JOIN vs UNION in SQL: Understanding Key Differences in 2025

Anna Panchenko

Digital Analyst

Vadym Kramarenko

Growth Marketing Manager

June 4, 2024

In SQL, both JOIN and UNION are crucial for combining data, but they serve different purposes. This article looks into the distinctions between these two operations, examining their syntax, usage scenarios, and performance implications. By the end, you'll understand when to use JOIN and when to opt for UNION to achieve optimal results in your database queries.

In SQL, understanding when to use JOIN versus UNION can significantly impact the efficiency and clarity of your queries. JOIN combines columns from related tables based on a common key, allowing for a detailed and interconnected dataset.

On the other hand, UNION stacks the results of two or more queries into a single result set, ideal for merging similar data from different sources.

Knowing the appropriate context for each operation is essential for effective database management and optimizing query performance.

What are JOIN and UNION in SQL?

JOIN and UNION are SQL operations used to combine data from multiple tables. JOIN merges columns based on related data, while UNION combines rows from separate queries, creating a unified dataset. Let's look at these operations in depth

JOIN in SQL

A JOIN in SQL combines rows from two or more tables based on a related column. It's used to retrieve data in multiple tables, providing a way to assemble a comprehensive dataset. JOINs come in various types, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN, each serving different purposes.

Syntax of JOIN

SELECT columns_from_both_tables
FROM table1
JOIN table2
ON table1.column1 = table2.column2

SELECT columns: Specifies the columns to retrieve from the tables. Replace columns with the actual column names you want to select.
FROM table1: Indicates the first table from which to retrieve data. Replace table1 with the actual name of your first table.
JOIN table2: Specifies the second table to join with the first table.
ON table1.column = table2.column: Defines the condition for the join, where table1.column is matched with table2.column.

Example of JOIN

For example, we will join the ‘Customers’ and ‘Orders’ tables based on a common column to retrieve relevant customer and order information.

SELECT Customers.customer_id, Customers.name, Orders.spend
FROM Customers
JOIN Orders
ON Customers.customer_id = Orders.customer_id;

Provides an overview of different types of JOIN operations available in SQL.

Here:

Customers.customer_id: This specifies the customer_id column from the Customers table, which uniquely identifies each customer.
Customers.name: This specifies the name column from the Customers table, which contains the first names of the customers.
Orders.spend: This specifies the spend column from the Orders table, which contains the order amounts.
FROM Customers: Indicates the first table from which to retrieve data. Replace Customers with the actual name of your first table.
JOIN Orders: Specifies the second table to join with the first table. Replace Orders with the actual name of your second table.
ON Customers.customer_id = Orders.customer_id: This join condition ensures that only rows where the customer_id in the Customers table matches the customer column in the Orders table are included in the result set.

Using the JOIN operation to combine the Customers and Orders tables based on a common column ensures that the retrieved data accurately reflects the relationship between customers and their orders, providing a comprehensive view for effective data analysis and reporting.

UNION in SQL

The UNION ALL operation in SQL combines the result sets of two or more SELECT queries into a single result set, including only distinct values. It's useful for merging similar data from different tables or queries. There are two main types: UNION DISTINCT and UNION ALL, where UNION DISTINCT removes duplicates, and UNION ALL includes all results.

Syntax of UNION

SELECT column1, column2, ...
FROM table1
UNION DISTINCT
SELECT column1, column2, ...
FROM table2;

SELECT column1, column2, ... FROM table1: Specifies the columns to retrieve from the first table.
FROM table1: Indicates the first table from which to retrieve data. Replace table1 with the actual name of your first table
UNION DISTINCT: Combines the results of the two SELECT statements into a single result set, including only distinct values.
SELECT column1, column2, ... FROM table2: Specifies the columns to retrieve from the second table. Replace column1, column2, ... with the actual column names you want to select from table2.
SELECT column1, column2, ...: This part specifies the columns you want to retrieve.
FROM table2: Indicates the second table from which to retrieve data.

Example of UNION DISTINCT

In this example, we will use the UNION operation to combine the customer_id columns from the Customer_info and Customer_purchases tables to create a unified list of unique customer_id who visited and ordered on the site.

