databases.

What is a Database Schema?

A database schema is the structure or blueprint of a database, detailing how data is organized. It involves designing tables with columns and rows, where each column has a defined data type, and tables are related to each other. This process, also known as data modeling, is the first step in designing a database system.

Typically, database designers create the schema, which acts as a skeleton, not storing any actual data. Developers then use this schema to implement the application and understand how data should be stored.

Types of Database Schema

Database schemas can be categorized into three main types:

1. Conceptual or Logical Schema

2. Internal or Physical Schema

3. External or View Schema

Conceptual or Logical Schema

The conceptual or logical schema outlines the entire database structure for all users, detailing entities, attributes, and their relationships. An Entity Relationship Diagram (ER-D) is often used to represent this schema, focusing on the conceptual level without detailing physical storage.

Example: A logical schema might depict the relationships between employee and department entities, along with their attributes.

Internal or Physical Schema

The internal or physical schema describes how data is physically stored on disk, representing the database at a low level. It details how tables, columns, and records are stored.

Example: An internal schema could show how the employee table's data is physically stored on disk.

External or View Schema

The external or view schema describes the database from a user's perspective, showing only the relevant parts of the database for specific users. Different users might have different view schemas, hiding non-relevant details.

Example: Three users might have different views of the employee table, each seeing only the data relevant to their needs.

Three-Schema Architecture

These three schema types form the three-schema architecture, which can be diagrammatically represented to show their interrelationships and distinct roles.

Importance of Database Schemas

Database schemas are crucial for organizing data into well-defined tables with relevant attributes. They provide the following benefits:

• Maintain clean and organized data related to an application.

• Prevent the need for reverse-engineering the data model, saving time and effort.

• Enable efficient querying for reporting, analytics, and other purposes.

A well-designed database schema ensures smooth operations for database engineers and developers, reducing costs and improving efficiency.

Conclusion

A database schema is a critical component in designing and managing relational databases. Understanding the three main types of schemas—conceptual, internal, and external—helps in organizing data effectively and efficiently. A well-structured schema facilitates better data management, security, and query performance, ultimately benefiting the entire database system.

Creating a Simple Database Schema

By the end of this guide, you'll know how to create a simple database schema using SQL. We'll build the schema for a shopping cart database consisting of three tables.

Step 1: Create the Database

First, we create a new database called shopping_cart_DB. To do this, type the following SQL command:

CREATE DATABASE shopping_cart_DB;

Run the statement, and the shopping_cart_DB database will appear in the left-hand Explorer. Now, we can create the tables inside this database.

Step 2: Create the Customer Table

Next, we'll create the customer table, which stores the following information for each customer: customer_id, name, address, email, and phone_number. Use the following SQL command:

CREATE TABLE customer (
    customer_id INT PRIMARY KEY,
    name VARCHAR(100),
    address VARCHAR(255),
    email VARCHAR(100),
    phone_number VARCHAR(10)
);

Here:

• customer_id is an integer and the primary key.

• name and email have a character limit of 100.

• address has a character limit of 255.

• phone_number has a character limit of 10.

Step 3: Create the Product Table

Next, create the product table, which stores the product_id, name, price, and description. Use the following SQL command:

CREATE TABLE product (
    product_id INT PRIMARY KEY,
    name VARCHAR(100),
    price NUMERIC(8, 2),
    description VARCHAR(255)
);

Here:

• product_id is an integer and the primary key.

• name has a character limit of 100.

• price is a numeric type with parameters 8, 2.

• description has a character limit of 255.

Step 4: Create the Cart Order Table

Finally, create the cart_order table, which holds the order_id, customer_id, product_id, quantity, order_date, and status. Use the following SQL command:

CREATE TABLE cart_order (
    order_id INT PRIMARY KEY,
    customer_id INT,
    product_id INT,
    quantity INT,
    order_date DATE,
    status VARCHAR(100),
    FOREIGN KEY (customer_id) REFERENCES customer(customer_id),
    FOREIGN KEY (product_id) REFERENCES product(product_id)
);

Here:

order_id is an integer and the primary key. customer_id, product_id, and quantity are all integers. order_date is a date type. status has a character limit of 100. customer_id is a foreign key referencing the customer_id in the customer table. product_id is a foreign key referencing the product_id in the product table.

