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Database Management System

SELECT department_id, COUNT(*) AS employee_count, AVG(salary) AS average_salary
FROM employees
GROUP BY department_id;

In this example, the query groups the employees by their `department_id` column. The aggregate functions `COUNT(*)` and `AVG(salary)` calculate the total number of employees and the average salary within each department, respectively. The result set includes one row per department, displaying the department ID, employee count, and average salary for each department.

The GROUP BY clause is particularly useful for generating summary information and performing calculations on groups of data. It allows you to analyze data at a higher level of aggregation, providing insights into patterns and trends within the data.

SELECT column1, column2
FROM table
WHERE column1 IS NOT NULL;

This query selects rows where `column1` is not NULL, effectively excluding rows with missing data in `column1`.

2.COALESCE: The COALESCE function allows you to substitute NULL values with a specified value. You can use it to replace NULLs with meaningful default values. For example:

SELECT column1, COALESCE(column2, 'N/A') AS column2_fixed
FROM table;

In this case, if `column2` has NULL values, they will be replaced with the string ‘N/A’ in the result set.

3.IFNULL / NVL: Depending on the specific database system you’re using, you might have access to functions like IFNULL or NVL. These functions serve a similar purpose to COALESCE and allow you to replace NULL values with a specified alternative value.

4.Handling Aggregations: When performing aggregations on data with missing values, consider using aggregate functions that automatically ignore NULL values, such as COUNT, SUM, AVG, MIN, and MAX. These functions exclude NULLs when calculating results.

5.LEFT JOIN / OUTER JOIN: When joining tables, using LEFT JOIN or OUTER JOIN allows you to include all rows from one table, even if there are no matching values in the other table. This can help retain rows with missing data.

SELECT t1.column1, t2.column2
FROM table1 t1
LEFT JOIN table2 t2 ON t1.id = t2.id;

In this example, the left join retains all rows from `table1`, even if there are no matching rows in `table2`.

6.NULLIF: The NULLIF function compares two expressions and returns NULL if they are equal. It can be used to explicitly set a value to NULL if certain conditions are met. For example:

SELECT column1, NULLIF(column2, 0) AS column2_fixed
FROM table;

In this case, if `column2` equals 0, the result will be NULL.

These techniques provide ways to handle missing data in a query result, allowing you to address NULL values or handle the absence of data in a manner that aligns with your specific requirements and data analysis needs.

SELECT DISTINCT column1, column2, ...
FROM table;

Unique Values: When you use the DISTINCT keyword, the result set will contain only unique values for the specified columns. It eliminates duplicate rows, ensuring that each unique combination of values is represented once.
Single Column or Multiple Columns: You can apply the DISTINCT keyword to a single column or to a combination of columns. When using DISTINCT with multiple columns, it considers the uniqueness of the combination of values across all selected columns.
Order of Evaluation: The DISTINCT keyword evaluates the uniqueness of values across the specified columns based on their order of appearance in the SELECT statement. The first occurrence of a combination of values is retained, and subsequent duplicates are removed.
Filtering Duplicate Rows: The DISTINCT keyword can be useful when you want to filter out duplicate rows from a result set. It helps to identify unique values and eliminate redundant information.
Aggregate Functions: DISTINCT can also be used in combination with aggregate functions like COUNT, SUM, AVG, MIN, and MAX. It allows you to perform calculations on distinct values rather than the entire set of values.

Here’s an example to illustrate the use of the DISTINCT keyword:

SELECT DISTINCT city
FROM customers;

In this example, the query retrieves all unique values from the `city` column in the `customers` table. The result set will contain only distinct city names, eliminating any duplicate cities.

By using the DISTINCT keyword, you can filter out duplicate values and obtain a result set that contains only unique values. It is commonly used when you want to identify distinct values or when you need to perform calculations on unique data subsets.