SELECT customer_id
FROM Customers
UNION DISTINCT
SELECT customer_id
FROM Orders;

Shows combining datasets while removing duplicates using the UNION operation.

Here:

SELECT customer_id FROM Customers: Retrieves the customer_id column from the Customers table, listing all uniquecustomer_id.
UNION DISTINCT: Combines the results of the two SELECT statements into a single result set, including only distinct values.
SELECT customer_id FROM Orders: Retrieves the custome_id column from the Orders table, listing all unique customer_id.

Using the UNION operation to combine the customer_id columns from the Customers and Orders tables ensures that the final result includes a unified list of distinct customer_id from both tables, providing a comprehensive dataset for analysis or reporting.

Exploring Different Types of JOIN

JOIN operations in SQL come in various types, each serving specific purposes for combining data from multiple tables. Understanding the different types of JOINs - such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN is important for effective data manipulation and retrieval in relational databases.

Type #1 INNER JOIN

INNER JOIN combines rows from two tables based on a common attribute, returning only the rows with a match in both tables. It's the most commonly used JOIN operation, ensuring that only related data from both tables is included in the result set.

Syntax of INNER JOIN

SELECT columns_from_both_tables
FROM table1
INNER JOIN table2
ON table1.column1 = table2.column2

SELECT columns_from_both_tables: Specifies the columns to retrieve from both tables. Replace columns_from_both_tables with the actual column names you want to select.
FROM table1: Indicates the first table from which to retrieve data.
INNER JOIN table2: Specifies the second table to join with the first table. INNER JOIN table2: Indicates that an INNER JOIN operation is performed with the second table.
ON table1.column1 = table2.column2: Defines the condition for the join, where table1.column1 is matched with table2.column2.

Example of INNER JOIN

Let's join the Employees and Departments tables based on a common attribute to retrieve relevant employee and department information.

SELECT Employees.employee_id, Employees.name, Contacts.phone_number
FROM Employees
INNER JOIN Contacts
ON Employees.employee_id = Departments.employee_id;

Highlights retrieving matching records between two tables using an INNER JOIN operation.

Here:

SELECT Employees.employee_id, Employees.name, Contacts.phone_number: Specifies the columns to retrieve from both tables.
Contacts.phone_number: This specifies the phone_number column from the Contacts table, which contains the phone numbers.
FROM Employees: Indicates the first table from which to retrieve data.
INNER JOIN Contacts: Specifies the second table to join with the first table.
ON Employees.employee_id = Contacts.employee_id: Defines the condition for the join, where Employees.employee_id is matched with Contacts.employee_id.

Using the INNER JOIN operation to combine the Employees and Contacts tables based on a common attribute ensures that the retrieved data accurately reflects the relationship between employees and their respective phone numbers, providing a comprehensive view for effective data analysis and reporting.

Type #2 FULL OUTER JOIN

FULL OUTER JOIN combines rows from both tables, returning all records when there is a match in either table. It includes rows with matching values and unmatched rows from both tables, ensuring that no data is lost due to the absence of a common attribute.

Syntax of FULL OUTER JOIN

SELECT columns
FROM table1
FULL OUTER JOIN table2
ON table1.column1 = table2.column2;

SELECT columns: Specifies the columns to retrieve from both tables.
FROM table1: Indicates the first table from which to retrieve data.
FULL OUTER JOIN table2: Specifies the second table to join with the first table.
ON table1.column1 = table2.column2: Defines the condition for the join, where table1.column1 is matched with table2.column2.

Example of FULL OUTER JOIN

Let's join the Customers and Orders tables using a FULL OUTER JOIN to retrieve customer and order information, including all rows from both tables, even if they do not have a matching entry.

SELECT Customers.customer_id, Customers.name, Orders.order_id, Orders.spend
FROM Customers
FULL OUTER JOIN Orders
ON Customers.customer_id = Orders.customer_id;

Shows how FULL OUTER JOIN retrieves all records from both tables, including unmatched rows

Here:

SELECT Customers.customer_id, Customers.name, Orders.order_id, Orders.spend: Specifies the columns to retrieve from both tables.
FROM Customers: Indicates the first table from which to retrieve data.
FULL OUTER JOIN Orders: Specifies the second table to join with the first table.
ON Customers.customer_id = Orders.customer_id: Defines the condition for the join, where Customers.customer_id is matched with Orders.customer_id.

