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March 23, 2026/4 min read

SQL Commands Cheat Sheet

Complete SQL Reference for Database Operations

Sample Database Structure

This cheat sheet uses two example tables: a 'rep' table containing sales representative information (name, ID, state) and a 'daily_sales' table tracking revenue and rep performance by date.

This comprehensive SQL cheat sheet uses a practical sales analytics scenario to demonstrate key database operations. We'll work with two interconnected tables: the "rep" table containing sales representative information (name, ID, and state), and the "daily_sales" table tracking individual transactions with corresponding rep IDs and revenue amounts. This real-world example mirrors the data structures you'll encounter in modern business intelligence and analytics workflows.

Syntax

Master these fundamental SQL commands to manipulate data efficiently and extract meaningful insights from your databases.

SQL Command Categories

Data Definition Language (DDL)

Commands like CREATE TABLE and ALTER TABLE that define database structure and schema modifications.

Data Manipulation Language (DML)

Commands including SELECT, INSERT, UPDATE, and DELETE for working with data within tables.

Data Query Language (DQL)

Advanced SELECT operations with joins, aggregations, and filtering for complex data retrieval.

Working with Tables

CREATE TABLE

Create a new table with defined columns and data types

CREATE TABLE table_name (

column_1 datatype,

column_2 datatype,

column_3 datatype); 

DELETE

Remove specific records from a table based on defined criteria

DELETE FROM table_name 

WHERE column_name = xyz;

ALTER TABLE

Modify existing table structure by adding new columns

ALTER TABLE table_name

ADD column_name datatype;

AS

Create column aliases for cleaner output and improved readability

SELECT column_name AS 'Alias'

FROM table_name;

INSERT

Add new records to an existing table

INSERT INTO table_name (column_1, column_2, column_3) 

VALUES (value_1, 'value_2', value_3);

LIMIT

Restrict query results to a specified number of rows for performance optimization

SELECT column_name(s)

FROM table_name

LIMIT number;

UPDATE

Modify existing records based on specified conditions

UPDATE table_name

SET some_column = some_value

WHERE some_column = some_value;

Essential Table Operations

CREATE TABLE
9
INSERT
8
UPDATE
7
DELETE
6
ALTER TABLE
5
Column Aliasing Best Practice

Use the AS keyword to rename columns for better readability in query results. This is especially useful when working with calculated fields or joining multiple tables.

LOGIC

These logical operators and conditions enable sophisticated data filtering and conditional processing essential for business analytics.

BETWEEN

Filter records within a specific range of values

SELECT column_name(s)

FROM table_name

WHERE column_name BETWEEN value_1 AND value_2;

CASE WHEN

Implement conditional logic to create calculated columns based on multiple criteria

SELECT column_name,

CASE

WHEN condition THEN 'Result_1'

WHEN condition THEN 'Result_2'

ELSE 'Result_3'

END AS new_column

FROM table_name;

GROUP BY

Aggregate data by one or more columns, essential for creating summary reports

SELECT column_name, COUNT(*)

FROM table_name

GROUP BY column_name;

HAVING

Apply filters to grouped data after aggregation (use instead of WHERE with aggregate functions)

SELECT column_name, COUNT(*)

FROM table_name

GROUP BY column_name

HAVING COUNT(*) > value;

IS NULL

Identify records with missing or undefined values

SELECT column_name(s)

FROM table_name

WHERE column_name IS NULL;

LIKE

Perform pattern matching with wildcards for flexible text searches

SELECT column_name(s)

FROM table_name

WHERE column_name LIKE 'xyz';

AND 

Combine multiple conditions that must all be true

SELECT column_name 

FROM table_name

WHERE column_1 = xyz

AND column_2 = abc;

OR

Select records that meet any one of multiple conditions

SELECT column_name

FROM table_name

WHERE column_name = value_1

OR column_name = value_2;

DISTINCT

Eliminate duplicate values to return only unique records

SELECT DISTINCT column_name

FROM table_name;

Conditional Operators Comparison

FeatureANDOR
Logic TypeAll conditions must be trueAt least one condition must be true
Use CaseNarrow down resultsBroaden search criteria
PerformanceMore selective, fasterLess selective, potentially slower
Recommended: Use AND for precise filtering, OR for inclusive searches

Building Complex Conditions

1

Start with Basic WHERE

Begin with simple equality conditions to establish your base dataset

2

Add Range Filters

Use BETWEEN for date ranges or numerical boundaries to further refine results

3

Apply Pattern Matching

Implement LIKE with wildcards for flexible text searching capabilities

4

Combine with Logic Operators

Use AND/OR to create sophisticated multi-criteria filtering conditions

MATH

Statistical and mathematical functions are crucial for data analysis, reporting, and generating business insights from raw data.