Understanding Primary and Foreign Keys

Primary keys uniquely identify each record in a table. Foreign keys establish relationships between tables by linking to the primary key in another table.

In the cart_order table:

• customer_id links to customer_id in the customer table.

• product_id links to product_id in the product table.

• These relationships are defined using the FOREIGN KEY and REFERENCES keywords.

Conclusion

In this guide, you learned the steps to create a simple database schema using SQL. This process applies to both small and large-scale databases. By following these steps, you can efficiently design and implement a structured database for your applications.

Types of Database Schemas

When creating your databases, it's essential to distinguish between different kinds of database schemas to determine the best fit for your project. This guide will explore two primary types of database schemas: logical and physical. By the end, you'll understand the concepts of both logical and physical database schemas.

Logical Database Schema

A logical database schema defines how data is organized in terms of tables and their relationships. It shows which tables should be in a database and how their attributes are linked. This process is known as entity-relationship (ER) modeling. Here’s how it works:

• Entities and Relationships: The logical schema illustrates relationships between components of your data, specifying what the relationships between entity types are.

• Primary and Foreign Keys: Each table typically has a primary key, a unique identifier for each record. Foreign keys in one table reference primary keys in another table, establishing relationships.

Example of a Logical Schema

Consider an ordering application:

• Order Entity: Contains order details with a primary key order_id.

• Shipment Entity: Contains shipment details with a primary key shipment_id.

• Courier Entity: Contains courier details with a primary key courier_id.

The order table might include shipment_id and courier_id as foreign keys, linking to the shipment and courier tables, respectively. This creates a relationship between these entities.

Physical Database Schema

A physical database schema defines how data is stored on disk. This involves creating the actual structure of your database using SQL code. Physical schemas can vary slightly between different database systems but generally involve:

• SQL Statements: Developers use SQL to create tables and other database objects. For example, creating a physical schema for an online store database might involve writing SQL statements to create tables for customers, products, and transactions.

Example of a Physical Schema

To create a physical schema for an online store, you might write the following SQL statements:

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    name VARCHAR(100),
    email VARCHAR(100)
);

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    name VARCHAR(100),
    price DECIMAL(10, 2)
);

CREATE TABLE transactions (
    transaction_id INT PRIMARY KEY,
    customer_id INT,
    product_id INT,
    transaction_date DATE,
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id),
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Here, the physical schema defines how the customers, products, and transactions tables are stored and linked on disk.

Importance of Database Schemas

Database schemas are crucial for database creation and form the foundation of your application. Understanding both logical and physical schemas allows you to:

• Organize Data: Logical schemas help in organizing data into well-defined tables and specifying relationships using ER models.

• Store Data: Physical schemas control how data is stored on disk using SQL statements.

By distinguishing between logical and physical schemas, you can effectively design and implement a structured database that meets the needs of your application.

Understanding the Relational Model for Databases

At this stage of the course, it's essential to explore the relational model for databases to understand how it influences database design and structure. This knowledge is crucial for building relationships between tables and determining how to extract information effectively. Over the next few minutes, you'll learn the basics of the relational model, different relationships between tables, and the basics of an Entity-Relationship Diagram (ER-Diagram).

Basics of the Relational Model

The relational model organizes data into tables (also known as relations) consisting of rows and columns. Let's consider an example with two tables from a college database:

1. Student Table: Contains a list of students along with their student ID and course ID.

2. Course Table: Lists courses available for study, each with a course ID and department.

Key Question

• Which student is studying which course?

• Is each student studying one or multiple courses?

To answer these questions, it's crucial to structure and connect tables correctly.

Types of Relationships Between Tables

There are three main types of relationships between tables in a relational database: one-to-many, one-to-one, and many-to-many.

One-to-Many Relationship

In a one-to-many relationship, a record in one table is linked to multiple records in another table. For example:

• A student with a student ID of 1 is enrolled in two courses listed in the course table.