By using the FULL OUTER JOIN operation to combine the Customers and Orders tables based on the customer_id, you ensure that the result set includes all customers and orders, even if there are no matching entries between the two tables.

Type #3 LEFT JOIN

SELECT columns_from_both_tables
FROM table1
LEFT JOIN table2
ON table1.column1 = table2.column2

LEFT JOIN returns all rows from the left table and the matching rows from the right table. If no matches exist, the result is NULL from the right table. This ensures that all data from the left table is included, regardless of matches.

Syntax of LEFT JOIN

SELECT columns_from_both_tables: Specifies the columns to retrieve from both tables.
FROM table1: Indicates the first table from which to retrieve data.
LEFT JOIN table2: Specifies the second table to join with the first table.
ON table1.column1 = table2.column2: Defines the condition for the join, where table1.column1 is matched with table2.column2.

Example of LEFT JOIN

Let's join the Customers and Orders tables using a LEFT JOIN to retrieve all customer information along with any matching order information.

SELECT Customers.customer_id, Customers.name, Orders.order_id, Orders.spend
FROM Customers
LEFT JOIN Orders
ON Customers.customer_id = Orders.customer_id;

Explains how LEFT JOIN retrieves all records from the left table and matching records from the right table.

Here:

SELECT Customers.customer_id, Customers.name, Orders.order_id, Orders.spend: Specifies the columns to retrieve from both tables.
FROM Customers: Indicates the first table from which to retrieve data.
LEFT JOIN Orders: Specifies the second table to join with the first table.
ON Customers.customer_id = Orders.customer_id: Defines the condition for the join, where Customers.customer_id is matched with Orders.customer_id.

Using the LEFT JOIN operation, you ensure that all customers are included in the result set, providing a comprehensive view of customer data even if some customers do not have associated orders. This is useful for analyses that require all records from the left table, regardless of matches in the right table.

Type #4 RIGHT JOIN

RIGHT JOIN returns all rows from the right table and the matching rows from the left table. If there are no matches, the result is NULL from the left table. This ensures that all data from the right table is included, regardless of matches.

Syntax of RIGHT JOIN

SELECT columns_from_both_tables
FROM table1
RIGHT JOIN table2
ON table1.column1 = table2.column2;

SELECT columns_from_both_tables: Specifies the columns to retrieve from both tables.
FROM table1: Indicates the first table from which to retrieve data.
RIGHT JOIN table2: Specifies the second table to join with the first table.
ON table1.column1 = table2.column2: Defines the condition for the join, where table1.column1 is matched with table2.column2.

Example of RIGHT JOIN

Let's join the Orders and Customers tables using a RIGHT JOIN to retrieve all order information and any matching customer information. This ensures that all orders are included, even if they do not have corresponding customer entries.

SELECT Orders.order_id, Orders.spend, Customers.customer_id, Customers.name
FROM Orders
RIGHT JOIN Customers
ON Orders.customer_id = Customers.customer_id;

Explains retrieving all records from the right table and matching records from the left table using RIGHT JOIN.

Here:

SELECT Orders.order_id, Orders.dpend, Customers.customer_id, Customers.name: Specifies the columns to retrieve from both tables.
FROM Orders: Indicates the first table from which to retrieve data.
RIGHT JOIN Customers: Specifies the second table to join with the first table.
ON Orders.customer_id = Customers.customer_id: Defines the condition for the join, where Orders.customer_id is matched with Customers.customer_id.

Using the RIGHT JOIN operation ensures that all customers are included in the result set, providing a comprehensive view of customer data even if some customers do not have associated orders. This is useful for analyses that require all records from the right table, regardless of matches in the left table.

Type #5 CROSS JOIN

CROSS JOIN returns the Cartesian product of two tables, combining each row from the first table with each row from the second table. This results in a dataset where the total number of rows is the product of the number of rows in both tables.

Syntax of CROSS JOIN

SELECT columns_from_both_tables
FROM table1
CROSS JOIN table2;

SELECT columns_from_both_tables: Specifies the columns to retrieve from both tables.
FROM table1: Indicates the first table from which to retrieve data.
CROSS JOIN table2: Combines each row from the first table with each row from the second table to produce the Cartesian product.