COUNT

Calculate the total number of records or non-null values

SELECT COUNT(column_name)

FROM table_name;

MAX

Find the highest value in a numeric or date column

SELECT MAX(column_name)

FROM table_name;

MIN

Identify the lowest value in a numeric or date column

SELECT MIN(column_name)

FROM table_name;

ORDER BY

Sort query results in ascending or descending order for better data presentation

SELECT column_name

FROM table_name

ORDER BY column_name ASC | DESC;

ROUND

Format numeric values to a specified number of decimal places

SELECT ROUND(column_name, integer)

FROM table_name;

SUM

Calculate the total of all values in a numeric column

SELECT SUM(column_name)

FROM table_name;

AVG

Compute the arithmetic mean of values in a numeric column

SELECT AVG(column_name)

FROM table_name;

SQL Aggregate Functions

COUNT

Returns the number of rows that match specified criteria. Essential for data validation and reporting.

SUM & AVG

Calculate totals and averages for numerical data. Critical for financial and statistical analysis.

MAX & MIN

Find extreme values in datasets. Useful for identifying outliers and boundary conditions.

ORDER BY Performance

Sorting operations can be resource-intensive on large datasets. Consider adding indexes on frequently sorted columns to improve query performance.

JOINS

Master these join operations to combine data from multiple tables and create comprehensive analytical reports.

INNER JOIN

Combine tables showing only records with matching values in both tables

SELECT column_name(s)

FROM table_1

JOIN table_2

ON table_1.column_name = table_2.column_name;

OUTER JOIN

Combine tables retaining all records from one table plus matching records from another

SELECT column_name(s)

FROM table_1

LEFT JOIN table_2

ON table_1.column_name = table_2.column_name;

JOIN Types Comparison

FeatureINNER JOINLEFT JOIN
Data ReturnedOnly matching recordsAll left table records
Missing Data HandlingExcludes non-matchesShows NULL for missing
Best ForComplete data analysisComprehensive reporting
Recommended: Use INNER JOIN for precise matching, LEFT JOIN for complete coverage
JOIN Performance Consideration

Always ensure proper indexes exist on join columns. Joining large tables without indexes can severely impact query performance and database responsiveness.

Applications

Apply these SQL concepts to solve real business problems using our sales database example. These practical queries demonstrate how to extract actionable insights from your data.

Real-World Examples

The following examples demonstrate practical applications using sales representative and daily sales data, showing how SQL commands work together in business scenarios.

Find the Name of Rep 555

SELECT name

FROM rep

WHERE ID = '555';

“
SELECT name FROM rep WHERE ID = '555';
Simple lookup query demonstrating basic WHERE clause usage for finding specific records by unique identifier.

Find the Maximum Revenue in One Day

SELECT MAX(revenue)

FROM daily_sales;

“
SELECT MAX(revenue) FROM daily_sales;
Aggregate function example showing how to find peak performance metrics across entire dataset.

Get Total Revenue by Day

SELECT date, SUM(revenue)

FROM daily_sales

GROUP BY date

ORDER BY date;

Revenue by State and by Day 

SELECT a.state, b.date, SUM(revenue)

FROM rep a

JOIN daily_sales b

ON a.ID = b.ID

GROUP BY state, date;

“
SELECT a.state, b.date, sum(revenue) FROM rep a JOIN daily_sales b ON a.ID = b.ID GROUP BY state, date;
Advanced multi-table join with grouping, demonstrating how to combine data from multiple sources for detailed reporting.

Query Optimization Checklist

0/4

Key Takeaways

1SQL commands fall into three main categories: DDL for structure, DML for data manipulation, and DQL for complex queries
2Table operations including CREATE, INSERT, UPDATE, and DELETE form the foundation of database management
3Logic operators like AND, OR, BETWEEN, and LIKE enable sophisticated filtering and search capabilities
4Mathematical functions COUNT, SUM, AVG, MAX, and MIN provide essential data analysis and reporting functionality
5INNER JOIN returns only matching records while LEFT JOIN preserves all records from the primary table
6GROUP BY operations combined with aggregate functions enable powerful data summarization and reporting
7Real-world applications often require combining multiple SQL concepts like joins, grouping, and sorting in single queries
8Proper indexing on frequently queried and joined columns is crucial for maintaining query performance at scale

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