Category: Data Query Language (DQL)

Platform Support:

✅ Snowflake | ✅ BigQuery | ✅ Databricks

Description

The SELECT statement retrieves data from one or more tables in a database. It's the most fundamental and commonly used SQL command for querying data.

Syntax

SELECT column1, column2, ...
FROM table_name
[WHERE condition]
[GROUP BY column]
[HAVING condition]
[ORDER BY column]
[LIMIT n]

SELECT column1, column2, ...
FROM table_name
[WHERE condition]
[GROUP BY column]
[HAVING condition]
[ORDER BY column]
[LIMIT n]

SELECT column1, column2, ...
FROM table_name
[WHERE condition]
[GROUP BY column]
[HAVING condition]
[ORDER BY column]
[LIMIT n]

Platform-Specific Notes

Snowflake:

• Supports LIMIT and TOP syntax

• Case-insensitive by default

• Supports QUALIFY clause for window function filtering

BigQuery:

• Uses LIMIT (not TOP)

• Requires backticks for reserved keywords

• Supports QUALIFY clause

Databricks:

• Supports both LIMIT and TOP

• Case-insensitive by default

• Does not support QUALIFY (use subquery instead)

Example 1: Basic SELECT

SELECT first_name, email, country
FROM customers
WHERE country = 'USA'

SELECT first_name, email, country
FROM customers
WHERE country = 'USA'

SELECT first_name, email, country
FROM customers
WHERE country = 'USA'

Sample Data (customers table):

Result:

Example 2: SELECT with Aggregation

All Platforms:

SELECT 
    country,
    COUNT(*) as customer_count,
    COUNT(DISTINCT email) as unique_emails
FROM customers
GROUP BY country
ORDER BY customer_count DESC

SELECT 
    country,
    COUNT(*) as customer_count,
    COUNT(DISTINCT email) as unique_emails
FROM customers
GROUP BY country
ORDER BY customer_count DESC

SELECT 
    country,
    COUNT(*) as customer_count,
    COUNT(DISTINCT email) as unique_emails
FROM customers
GROUP BY country
ORDER BY customer_count DESC

Sample Data:

Result:

Best Practices

• Always specify column names explicitly rather than using SELECT * in production code

• Use aliases for better readability

• Add LIMIT for exploratory queries on large tables

• Consider using QUALIFY (Snowflake/BigQuery) for filtering window functions