Example of CROSS JOIN

Let's join the Products and Categories tables using a CROSS JOIN to retrieve every possible combination of products and categories. This will result in a Cartesian product of the two tables.

SELECT Products.product_id, Categories.price
FROM Products
CROSS JOIN Categories;

Here:

SELECT Products.product_id, Categories.price: Specifies the columns to retrieve from both tables.
FROM Products: Indicates the first table from which to retrieve data.
CROSS JOIN Categories: Specifies the second table to join with the first table.

Using the CROSS JOIN operation, you combine each row from the Products table with each from the Categories table, generating all possible pairs of products and categories for a comprehensive dataset.

Reviewing Different Types of UNION

In SQL, the UNION operation combines the results of two or more SELECT queries into a single result set. There are two main types: UNION DISTINCT, which removes duplicate rows, and UNION ALL, which includes all rows, even duplicates. Understanding these types helps select the appropriate operation for specific data merging needs.

Type 1: SQL UNION DISTCINT

UNION DISTINCT combines the result sets of two or more SELECT statements, showing only distinct values. The SQL syntax for UNION involves two tables with columns of matching data types, merging their results into a single set without duplicates.

Syntax of SQL UNION DISTINCT

SELECT column_1, column_2
FROM table_1
UNION DISTINCT
SELECT column_1, column_2
FROM table_2

SELECT column_1, column_2: Specifies the columns to retrieve from both tables.
FROM table_1: Indicates the first table from which to retrieve data.
UNION DISTINCT: Combines the results of the two SELECT statements into a single result set, including only distinct values.
SELECT column_1, column_2: Specifies the columns to retrieve from the second table.
FROM table_2: Indicates the second table from which to retrieve data.

Example 1: Using UNION DISTINCT

Let's use the UNION DISTINCT operation to combine the results from the Customers and Suppliers tables, ensuring only distinct cities and countries are included in the result set.

SELECT product_id
FROM Inventory
UNION DISTINCT
SELECT product_id
FROM Catalog;

Shows how UNION DISTINCT combines datasets and ensures unique records in the result.

SELECT product_id: Specifies the column to retrieve from both tables.
FROM Inventory: Indicates the first table from which to retrieve data.
UNION DISTINCT: Combines the results of the two SELECT statements into a single result set, including only distinct values.
SELECT product_id: Specifies the column to retrieve from the second table.
FROM Catalog: Indicates the second table from which to retrieve data.

By using the UNION DISTINCT operation, we ensure that only unique combinations of product_id from both the Inventory and Catalog tables are included in the final result set, providing a distinct and comprehensive dataset for analysis or reporting.

Example 2: Using UNION DISTINCT with WHERE Clause

In this example, we will use the UNION operation to combine the results from the Customers and Suppliers tables, filtering the data with a WHERE clause to include only those rows where the country is 'US'.

SELECT city, country
FROM Customers
WHERE country = 'US'
UNION DISTINCT
SELECT city, country
FROM Suppliers
WHERE country = 'US';

Here:

SELECT city, country: Specifies the columns to retrieve from both tables.
FROM Customers: Indicates the first table from which to retrieve data.
WHERE country = 'US': Filters the rows in the Customers table to include only those where the country is 'US'.
UNION DISTINCT: Combines the results of the two SELECT statements into a single result set, including only distinct values.
SELECT city, country: Specifies the columns to retrieve from the second table.
FROM Suppliers: Indicates the second table from which to retrieve data.
WHERE country = 'US': Filters the rows in the Suppliers table to include only those where the country is 'US'.

By using the UNION DISTCINT operation with a WHERE clause, we combine the unique cities and countries from both the Customers and Suppliers tables but only include those records where the.

Type 2: SQL UNION ALL

SQL UNION ALL combines the result sets of two or more SELECT statements, including all duplicate rows. Unlike UNION, it does not remove duplicates, ensuring that every row from each SELECT statement is included in the final result set, providing a complete dataset.