This relationship can be illustrated in a basic Entity-Relationship Diagram (ERD) using shapes and symbols:

• Entities: Student and Course (rectangles)

• Relationship: Enrolled (diamond shape)

• Notation: Crow's foot notation symbol to depict "many"

In a more complex ER-Diagram, the course ID in the student table is a foreign key (FK) referencing the primary key (PK) course ID column in the course table.

One-to-One Relationship

In a one-to-one relationship, a single record in one table is associated with a single record in another table. For example:

• Department Staff Table: Contains key information about staff in each college department.

• Department Location Table: Records key data about the location of each department on campus.

Each department head is linked to one department building. This relationship can be depicted in an ERD as follows:

• Entities: Department Staff and Department Location (rectangles)

• Relationship: Leads (diamond shape)

Many-to-Many Relationship

In a many-to-many relationship, a record in one table is associated with multiple records in another table, and vice versa. For example:

• Student Maurice Doyle: Undertakes two research projects, each supervised by a different staff member.

• Staff Members: Can supervise multiple students on their research projects.

This relationship can be depicted in an ERD as follows:

• Entities: Student and Staff (rectangles)

• Relationship: Supervised by (diamond shape)

Summary

Understanding the relational model helps in structuring databases and establishing correct relationships between tables. The key types of relationships are:

• One-to-Many: A single record in one table links to multiple records in another.

• One-to-One: A single record in one table links to a single record in another.

• Many-to-Many: Records in one table link to multiple records in another, and vice versa.

With this knowledge, you can design more efficient and effective relational databases. Good work!

Understanding the Relational Model for Databases

In this reading, you'll delve deeper into the relational database model, a foundational concept in database management. Despite its introduction many years ago, it remains the most widely used model for commercial databases.

What is the Relational Model?

The relational model revolves around three main concepts:

• Data

• Relationships

• Constraints

It defines a database as "a collection of inter-related relations (or tables)". This model is pivotal in organizing and storing data efficiently within a database. SQL (Structured Query Language) is commonly used to retrieve data from relational databases.

Fundamental Concepts of the Relational Model

Relation

A relation, also known as a table, stores data organized into rows and columns. Each row represents a tuple (or record) containing related data values. Columns, also called fields or attributes, define the nature of the data stored in each row.

Example of a Relational Model

In this example, "ID", "First Name", and "Last Name" are columns. Each row represents specific instances of data stored in these columns.

Domain

The domain specifies the set of acceptable values for a column based on its data type (numeric, text, date, etc.). For example, the "ID" column accepts only numeric values, while "First Name" accepts text values. This ensures data integrity within the database.

Record or Tuple

A record (or tuple) is a single row within a table that contains a specific set of attributes. For instance, a record in a student table would include attributes like ID, First Name, and Last Name.

Key

Each row or tuple in a table has one or more attributes known as keys, which uniquely identify that row. The primary key is crucial and ensures each record is distinct within the table.

Degree

Degree refers to the number of columns or attributes within a relation. For example, a student table with attributes like name, address, phone number, and email address has a degree of four.

Cardinality

Cardinality refers to the number of records within a particular table in a database. For example, if a student table contains 100 records, its cardinality is 100.

Constraints in the Relational Model

In the relational model, every relation must satisfy three relational integrity constraints:

• Key Constraints: Ensure key attributes are unique and not NULL.

• Domain Constraints: Enforce valid data types for each attribute.

• Referential Integrity Constraints: Ensure consistency between linked tables using primary and foreign keys.

Types of Relationships

In the relational model, relationships between tables can be categorized into three types:

One-to-One

A one-to-one relationship (1:1) means each record in Table A relates to exactly one record in Table B, and vice versa. For example, each country has one capital city, and each capital city belongs to one country.

One-to-Many

A one-to-many relationship (1:N) means a record in Table A can relate to zero, one, or many records in Table B, but each record in Table B relates to only one record in Table A. For instance, each customer can place many orders, but each order belongs to only one customer.

Many-to-Many

A many-to-many relationship (N:M) means many records in Table A can relate to many records in Table B, and vice versa. For example, many customers can purchase many products, and each product can be purchased by many customers.

However, many-to-many relationships are typically resolved by introducing a junction (or middle) table, which breaks them down into two one-to-many relationships.

