The statement is the backbone of SQL querying. It lets you grab specific data tables, filter results, and perform calculations. Mastering SELECT opens up a world of data manipulation possibilities.

In this section, we'll cover the basics of SELECT and FROM clauses, aliasing, wildcards, and using . We'll also dive into arithmetic operations and string concatenation to level up your querying skills.

SELECT and FROM Clauses

Specifying Columns and Tables

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  • Use the
    SELECT
    clause to specify the columns to be retrieved from the database
  • Follow the
    SELECT
    keyword with a comma-separated list of column names or expressions
  • Employ the
    FROM
    clause to indicate the table or tables from which the data will be retrieved
  • After the
    FROM
    keyword, specify the name of the table or tables

Aliasing and Wildcards

  • Assign aliases to columns or tables using the
    AS
    keyword to provide more readable or meaningful names
    • Column aliases are defined immediately after the column name or expression (e.g.,
      SELECT first_name AS "First Name"
      )
    • Table aliases are specified after the table name (e.g.,
      FROM employees e
      )
  • Retrieve all columns from a table using the wildcard character
    *
    in the
    SELECT
    clause (
    SELECT * FROM employees
    )

Selecting Specific Columns

  • Choose specific columns to retrieve by listing their names after the
    SELECT
    keyword
  • Separate multiple column names with commas (e.g.,
    SELECT first_name, last_name, email FROM employees
    )
  • Perform calculations or apply functions to columns directly in the
    SELECT
    clause (e.g.,
    SELECT salary * 1.1 AS "New Salary" FROM employees
    )

DISTINCT and Operators

Eliminating Duplicate Rows

  • Use the
    DISTINCT
    keyword to remove duplicate rows from the
  • Place
    DISTINCT
    immediately after the
    SELECT
    keyword (e.g.,
    SELECT DISTINCT department_id FROM employees
    )
  • DISTINCT
    considers the combination of all selected columns when determining uniqueness

Arithmetic Operations

  • Perform arithmetic operations on numeric columns using operators such as
    +
    ,
    -
    ,
    *
    , and
    /
    • Addition:
      SELECT salary + bonus AS "Total Compensation" FROM employees
    • Subtraction:
      SELECT price - discount AS "Discounted Price" FROM products
    • Multiplication:
      SELECT quantity * unit_price AS "Total Amount" FROM order_items
    • Division:
      SELECT total_sales / num_employees AS "Sales per Employee" FROM departments

Concatenating Strings

  • Concatenate string values using the
    ||
    operator or the
    CONCAT()
    function
    • ||
      operator:
      SELECT first_name || ' ' || last_name AS "Full Name" FROM employees
    • CONCAT()
      function:
      SELECT CONCAT(first_name, ' ', last_name) AS "Full Name" FROM employees
  • Combine string literals with column values to create meaningful output (e.g.,
    SELECT 'Employee: ' || first_name || ' ' || last_name AS "Employee Name" FROM employees
    )

Key Terms to Review (18)