Syntax of SQL UNION ALL

SELECT column_1, column_2
FROM table_1
UNION ALL
SELECT column_1, column_2
FROM table_2;

SELECT column_1, column_2: Specifies the columns to retrieve from both tables.
FROM table_1: Indicates the first table from which to retrieve data.
UNION ALL: Combines the results of the two SELECT statements into a single result set, including all duplicate rows.
SELECT column_1, column_2: Specifies the columns to retrieve from the second table.
FROM table_2: Indicates the second table from which to retrieve data.

Example 1: Using UNION ALL

Let's use the UNION ALL operation to combine the results from the Employees and Contractors tables, including all rows, even duplicates. We want to retrieve the name and role columns from both tables.

SELECT name, status
FROM Customers
UNION ALL
SELECT name, status
FROM Contractors;

Combining datasets while retaining duplicates using UNION ALL.

Here:

SELECT name, status: Specifies the columns to retrieve from both tables.
FROM Customers: Indicates the first table from which to retrieve data.
UNION ALL: Combines the results of the two SELECT statements into a single result set, including all duplicate rows.
SELECT name, status: Specifies the columns to retrieve from the second table.
FROM Contractors: Indicates the second table from which to retrieve data.

Using the UNION ALL operation, you ensure that all records from both the Customers and Contractors tables are included in the result set, providing a comprehensive dataset that includes any duplicate entries.

Example 2: Using UNION ALL with WHERE Clause

In this example, we will use the UNION ALL operation to combine the results from the Customers and Suppliers tables, including all rows, even duplicates. The columns in both SELECT statements have matching data types, and the WHERE clause is also applied.

SELECT city, country
FROM Customers
WHERE country = 'US'
UNION ALL
SELECT city, country
FROM Suppliers
WHERE country = 'US';

Using UNION ALL to combine datasets, including duplicates, in a secondary example.

Here:

SELECT city, country FROM Customers: Retrieves the city and country columns from the Customers table, filtering by rows where the country is 'US'.
UNION ALL: Combines the results of the two SELECT statements, including all duplicate rows.
SELECT city, country FROM Suppliers: Retrieves the city and country columns from the Suppliers table, filtering by rows where the country is 'US'.

Using the UNION ALL operation, we ensure that all records from the Customers and Suppliers tables are included in the result set, even if there are duplicate rows. This approach provides a comprehensive dataset for analysis, capturing all relevant entries from both tables.

Advanced Techniques to Use JOIN and UNION IN SQL

Using JOIN and UNION operations in SQL is essential for effective data analysis and manipulation. Advanced methods enable you to create more efficient, powerful queries, ensuring comprehensive and accurate data retrieval for sophisticated analytical tasks.

Join Two tables based on Matching Fields Using INNER JOIN

Joining two tables with a matching field involves combining rows from both tables based on a shared column. This method, commonly used in INNER JOIN operations, links related data from different tables, enabling a cohesive dataset. It ensures that only rows with matching values in the specified column are included in the results.

Example:

Suppose you want to join the Categories and Products tables based on their matching cat_id fields; you can use the following SQL query.

SELECT Categories.cat_name, Products.prod_title
FROM Categories
INNER JOIN Products
ON Categories.cat_id = Products.cat_id;

Here:

SELECT Categories.cat_name, Products.prod_title: Specifies the columns to be retrieved from the tables. cat_name is from the Categories table, and prod_title is from the Products table.
FROM Categories: This indicates that the Categories table is the primary table from which we select data.
INNER JOIN Products: This clause specifies that we perform an INNER JOIN with the Products table.
ON Categories.cat_id = Products.cat_id: This condition ensures that only rows where the cat_id in the Categories table matches the cat_id in the Products table are included in the result set.

The query effectively joins the Categories and Products tables based on their shared cat_id field, extracting and displaying the cat_name and prod_title columns from the respective tables. This INNER JOIN operation ensures that the results only include products with a corresponding category, providing a clear and meaningful relationship between the data in both tables.

JOIN Multiple Tables

Joining multiple tables is crucial when the data needed for a query is spread across several tables within a database. This involves extending the basic join to include more than two tables by using multiple JOIN statements.

This approach provides a comprehensive view of related data from various sources, enabling complex data analysis and insights. You can extract and combine data efficiently to meet specific query requirements by linking multiple tables through their common fields.