Conclusion

The relational database model offers numerous advantages, including effective data organization, meaningful information system design, and efficient data retrieval capabilities. Understanding these concepts is essential for designing robust and scalable database systems.

Understanding Primary Keys in Database Tables

In this video, you'll gain a clear understanding of primary keys within database tables, their purpose, and the distinction between simple and composite primary keys.

Purpose of Primary Key

A primary key in a database table serves the crucial role of uniquely identifying each record within the table. It prevents duplicate records and ensures data integrity. Let's explore how primary keys are selected and their types.

Example Scenario

Consider a student table with attributes: ID, name, date of birth, email, and grade. The challenge is to identify a specific student, Mary, whose grade needs to be entered. However, using attributes like name or date of birth isn't feasible due to potential duplicates.

Selecting a Primary Key

To solve this, a candidate key is identified - an attribute that uniquely identifies each row and cannot have null values. In the student table, both the student ID and student email can serve as candidate keys since they uniquely identify each student.

Simple Primary Key

Choosing the student ID as the primary key makes it the unique identifier for each student's record. The student email, in this case, becomes an alternate key.

Composite Primary Key

In situations where no single attribute can uniquely identify each row, a composite primary key is used. For instance, in a delivery department's table for an online store, combining customer ID and project code creates a unique identifier for each delivery record.

Example of Composite Primary Key

Here, the combination of Customer ID and Project Code uniquely identifies each delivery record.

Conclusion

Understanding primary keys is essential for ensuring data uniqueness and integrity within database tables. Whether using a simple or composite primary key depends on the uniqueness requirements of the data. By mastering these concepts, you'll be equipped to make informed decisions on how to effectively structure database tables to meet specific data management needs.

Understanding Foreign Keys in Relational Databases

In this module, we delve into the concept of foreign keys within relational databases, their purpose, and how they establish relationships between tables.

Purpose of Foreign Key

A foreign key is a fundamental concept in database management that connects two tables through a column or a set of columns. It ensures referential integrity by linking the child table to a parent table. Let's explore how foreign keys work and their application in database systems.

Explaining Foreign Key

A foreign key is a column in one table that points to the primary key in another table. It establishes a relationship between these tables, enabling queries that combine data from both tables based on this relationship. The foreign key column in the child table contains values that correspond to the primary key values in the parent table.

Example Scenario: Customer and Order Tables

Consider an online store database with two tables: Customer and Order. The Customer table contains customer information, while the Order table records each customer's orders, including order date and status. The challenge is to connect these tables so that each order is associated with the correct customer.

Connecting Tables with Foreign Key

To achieve this, we add a customer ID column as a foreign key in the Order table. This column references the customer ID column in the Customer table. By establishing this relationship, we ensure that each order in the Order table is linked to a specific customer in the Customer table.

Entity Relationship Diagram (ERD)

An Entity Relationship Diagram helps visualize this relationship. In the diagram, the Order table includes the customer ID attribute as a foreign key, linking it to the Customer table. This relationship is depicted as a one-to-many relationship, where each customer may have multiple orders, but each order belongs to only one customer.

Handling Multiple Foreign Keys

In more complex scenarios, a table can have multiple foreign keys, each connecting it to different parent tables. For instance, adding a Product table introduces a second foreign key in the Order table, linking it to the Product table via product ID. This establishes a one-to-one relationship between orders and products.

Example of Multiple Foreign Keys

Here, each order is linked to a specific customer and product, ensuring accurate tracking and management of orders.

Conclusion

Understanding foreign keys is essential for maintaining data integrity and establishing relationships between tables in a relational database. By grasping these concepts, you can effectively design and manage databases to meet specific business needs, ensuring seamless data retrieval and manipulation operations.

Understanding Entities and Attributes in Relational Databases

In this module, we explore the fundamental concepts of entities and attributes within relational databases, their types, and their significance in database design.

Entities in Relational Databases

An entity in a relational database can be defined as an object or thing that we want to collect and store data about. Essentially, an entity represents a single occurrence of something that is important to the business or project. For example, in an e-commerce store database, entities could include customers, products, orders, and deliveries.

