SQL Basics for Entry-Level Developers
If you’re looking to make your way in the world of database development, one thing you’ll need to master is the SQL language. SQL stands for Structured Query Language, which is a programming language used to manage relational databases.
In this article, we will cover some of the basics of SQL that entry-level developers need to know.
1) Relational Data Model
The relational data model is the foundation of most modern databases. It’s based on the concept of a relation, which is a table of data that represents a set of entities.
Each row in the table represents a single instance of the entity, with each column representing a specific attribute. For example, a table of customers might have columns for the customer’s name, address, phone number, and email address.
Each row would represent a single customer, with their individual details stored in the appropriate columns.
2) RDBMS
Relational Database Management Systems (
RDBMS) are software programs that allow you to create, manage, and access relational databases. They provide a way to organize your data into tables, enforce data integrity, and perform various data manipulation tasks.
The most well-known
RDBMS is probably MySQL, but there are many others available, including PostgreSQL, Microsoft SQL Server, and Oracle.
3) ER Diagram Components
Entity-Relationship (ER) diagrams are a tool used to visualize the relationships between entities in a database. The three main components of an ER diagram are entities, relationships, and attributes.
Entities are the objects that are represented in the database. Examples of entities might include customers, orders, or products.
Relationships describe how the entities are connected to each other, such as when a customer places an order. Attributes are the characteristics of an entity, such as the customer’s name, which is an attribute of the customer entity.
4) Database Normalization
Database normalization is the process of organizing your database tables to minimize data redundancy and improve data integrity. The goal is to ensure that each piece of data is stored in only one place in the database, so you don’t have to update multiple tables if that data changes.
Normalization is usually done using a series of normal forms. There are five normal forms, with each one building on the one before it.
The first normal form (1NF) requires that each table has a primary key, and that each column contains only atomic (indivisible) values. The second normal form (2NF) requires that all non-key columns are dependent on the primary key.
And so on.
5) Attribute Constraints
Attribute constraints are rules that you can apply to a column in a database table to enforce data integrity. The most common attribute constraints are NOT NULL, CHECK, UNIQUE, PRIMARY KEY, and FOREIGN KEY.
NOT NULL ensures that a column cannot contain null (empty) values. CHECK allows you to specify a condition that the data in the column must meet.
UNIQUE ensures that each value in the column is unique. PRIMARY KEY is a combination of NOT NULL and UNIQUE, and it ensures that each row in the table is uniquely identifiable.
FOREIGN KEY is used to create relationships between tables, ensuring that data is consistent across multiple tables.
6) SQL Sublanguages and Main Keywords
SQL is divided into three sublanguages: Data Definition Language (DDL), Data Control Language (DCL), and Data Manipulation Language (DML). DDL is used to create and modify database structures, such as tables and indexes.
DCL is used to manage user access to the database. DML is used to manipulate data in the tables, such as inserting, updating, and deleting rows.
The main keywords in SQL include CREATE, DROP, SELECT, INSERT, UPDATE, and DELETE. CREATE is used to create new database structures, such as tables and indexes.
DROP is used to delete database structures. SELECT is used to retrieve data from the database.
INSERT is used to add new rows of data to the tables. UPDATE is used to modify existing rows of data.
DELETE is used to remove rows of data from tables.
7) Types of SQL Joins
A join is a way to combine data from two or more tables based on a related column between them. There are several types of joins.
A cross join (also known as a Cartesian product) returns all possible combinations of rows from both tables. An inner join returns only the rows where there is a match between the related columns in both tables.
A left outer join returns all the rows from the first (left) table and any matching rows from the second (right) table. A right outer join returns all the rows from the second (right) table and any matching rows from the first (left) table.
A full outer join returns all the rows from both tables, whether or not there is a match between them.
Conclusion
In conclusion, SQL is an essential language for anyone who wants to work with databases. By understanding the basics of the relational data model,
RDBMS, ER diagrams, normalization, attribute constraints, SQL sublanguages and main keywords, and types of SQL joins, entry-level developers can start building their knowledge of SQL and begin using it to build and manage databases.
3)
RDBMS
Relational Database Management Systems (
RDBMS) are software systems that allow users to create, manage, and access relational databases. The purpose of an
RDBMS is to facilitate data entry, storage, and retrieval in a structured way.