1NF: First Normal Form (1NF) is a fundamental property of a relational database table that ensures that the table structure is free of duplicate rows and that each column contains atomic, indivisible values. This concept lays the groundwork for organizing data efficiently, ensuring each piece of data is stored in its simplest form, which is crucial for effective database design and management.
2NF: Second Normal Form (2NF) is a level of database normalization that aims to eliminate partial dependencies of attributes on a composite primary key. It builds upon First Normal Form (1NF) by ensuring that all non-key attributes are fully functionally dependent on the entire primary key, which helps to reduce redundancy and improve data integrity. This concept is crucial in creating efficient relational schemas, querying data effectively, and performing accurate aggregations.
3NF: Third Normal Form (3NF) is a database normalization level that aims to eliminate redundant data and ensure that all non-key attributes are fully functionally dependent on the primary key. In simpler terms, it means organizing a database in such a way that no information is repeated unnecessarily and that every piece of data relates directly to the key attribute. This structure not only promotes data integrity but also improves the efficiency of data retrieval and management.
Avg(): The avg() function is a built-in aggregate function in SQL that calculates the average value of a specified numeric column across a set of rows. It is widely used to summarize data and provide insights into trends and patterns by giving a single representative value for the dataset. This function can be combined with other SQL commands to filter, group, and sort data effectively.
Count(): The count() function is an aggregate function in SQL that returns the number of rows that match a specified criterion. It's commonly used in conjunction with the SELECT statement to provide insights into the data, like how many entries meet certain conditions or are grouped by specific attributes. This function can also be utilized alongside filtering and sorting operations to refine results and gather meaningful statistics from a dataset.
Cursor: A cursor is a database object that allows you to retrieve and manipulate data row by row from a result set. This enables more complex operations than a standard SQL query, especially when you need to process each row individually. Cursors are useful for applications that require iterative processing, like updates or calculations on each record in a result set.
Date: A date is a specific point in time represented in a standard format, commonly used to indicate events or transactions within a database. Dates play a crucial role in tracking data changes, managing records over time, and performing time-based queries to extract relevant information.
Distinct: In database systems, 'distinct' refers to a keyword used in SQL queries to eliminate duplicate values from the result set. When retrieving data, applying 'distinct' ensures that each value appears only once, providing a clear view of unique entries. This is particularly useful in generating accurate reports and analyzing data by removing redundancy.
From: The 'from' clause in SQL is used to specify the tables from which to retrieve data in a query. It plays a crucial role in determining the source of data for the selection process, allowing users to target specific datasets effectively. This clause is essential for forming complex queries that may involve multiple tables through joins, and it directly influences the outcome of the data retrieval by defining the context of the query.
Inner join: An inner join is a type of join in SQL that combines rows from two or more tables based on a related column between them. This method filters the results to include only those records where there is a match in both tables, which is crucial for extracting meaningful data across multiple sources. Inner joins are essential when using SELECT statements to retrieve data, as well as when working with aggregate functions and grouping results for analysis.
Integer: An integer is a whole number that can be positive, negative, or zero, and does not include any fractional or decimal component. In the context of databases, integers are often used as data types for fields that require whole number values, such as counts, identifiers, and rankings. They play a crucial role in operations like data manipulation, retrieval, filtering, and setting constraints to maintain data integrity.
Join: A join is an operation used in databases to combine rows from two or more tables based on a related column between them. This operation is fundamental in relational database systems, allowing users to gather and analyze related data across multiple tables effectively.
Outer join: An outer join is a type of join that returns all records from one table and the matched records from the other table, filling in NULLs for unmatched records. This allows for comprehensive data retrieval, especially in cases where not all entries in one table have corresponding entries in the other. It’s particularly useful in situations where you want to see all data related to a specific category, even if there are missing links in related data sets.
Result set: A result set is the collection of rows returned by a database query after executing a SQL statement. It is generated by commands like SELECT and represents the data that meets the criteria defined in the query. Understanding result sets is crucial, as they are the primary means by which users interact with and retrieve data from a database, forming the foundation for both data analysis and reporting.
Select: The term 'select' refers to a fundamental operation in database systems that retrieves specific data from a database table based on certain criteria. This operation forms the backbone of many database queries, allowing users to access and manipulate the data they need. It connects to various features such as filtering results, sorting data, performing calculations through aggregate functions, and enabling more complex queries using subqueries.
SELECT statement fundamentals: The SELECT statement is a fundamental SQL command used to query and retrieve data from a database. It allows users to specify exactly which data they want to see, including the selection of specific columns, filtering rows with conditions, and organizing results in a meaningful way. Mastering the SELECT statement is crucial for effective data manipulation and analysis.
Sum(): The sum() function is an aggregate function used in SQL to calculate the total of a specified numeric column. This function plays a key role in data analysis by allowing users to quickly obtain the total for groups of data, making it essential for reporting and summarizing information.
Varchar: The varchar data type is used in databases to store variable-length character strings, allowing for efficient storage and retrieval of textual data. It is particularly useful because it can adapt to the size of the data being stored, minimizing wasted space. By specifying a maximum length, varchar helps ensure that entries remain within defined limits, which enhances data integrity and optimizes database performance during various operations.
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