Example: Delivery Records Table

Let's consider a table named deliveries in an e-commerce store database. Here, each row represents a delivery record, and the table includes attributes such as ID, name, and delivery status. These attributes store relevant data about each delivery entity, such as the ID of the delivery, the recipient's name, and the current status of the delivery.

Types of Attributes

Attributes in a relational database define the characteristics or properties of entities. They can vary in type and purpose, influencing how data is stored and managed within tables.

Types of Attributes Explained

1. Simple Attribute: Represents a basic, indivisible value. For instance, in a student table, the grade attribute holds simple values that cannot be further divided.

2. Composite Attribute: Can be split into multiple smaller sub-attributes. For example, the name attribute in a student table can be divided into first name and last name sub-attributes.

3. Single-Valued Attribute: Holds only one value for each occurrence of the entity. An example is the date of birth attribute in a student table, where each student has a single date of birth.

4. Multi-Valued Attribute: Can contain multiple values for the same attribute. However, this practice is generally avoided in relational databases due to normalization principles.

5. Derived Attribute: The value is derived from another attribute or set of attributes. For instance, the age of a student can be derived from their date of birth.

6. Key Attribute: A unique identifier for each entity occurrence. In a student table, the student ID serves as a key attribute that uniquely identifies each student record.

Practical Considerations in Database Design

When designing a database, it's crucial to focus only on entities and attributes that are essential to the project's goals and user tasks. Unnecessary entities or attributes should be avoided to maintain simplicity and efficiency in database operations.

Conclusion

Understanding entities and attributes is foundational to designing effective relational databases. By identifying and defining these elements accurately, database designers ensure data integrity and efficient data management, supporting the overall objectives of the application or system.

By mastering these concepts, you are well-equipped to differentiate between attribute types and effectively design databases that meet specific business requirements.

Understanding Entity Relationship Diagrams (ERD) in Relational Databases

This module explores the Entity Relationship Diagram (ERD) and its role in designing and documenting relational databases.

Importance of ERD in Relational Database Design

The relational database model organizes data into tables to maintain consistency and accuracy. Designing tables and defining their relationships is crucial for ensuring effective data management and retrieval. The Entity Relationship Diagram (ERD) serves as a visual representation of these relationships, helping to conceptualize and implement the database structure.

Purpose of ERD

ERDs provide a holistic view of the database structure, documenting data requirements and operations essential for project development. They serve as blueprints guiding database developers through the actual implementation process using database management systems like Oracle and MySQL.

Components of an ERD

Entity Representation

In an ERD, entities are represented by boxes with two compartments:

• The top compartment displays the entity name.

• The bottom compartment lists the entity's attributes.

Example: College Enrollment System

Consider a college enrollment system with entities:

• Student

• Course

• Department

Each entity is represented by a separate box in the ERD, capturing essential attributes related to students, courses, and departments.

Relationship Representation

ERDs use different line styles to depict relationships between entities, based on their cardinality:

• One-to-One (1:1): Represented by a straight line connecting two entities. Example: Each passenger has only one ticket.

• One-to-Many (1:N): Represented by a straight line with a crow’s foot notation indicating "many" on one side. Example: One parent can have many children.

• Many-to-Many (M:N): Represented by a straight line with crow’s foot notations on both sides. Example: Many players participate in many games.

Example: Relationships in College Enrollment System

In the college enrollment system:

• Many students can enroll in one course (one-to-many).

• One department can offer many courses (one-to-many).

Attributes Representation

Attributes define the properties or characteristics of entities and are listed within each entity box in the ERD. Each attribute must have a defined data type.

Example: Attributes in College Enrollment System

Attributes for each entity:

• Department: department number, department name, head of department.

• Course: course ID, course name, course credits.

• Student: student ID, name, date of birth.

Foreign Keys in ERD

Foreign keys establish relationships between tables. In the ERD, a foreign key is indicated by linking an attribute from one entity to the primary key of another entity, ensuring data integrity and connectivity.

Example: Foreign Key Usage

In the college enrollment system:

• The course table includes a department number as a foreign key to link courses with departments.

• The student table includes a course ID as a foreign key to link students with courses.

Final ERD Illustration

The ERD for the college enrollment system consists of three separate tables (entities) interconnected by foreign keys, reflecting the relationships and attributes defined in the database design.