Example:

This example demonstrates how to join three tables: Customers, Orders, and Shipping. This approach showcases the power of SQL joins in combining data from multiple sources to provide a complete and detailed dataset for analysis.

SELECT Customers.first_name, Orders.item, Shipping.status
FROM Customers
JOIN Orders ON Customers.customer_id = Orders.customer_id
JOIN Shipping ON Customers.customer_id = Shipping.customer_id;

Here:

SELECT Customers.first_name, Orders.item, Shipping.status: Specifies the columns to be retrieved from the tables.
FROM Customers: This indicates that the Customers table is the primary table from which we are selecting data.
JOIN Orders ON Customers.customer_id = Orders.customer_id: This clause performs an INNER JOIN between the Customers and Orders tables based on the customer_id field.
JOIN Shipping ON Customers.customer_id = Shipping.customer_id: This clause performs another INNER JOIN, this time between the Customers and Shippings tables, again using the customer_id field.

This query joins the Customers, Orders, and Shipping tables using their shared customer_id field. It retrieves the first_name from the Customers table, the item from the Orders table, and the status from the Shipping table. By joining these tables, the query provides a consolidated view of the customers' information, orders, and the shipping status, facilitating comprehensive data analysis across multiple tables.

Combining Different COLUMNS with UNION

The UNION operator allows you to combine different columns from multiple tables into a single result set. This technique enables flexible data consolidation, making merging comprehensive datasets from various sources easier for detailed analysis and reporting.

Example:

The following example illustrates combining columns from two tables, clients and staff, using the UNION ALL operator. This approach ensures a more detailed and complete result set, ideal for complex data analysis and reporting.

SELECT First_Name, Last_Name, City, Country, Role 
FROM `my_project.my_dataset.clients`
UNION ALL
SELECT First_Name, Last_Name, City, Country, 
Position AS Role
FROM `my_project.my_dataset.staff`;

Here:

SELECT First_Name, Last_Name, City, Country, Role FROM my_project.my_dataset.clients: This part selects the First_Name, Last_Name, City, Country, and Role columns from the clients' table.
UNION ALL: The UNION ALL operator combines the result set of the first SELECT statement with the result set of the second SELECT statement.
SELECT First_Name, Last_Name, City, Country, Position AS Role FROM my_project.my_dataset.staff: This part selects the First_Name, Last_Name, City, Country, and Position columns from the staff table.

Using the UNION operator allows you to combine columns from different tables into a cohesive result set. This method effectively aggregates data from various sources, providing a more complete view for analysis. Ensuring that the columns in each SELECT statement align, you maintain a uniform structure in the combined result, facilitating easier data manipulation and reporting.

Using LEFT JOIN With WHERE Clause

The LEFT JOIN statement in SQL combines rows from two tables, ensuring all records from the left table are included, even if there are no matching rows in the right table. Adding a WHERE clause allows you to filter the results based on specific conditions, refining the data set to meet particular criteria. This combination is useful for including all relevant data from the primary table while applying filters to the associated table.

Example:

The LEFT JOIN is useful for combining rows from two tables while including all records from the left table. You can filter the results using an optional WHERE clause based on specific conditions. The following example demonstrates how to join the Employees and Salaries tables and filter for employees with salaries greater than 60,000.

SELECT Employees.employee_id, Employees.name, Salaries.salary
FROM Employees
LEFT JOIN Salaries
ON Employees.employee_id = Salaries.employee_id
WHERE Salaries.salary > 60000;

Here:

SELECT Employees.employee_id, Employees.name, Salaries.salary: Selects employee_id and name from the Employees table and salary from the Salaries table.
FROM Employees: Sets Employees as the primary table.
LEFT JOIN Salaries ON Employees.employee_id = Salaries.employee_id: Includes all rows from Employees, adding matching rows from Salaries. Non-matching rows show NULL for Salaries columns.
WHERE Salaries.salary > 60000: Filters results to include only employees with a salary greater than 60,000. Non-matching rows from Salaries are not filtered out.

This query joins the Employees and Salaries tables and selects rows with a salary greater than 60,000. The LEFT JOIN ensures that all employees are included in the results, even if they don't have a corresponding salary record. It provides a complete view of employees and their salaries based on the specified conditions.