SELECT
  aggregate_function(CASE
    WHEN condition1 THEN expression1
    WHEN condition2 THEN expression2
    ...
    ELSE expressionN
  END) AS result_alias
FROM table
GROUP BY grouping_column;

2.CASE Statement: Within the CASE statement, you define different conditions using the WHEN keyword, followed by the condition to evaluate. If a condition is satisfied, the corresponding expression is evaluated. You can have multiple WHEN conditions, each with its own expression. The ELSE clause specifies the default expression if none of the conditions are met.

3.Aggregate Function: You select the aggregate function that matches your calculation requirements, such as SUM, AVG, COUNT, MIN, or MAX. The aggregate function is applied to the result of the CASE statement, allowing you to perform calculations on the conditionally selected values.

4.GROUP BY: To perform conditional aggregation, you typically use the GROUP BY clause to group the data by one or more columns. This determines the subsets of data on which the conditional aggregation is performed.

Here’s an example to illustrate conditional aggregation:

SELECT
  department_id,
  SUM(CASE
    WHEN salary &gt; 5000 THEN 1
    ELSE 0
  END) AS high_salary_count,
  AVG(CASE
    WHEN salary &gt; 5000 THEN salary
    ELSE NULL
  END) AS average_high_salary
FROM employees
GROUP BY department_id;

In this example, the query calculates two aggregations based on a condition: the count of employees with a salary greater than 5000 (`high_salary_count`), and the average salary of those employees (`average_high_salary`). The CASE statement is used to evaluate the condition and select the appropriate values for each aggregation.

By using the CASE statement with aggregate functions, you can perform conditional aggregations in SQL, allowing you to calculate different results based on specific conditions. It provides flexibility in summarizing and analyzing data subsets within your queries.

SELECT column1, aggregate_function(column2)
FROM table
GROUP BY column1
HAVING condition;

4. Aggregate Functions: The HAVING clause is typically used with aggregate functions like SUM, AVG, COUNT, MIN, and MAX. You can apply conditions on the result of these functions within the HAVING clause.

5. Filtering Group Results: The HAVING clause allows you to filter groups based on conditions such as the sum being greater than a certain value, the count meeting a specific criteria, or any other condition involving aggregate functions.

6. Logical Operators: You can use logical operators like AND, OR, and NOT in combination with conditions in the HAVING clause to create more complex filtering conditions.

7. Comparison Operators: The HAVING clause supports comparison operators like =, <, >, <=, >=, <> (or !=), etc., to specify conditions based on the aggregate function results.

Here’s an example to illustrate the use of the HAVING clause:

SELECT department_id, COUNT(*) AS employee_count
FROM employees
GROUP BY department_id
HAVING COUNT(*) &gt; 5;

In this example, the query groups employees by their department ID and calculates the count of employees in each department. The HAVING clause is then used to filter out departments with less than 6 employees, retaining only the groups that meet the condition.

The HAVING clause is useful when you need to apply conditions to groups rather than individual rows. It allows you to filter and analyze data based on aggregate function results, providing a way to extract specific subsets of grouped data from a query result.

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The GROUP BY clause is particularly useful for generating summary information and performing calculations on groups of data. It allows you to analyze data at a higher level of aggregation, providing insights into patterns and trends within the data.

This query selects rows where column1 is not NULL, effectively excluding rows with missing data in column1.

2.COALESCE: The COALESCE function allows you to substitute NULL values with a specified value. You can use it to replace NULLs with meaningful default values. For example:

In this case, if column2 has NULL values, they will be replaced with the string ‘N/A’ in the result set.

3.IFNULL / NVL: Depending on the specific database system you’re using, you might have access to functions like IFNULL or NVL. These functions serve a similar purpose to COALESCE and allow you to replace NULL values with a specified alternative value.

4.Handling Aggregations: When performing aggregations on data with missing values, consider using aggregate functions that automatically ignore NULL values, such as COUNT, SUM, AVG, MIN, and MAX. These functions exclude NULLs when calculating results.

5.LEFT JOIN / OUTER JOIN: When joining tables, using LEFT JOIN or OUTER JOIN allows you to include all rows from one table, even if there are no matching values in the other table. This can help retain rows with missing data.