RDBMSs enable users to store data in tables consisting of rows and columns, with each table having a primary key that identifies each row uniquely. Relational databases are named after their ability to represent relationships between tables.
Tables are connected based on common fields, which allows information to be retrieved from multiple tables in a single query. The tables in a relational database are connected using primary and foreign keys.
A primary key in a table uniquely identifies each row, and a foreign key in one table links to the primary key of another table. There are several popular
RDBMSs available, including Microsoft SQL Server, Oracle Database, MySQL, and IBM DB2.
Each has its own strengths and weaknesses and is better suited to different use cases. For example, MySQL is open source and is commonly used in web applications, while SQL Server is often used in enterprise settings for larger applications.
4)
ER Diagram Components
Entity-Relationship (ER) diagrams are a standard tool for data modeling and are used to conceptualize the relationships between entities in a database. There are three main components of an ER diagram: entities, relationships, and attributes.
An entity is a collection of data that represents a real-world object, such as a person, place, thing, or event. Entity sets are the groups of related entities in a database.
For example, in a university database, the entity set “student” might include attributes such as student ID, name, major, and date of birth. Relationships describe how entities are related to each other.
There are three main types of relationships: one-to-one, one-to-many, and many-to-many. A one-to-one relationship exists when each entity in one set corresponds to exactly one entity in the other set.
In a one-to-many relationship, an entity in one set corresponds to multiple entities in the other set. In a many-to-many relationship, multiple entities in one set correspond to multiple entities in the other set.
Attributes are the properties of an entity that describe the characteristics of that entity. A set of attributes is the collection of all attributes that applies to an entity.
For example, the entity set “student” might have attributes such as student ID, name, major, and date of birth. ER diagrams are useful for visualizing relationships between entities and can help database designers create a more accurate and efficient database schema.
They can also be used as a communication tool between developers, stakeholders, and end-users to ensure that all parties understand the structure of the database.
Conclusion
Understanding the basics of
RDBMS and ER diagrams is essential for anyone working with databases.
RDBMSs enable users to manage and access relational databases with ease, while ER diagrams provide a visual representation of the relationships between entities in a database.
By utilizing these tools, database designers and developers can create efficient and effective databases that meet the needs of their users. 5)
Database Normalization
Database normalization is the process of improving data organization by removing redundancy, ensuring data integrity, and making the database more flexible in handling future changes.
The goal of normalization is to make the database more efficient, easier to understand and manage, and less prone to errors. Normalization is achieved by breaking down a large table into smaller, more focused tables, each with a specific purpose.
This process reduces data redundancy by eliminating duplicate data, which minimizes the chances of inconsistencies and inaccuracies in the database. It also ensures data integrity by eliminating abnormal relationships among attributes, and it helps create a more flexible database structure that can better accommodate future changes in data requirements.
There are three normal forms that describe the degree of normalization achieved in a database:
- First Normal Form (1NF): This requires that each table has a primary key, and that each column contains only atomic (indivisible) values. Each row must be uniquely identifiable and contain no repeating groups.
- Second Normal Form (2NF): This requires that all non-key columns are dependent on the primary key. Any column that is not uniquely identified by the primary key is removed from the table and placed in its own table, with the appropriate primary key referenced as a foreign key.
- Third Normal Form (3NF): This requires that all non-key columns are independent of each other, and that no non-key column depends on another non-key column. This ensures that there are no transitive dependencies, where the value of one attribute determines the value of another attribute.
For example, if a table has three columns: customer ID, order ID, and customer address, the customer’s address should not depend on the order ID, as it will lead to data redundancy. In this case, a separate customer table with its own primary key and address column should be created, and a foreign key should be used to establish the relationship between the two tables.
6)
Attribute Constraints
Attribute constraints are used to enforce data validation rules and maintain database integrity. They ensure that data is entered correctly and consistently, and that data dependencies are maintained throughout the database.
There are several common attribute constraints in a database management system:
- NOT NULL: This constraint ensures that a column cannot contain null (empty) values. It is typically used for columns that must always contain data, such as a customer’s name or address.
- CHECK: This constraint allows you to define a condition that the data in the column must meet. For example, a check constraint might require that a customer’s age must be over 18 or that a product’s price must be greater than zero.