Example: Final ERD

Conclusion

By mastering ERDs, database designers can effectively model and document relational databases, ensuring data consistency, integrity, and usability. Understanding entities, attributes, and their relationships is essential for designing robust database systems tailored to specific project needs.

Understanding Database Normalization and Anomalies

In database management, tables often encounter issues such as data duplication, update complexities, and querying inefficiencies. These challenges can be effectively mitigated through the process of database normalization. By the end of this overview, you'll have a clear understanding of what database normalization entails and how it addresses insert, update, and deletion anomalies.

What is Database Normalization?

Database normalization is a systematic approach to organizing data in tables to reduce redundancy and dependency, thus improving data integrity and query performance. It involves structuring tables so that each table serves a single purpose and holds minimal redundant data.

Challenges Addressed by Database Normalization

Let's explore the issues that normalization resolves through an example of a non-normalized table, such as the College Enrollment Table.

Example: College Enrollment Table

The College Enrollment Table aims to capture student, course, and department information in a single structure, leading to several anomalies:

Insert Anomaly

Insert anomalies occur when inserting new data requires additional unrelated data to be entered first. For instance, in the College Enrollment Table, new courses can't be added until new students are enrolled, as each student must have a unique ID. This dependency on unrelated data illustrates an insert anomaly.

Update Anomaly

Update anomalies arise when updating data in one place necessitates updates in multiple locations to maintain consistency. In the College Enrollment Table, updating department information (e.g., replacing a department head) requires modifying multiple records across all students enrolled in that department. This redundancy complicates data maintenance and increases the risk of inconsistencies.

Deletion Anomaly

Deletion anomalies occur when removing data unintentionally causes loss of unrelated data. For example, deleting a student's record in the College Enrollment Table might inadvertently remove the department information if it's stored within the same table and there's no cascading delete mechanism. This dependence on unrelated data highlights a deletion anomaly.

Solving Anomalies with Database Normalization

Normalization addresses these anomalies by restructuring tables to ensure each table serves a specific purpose and holds only related data.

Example: Normalizing the College Enrollment Table

To normalize the College Enrollment Table:

• Student Table: Contains student-specific information (e.g., student ID, name, date of birth).

• Course Table: Stores course details independently (e.g., course ID, course name, credits).

• Department Table: Manages department information (e.g., department ID, department name, head of department).

By separating data into these distinct tables, each table maintains its focus and reduces redundancy. This separation simplifies data management, improves data consistency, and facilitates efficient querying.

Conclusion

Database normalization is crucial for optimizing database design and mitigating anomalies that arise from redundant and inconsistent data. By understanding normalization principles, database designers can create robust and efficient databases that support reliable data management and retrieval operations.

By now, you should have a solid grasp of what database normalization entails and how it addresses common data anomalies in database systems. Well done!

Achieving First Normal Form (1NF) in Database Design

In the realm of database engineering, encountering tables filled with duplicate data and multiple values is a common challenge. However, with proper normalization techniques, particularly focusing on achieving First Normal Form (1NF), these challenges can be effectively addressed. By the end of this explanation, you'll grasp the concept of 1NF, understand its atomicity rule, learn how to enforce it, and explore effective strategies to eliminate repeating group problems in datasets.

Understanding First Normal Form (1NF)

First Normal Form (1NF) is the foundational step in the normalization process, crucial for organizing data efficiently within database tables. The primary objective of 1NF is to ensure data atomicity and eliminate unnecessary repeating groups of data.

Enforcing the Atomicity Rule

The atomicity rule in 1NF mandates that each column in a table must contain atomic (indivisible) values. This means every field should have only a single value, thereby avoiding the storage of multiple values or sets within a single field.

Example: Addressing Data Atomicity

Consider an unnormalized table, "Course Table," from a college database:

• Course Table:

  • Course ID (Primary Key)

  • Course Name

  • Contact Number (includes cell phone and landline numbers for each tutor)

Identifying the Problem

The Contact Number column violates 1NF because it contains multiple values (cell phone and landline) within a single field. This violates data atomicity by storing more than one piece of information in one column.