In this example, the left join retains all rows from table1, even if there are no matching rows in table2.

6.NULLIF: The NULLIF function compares two expressions and returns NULL if they are equal. It can be used to explicitly set a value to NULL if certain conditions are met. For example:

In this case, if column2 equals 0, the result will be NULL.

These techniques provide ways to handle missing data in a query result, allowing you to address NULL values or handle the absence of data in a manner that aligns with your specific requirements and data analysis needs.

Unique Values: When you use the DISTINCT keyword, the result set will contain only unique values for the specified columns. It eliminates duplicate rows, ensuring that each unique combination of values is represented once.

Single Column or Multiple Columns: You can apply the DISTINCT keyword to a single column or to a combination of columns. When using DISTINCT with multiple columns, it considers the uniqueness of the combination of values across all selected columns.

Order of Evaluation: The DISTINCT keyword evaluates the uniqueness of values across the specified columns based on their order of appearance in the SELECT statement. The first occurrence of a combination of values is retained, and subsequent duplicates are removed.

Filtering Duplicate Rows: The DISTINCT keyword can be useful when you want to filter out duplicate rows from a result set. It helps to identify unique values and eliminate redundant information.

Aggregate Functions: DISTINCT can also be used in combination with aggregate functions like COUNT, SUM, AVG, MIN, and MAX. It allows you to perform calculations on distinct values rather than the entire set of values.

Here’s an example to illustrate the use of the DISTINCT keyword:

In this example, the query retrieves all unique values from the city column in the customers table. The result set will contain only distinct city names, eliminating any duplicate cities.

By using the DISTINCT keyword, you can filter out duplicate values and obtain a result set that contains only unique values. It is commonly used when you want to identify distinct values or when you need to perform calculations on unique data subsets.

3.Aggregate Function: You select the aggregate function that matches your calculation requirements, such as SUM, AVG, COUNT, MIN, or MAX. The aggregate function is applied to the result of the CASE statement, allowing you to perform calculations on the conditionally selected values.

4.GROUP BY: To perform conditional aggregation, you typically use the GROUP BY clause to group the data by one or more columns. This determines the subsets of data on which the conditional aggregation is performed.

Here’s an example to illustrate conditional aggregation:

By using the CASE statement with aggregate functions, you can perform conditional aggregations in SQL, allowing you to calculate different results based on specific conditions. It provides flexibility in summarizing and analyzing data subsets within your queries.

4. Aggregate Functions: The HAVING clause is typically used with aggregate functions like SUM, AVG, COUNT, MIN, and MAX. You can apply conditions on the result of these functions within the HAVING clause.

5. Filtering Group Results: The HAVING clause allows you to filter groups based on conditions such as the sum being greater than a certain value, the count meeting a specific criteria, or any other condition involving aggregate functions.

6. Logical Operators: You can use logical operators like AND, OR, and NOT in combination with conditions in the HAVING clause to create more complex filtering conditions.

7. Comparison Operators: The HAVING clause supports comparison operators like =, <, >, <=, >=, <> (or !=), etc., to specify conditions based on the aggregate function results.

Here’s an example to illustrate the use of the HAVING clause:

In this example, the query groups employees by their department ID and calculates the count of employees in each department. The HAVING clause is then used to filter out departments with less than 6 employees, retaining only the groups that meet the condition.

The HAVING clause is useful when you need to apply conditions to groups rather than individual rows. It allows you to filter and analyze data based on aggregate function results, providing a way to extract specific subsets of grouped data from a query result.

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This query selects rows where `column1` is not NULL, effectively excluding rows with missing data in `column1`.

In this case, if `column2` has NULL values, they will be replaced with the string ‘N/A’ in the result set.

In this example, the left join retains all rows from `table1`, even if there are no matching rows in `table2`.

In this case, if `column2` equals 0, the result will be NULL.

In this example, the query retrieves all unique values from the `city` column in the `customers` table. The result set will contain only distinct city names, eliminating any duplicate cities.