- UNIQUE: This constraint ensures that each value in the column is unique, preventing duplicate values from being entered. It is often used for columns that must be unique, such as a customer’s email address or a product’s SKU number.
- PRIMARY KEY: This constraint is a combination of NOT NULL and UNIQUE, and it ensures that each row in the table is uniquely identifiable. It is used to establish relationships between tables and maintain data integrity throughout the database.
- FOREIGN KEY: This constraint is used to create relationships between tables, ensuring that data is consistent across multiple tables. It references the primary key of another table and ensures that the values entered in the column correspond to values in the referenced table.
Attribute constraints are important for maintaining data integrity and consistency in a database. They help prevent data entry errors and ensure that the database remains organized and efficient.
Proper use of attribute constraints is crucial for creating a reliable and effective database system. 7)
SQL Sublanguages and Main Keywords
SQL (Structured Query Language) has three sublanguages: Data Definition Language (DDL), Data Control Language (DCL), and Data Manipulation Language (DML).
Each sublanguage serves a specific purpose and has its own set of keywords. Data Definition Language (DDL) is used for creating and modifying database structures such as tables, indexes, and constraints.
The primary keywords associated with DDL are CREATE, DROP, and ALTER. CREATE is used to create new database structures, DROP is used to delete a database object, and ALTER is used to change the structure of an existing object.
Data Control Language (DCL) is used to manage user privileges and permissions. The main DCL keywords are GRANT and REVOKE.
GRANT is used to give a user permission to perform a specific action, while REVOKE revokes such permission. Data Manipulation Language (DML) is used to manipulate data within a database.
This sublanguage includes the main keywords SELECT, INSERT, UPDATE, MERGE, DELETE, TRUNCATE, BEGIN WORK, COMMIT, and ROLLBACK. SELECT is used to retrieve data from one or more tables, INSERT is used to add new rows of data to a table, UPDATE is used to modify existing rows of data, MERGE is used to update or insert data depending on its existence, DELETE is used to remove rows of data from a table, TRUNCATE is used to remove all the data from a table in a single operation, BEGIN WORK marks the start of a transaction, COMMIT marks the end of a transaction and ROLLBACK cancels the changes made during a transaction.
SQL sublanguages and their associated keywords are critical to efficiently managing and manipulating data within a database. 8)
Types of SQL Joins
SQL Joins are used to combine data from multiple tables in a database.
Various types of SQL Joins are available for matching data across multiple tables in the cross-database environment. The purpose of SQL Joins is to allow data integration from multiple tables that share a common column.
- Cross Join: The cross join, also known as a Cartesian product, returns the combination of every row in each table with every row in every other table, effectively multiplying the data. This type of join can produce a large number of rows, and it is only appropriate when the tables involved are small.
- Inner Join: The inner join returns only the rows for which the join conditions are satisfied. It is the most commonly used type of join and is used to retrieve data that has matching values in
SELECT *
FROM Customers AS c
INNER JOIN Orders AS o
ON c.CustomerID = o.CustomerID;
both tables. The above example would return all rows where the CustomerID in the Customers table matches the CustomerID in the Orders table.
- Left Outer Join: The left outer join returns all rows from the left table, and any matching rows from the right table. If there is no match for a row in the left table, the corresponding columns in the right table will be filled with NULL values. The following code would return all customers, and any associated orders. Customers without any orders will have null values in the order fields.
SELECT *
FROM Customers AS c
LEFT OUTER JOIN Orders AS o
ON c.CustomerID = o.CustomerID;
- Right Outer Join: The right outer join returns all rows from the right table, and any matching rows from the left table. If there is no match for a row in the right table, the corresponding columns in the left table will be filled with NULL values. The following code would return all orders, and any associated customers. Orders without any customers will have null values in the customer fields.
SELECT *
FROM Customers AS c
RIGHT OUTER JOIN Orders AS o
ON c.CustomerID = o.CustomerID;
- Full Outer Join: The full outer join returns all rows from both tables, including rows that do not have a match in the other table. The following code would return all customers and orders, regardless of whether they have a match in the other table.
SELECT *
FROM Customers AS c
FULL OUTER JOIN Orders AS o
ON c.CustomerID = o.CustomerID;
Understanding the different types of joins is essential for efficiently retrieving data from multiple tables in a database. By choosing the appropriate join type for your needs, you can ensure that you retrieve only the data that is relevant to your query.