Combining Multiple Tables using UNION

By using the UNION ALL operator, multiple tables can be combined into a single data table, consolidating rows from each table into one unified result set. This technique is useful for merging similar datasets from different sources, ensuring a comprehensive and cohesive data collection for more efficient analysis and reporting.

Example:

Using the UNION ALL operator, we can combine several tables to create a single unified data table. The example below will merge data from three tables: employees, managers, and contractors, focusing on the City and Country columns.

SELECT City, Country FROM `my_project.my_dataset.employees`
UNION ALL
SELECT City, Country FROM `my_project.my_dataset.managers`
UNION ALL
SELECT City, Country FROM `my_project.my_dataset.contractors`;

Here:

SELECT City, Country FROM my_project.my_dataset.employees: Selects the City and Country columns from the employees table.
UNION ALL: Combines the result set of the first SELECT statement with the result set of the subsequent SELECT statements. UNION ALL includes all duplicates.
SELECT City, Country FROM my_project.my_dataset.managers: Selects the City and Country columns from the managers table.
UNION ALL: Continues to combine the result sets, ensuring that all entries are included.
SELECT City, Country FROM my_project.my_dataset.contractors: Selects the City and Country columns from the contractors table.

This query merges the City and Country data from the employees, managers, and contractors tables into a single result set. Using the UNION ALL operator ensures that all records from each table are included, providing a comprehensive dataset that combines information from multiple sources for enhanced analysis and reporting.

SQL UNION DISTINCT with ORDER BY

The UNION DISTINCT operation combines the results of multiple SELECT queries into one result set, excluding duplicates by default. When used with the ORDER BY clause, it sorts the aggregated results based on specified columns. This technique is useful for organizing data from different sources into a specific order, improving readability and analysis.

Example:

For instance, consider the employees and contractors tables containing first_name and last_name columns. Using UNION DISTINCT, we can combine these columns from both tables and sort the final result by last_name using the ORDER BY clause. This approach ensures a unified and organized dataset, enhancing readability and analysis.

SELECT
    first_name,
    last_name
FROM
    employees
UNION DISTINCT
SELECT
    first_name,
    last_name
FROM
    contractors
ORDER BY
    last_name;

Here :

SELECT first_name, last_name FROM employees: Retrieves the first_name and last_name columns from the employees table.
UNION DISTINCT: Combines the result set of the first SELECT statement with the result set of the second SELECT statement, removing duplicates by default.
SELECT first_name, last_name FROM contractors: Retrieves the first_name and last_name columns from the contractors table.
ORDER BY last_name: Sorts the combined result set by the last_name column.

Using the UNION DISTINCT operator with the ORDER BY clause, you can effectively merge and sort data from different sources, ensuring a cohesive and ordered dataset. This method facilitates easier data handling, readability, and reporting.

Difference Between JOIN and UNION in SQL

In SQL, both JOIN and UNION are essential for combining data from multiple tables, but they serve distinct purposes and operate differently. Below is a table that summarizes their key differences at a glance.

Operational Characteristics of JOIN and UNION

JOIN: Combines rows based on related columns, expanding the result set horizontally.
UNION: Merges rows from different SELECT statements, expanding the result set vertically.

Composition of Result Sets

JOIN: Produces rows that match the join condition.
UNION: Combines all rows from each SELECT statement into one result set.

Data Compatibility and Requirements

JOIN: Requires related columns between tables to establish connections with compatible data types.
UNION: Requires SELECT statements to have the same column structure and compatible data types.

Performance Factors of SQL Operations

JOIN: Can be computationally expensive, especially with large tables or lack of indexes.
UNION: Generally has better performance, requiring less computation and no complex comparisons.

Combination Type

JOIN: Records are combined into new columns.
UNION: Records are combined into new rows.

Orientation

JOIN: Facilitates the joining of tables horizontally.
UNION: Facilitates the connection of tables vertically.

Result Characteristics

JOIN: Used to produce the given table's intersection.
UNION: Used to produce the given table's conjunction.

Duplicate Handling

JOIN: Duplicate values can exist in JOIN results.
UNION: Duplicate values are removed by default in UNION DISTINCT results (use UNION ALL to include duplicates).