Solving the Atomicity Issue

To conform to 1NF:

1. Splitting Entities: Recognize the repeating group (tutor's name and contact numbers) and separate them into distinct entities: Course and Tutor.

2. Creating Separate Tables:

   • Course Table: Holds course-specific information (e.g., course ID, course name).

   • Tutor Table: Stores tutor details, including tutor ID, name, and contact information (split into separate columns for cell phone and landline numbers).

3. Establishing Relationships: Link the Course and Tutor tables using a foreign key (e.g., Tutor ID in Course Table).

Benefits of Achieving 1NF

By restructuring the database to adhere to 1NF principles:

• Data Integrity: Ensures each column contains atomic values, reducing data redundancy and inconsistency.

• Simplified Maintenance: Updates and deletions become straightforward and consistent across related tables.

• Enhanced Query Performance: Facilitates efficient querying and data retrieval operations.

Conclusion

First Normal Form (1NF) is foundational in database design, aimed at structuring data to eliminate redundancy and ensure data integrity. By enforcing data atomicity and separating entities appropriately, database engineers can create robust and efficient database schemas.

Now, you should have a clear understanding of First Normal Form (1NF), its significance in database normalization, and how to implement it effectively. Well done!

Achieving Second Normal Form (2NF) in Database Design

As a database engineer, you frequently encounter tables cluttered with duplicate data and multiple values, making data management challenging. However, normalization, specifically achieving Second Normal Form (2NF), can effectively address these issues. By the end of this explanation, you'll understand how to design databases in 2NF, grasp the concepts of functional dependency and partial dependency, and learn to identify and resolve these issues in database schemas.

Understanding Second Normal Form (2NF)

Second Normal Form (2NF) is a critical step in database normalization, aimed at ensuring data integrity and eliminating redundancy. To achieve 2NF, all non-key attributes in a table must depend on the entire primary key, avoiding partial dependency where non-key attributes depend on only part of the primary key.

Functional Dependency Recap

Functional dependency in a table implies that the value of one attribute uniquely determines the value of another attribute. For example, in a table "Student" with columns: Student ID, Name, and Date of Birth, the Student ID (primary key) uniquely determines the Name and Date of Birth for each student. This adherence ensures that each column contains only atomic (indivisible) values, crucial for database normalization.

Partial Dependency Explanation

Partial dependency occurs when a table has a composite primary key (formed by multiple columns), and non-key attributes depend on only part of the primary key. For instance, in a "Vaccination Status" table with Patient ID, Vaccine ID, Vaccine Name, and Status columns, if Vaccine Name depends only on Vaccine ID (part of the composite key) and not on both Patient ID and Vaccine ID together, it violates 2NF.

Example: Addressing Partial Dependency

Consider a table showing vaccination status where:

• Composite Primary Key: (Patient ID, Vaccine ID)

• Non-Key Attributes: Vaccine Name and Status

To ensure 2NF compliance:

1. Identify Entities: Patient, Vaccine, and Vaccination Status.

2. Normalize the Table: Split the table into separate entities:

   • Patient Table: Contains Patient ID and Patient Name.

   • Vaccine Table: Holds Vaccine ID and Vaccine Name.

   • Vaccination Status Table: Includes Patient ID, Vaccine ID, and Status.

3. Dependency on Primary Key: Ensure that all non-key attributes (Vaccine Name, Patient Name, and Status) in their respective tables depend solely on the primary key attributes (Patient ID and Vaccine ID).

Benefits of Achieving 2NF

By adhering to 2NF principles:

• Data Integrity: Ensures each piece of data is logically stored and avoids anomalies such as data redundancy.

• Simplified Updates: Changes to data are localized and do not propagate unnecessary updates across multiple tables.

• Efficient Querying: Enhances database performance by structuring data for optimized retrieval and manipulation.

Conclusion

Second Normal Form (2NF) plays a crucial role in database design by minimizing data redundancy and maintaining data integrity. By understanding functional and partial dependencies and applying normalization techniques, database engineers can build robust and efficient database schemas.

Now, you should have a comprehensive understanding of Second Normal Form (2NF), its significance in database normalization, and how to transform tables to adhere to its principles effectively. Well done!