Column Requirement

JOIN: Requires at least one common column between the tables.
UNION: Requires the same number of columns with the same data types in the SELECT statements.

Choosing the Right Operation: JOIN or UNION?

Selecting between JOIN and UNION operations depends on data relationships, consistency, and specific use cases. Understanding these factors ensures efficient data handling and accurate query results.

Evaluating Data Relationships

JOIN operations are essential for combining rows from two or more tables based on a related column, merging data horizontally. This approach is suitable when your datasets share meaningful relationships, allowing you to create comprehensive datasets and gain deeper insights by merging related information for more complex data analysis.

Consistency of Data Structures

To use UNION, ensure that data structures across SELECT statements are consistent with compatible data types and column structures. This consistency is crucial for merging datasets effectively without errors. For JOINs, matching data types in the combined columns ensures smooth merging and accurate results, maintaining the integrity of the consolidated output.

Handling Data Duplication

JOIN operations can lead to duplicate records if not managed carefully, combining rows from multiple tables. UNION DISTINCT removes duplicates by default unless UNION ALL is specified, retaining all records. Consider the impact of duplicates on your data analysis, and choose the operation that aligns with your data accuracy and cleanliness needs.

Consideration of Specific Use Cases and Scenarios

JOIN is ideal for relational databases requiring detailed combined data from multiple tables, offering a comprehensive view of related information. UNION is better suited for consolidating results from separate queries into a single output, especially useful for merging similar datasets from various sources. Evaluate your specific requirements to choose the most suitable operation.

Tips for Optimizing Query Design

Follow these best practices to enhance the performance of JOIN and UNION operations. They include creating indexes, choosing appropriate join types, and limiting data retrieval, which can significantly improve query efficiency and speed.

Indexing Key Columns for Performance

Creating indexes on the columns involved in JOIN conditions or used for grouping can greatly enhance query performance. Indexes allow the database to locate and access the necessary data quickly, reducing query execution time. Proper indexing is crucial for optimizing database performance and ensuring efficient data retrieval in complex queries.

Choosing the Right Join Type

The appropriate join type is essential for achieving the desired outcome and maintaining efficient query performance. Different join types, such as INNER JOIN, LEFT JOIN, and RIGHT JOIN, serve distinct purposes. Choose the join type that best fits the relationship between the tables and the specific requirements of your query.

Optimizing Data Retrieval in Queries

Limit the data being retrieved by specifying only the necessary columns in your SELECT statement rather than retrieving all columns. This approach reduces the data transfer size, improves query performance, and ensures that the database processes only the required information. Efficient data retrieval is key to optimizing overall query performance.

Upgrade Your Proficiency with These Core BigQuery Functions

Mastering BigQuery functions is essential for efficient data analysis and manipulation. These are some key functions that will enhance your data handling capabilities, making complex tasks simpler and more effective.

String Functions: Useful for handling and manipulating string data when working with JOIN and UNION operations.
Data Manipulation Language (DML): Provides context on modifying data, which complements understanding JOIN and UNION operations.
Date Functions: Important for performing date-related operations within JOIN and UNION queries.
Conversion Functions: Helps in converting data types, which is often necessary when joining tables with different data formats.
Aggregate Functions: Essential for summarizing data, which can be used in conjunction with JOIN and UNION queries.
Navigation Functions: Useful for navigating through rows in the result set of JOIN and UNION queries.

Get Enhanced Data Insights with OWOX Reports Extension for Google Sheets

Unlock new levels of data insights by utilizing the OWOX Reports Extension for Google Sheets, a powerful tool designed to simplify complex data transformation tasks and optimize SQL operations like JOIN and UNION.

By leveraging the OWOX Reports, data professionals can effortlessly manage and manipulate data for in-depth analysis. This tool ensures that your JOIN operations, which combine columns from related tables based on a common key, and UNION operations, which merge the results of multiple queries into a single result set, are executed efficiently and accurately.

When dealing with SQL operations, the OWOX Reports Extension for Google Sheets guarantees error-free data handling and improved data quality. This means you can focus on optimizing your queries, whether combining columns from related tables with JOIN or merging similar data sets with UNION. Empower your analytics with this extension to achieve precise, actionable insights and take your SQL queries to the next level.