<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Fundamental SQL Syntax

-- Select all columns from a table
SELECT * FROM table_name;

-- Select specific columns from a table
SELECT column1, column2 FROM table_name;

-- Filtering rows with WHERE clause
SELECT * FROM table_name WHERE condition;

-- Sorting results with ORDER BY
SELECT * FROM table_name ORDER BY column1 ASC;  -- ASC for ascending, DESC for descending

• The SELECT * command is the simplest way to retrieve all data from a table. However, it is often not recommended for production queries because it can retrieve unnecessary data. It's better to select only the columns you need.
• By specifying the columns, you ensure your query is more efficient and only returns the data you actually need. This is crucial for performance, especially with large datasets.
• The WHERE clause allows you to filter records based on specific conditions, making it a powerful tool for data retrieval. This is the foundation of SQL querying and is used to hone in on the exact data you need.
• Sorting data is often necessary for analysis. The ORDER BY clause sorts the result set by one or more columns. ASCspecifies ascending order (default), while DESC specifies descending order. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Data Querying

-- Basic SELECT statement
SELECT column1, column2 FROM table_name;

-- Using WHERE clause for filtering
SELECT * FROM table_name WHERE condition;

-- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
SELECT COUNT(column_name) FROM table_name;
SELECT SUM(column_name) FROM table_name;

-- GROUP BY clause
SELECT column_name, COUNT(*) FROM table_name
GROUP BY column_name;

-- HAVING clause (used with GROUP BY)
SELECT column_name, COUNT(*) FROM table_name
GROUP BY column_name
HAVING COUNT(*) > value;

-- JOIN operations
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column;

• The SELECT statement is the most common SQL command, used to retrieve data from one or more tables. Understanding how to write efficient SELECT statements is key to extracting meaningful insights from your data.
• Filtering data with the WHERE clause helps you narrow down the results to only those that meet specific criteria, making your queries more targeted and relevant.
• Aggregate functions perform a calculation on a set of values and return a single value. These functions are essential for reporting and data analysis, allowing you to summarize large datasets quickly.
• The GROUP BY clause is used with aggregate functions to group the result set by one or more columns. This is useful for creating summaries, such as counting the number of records in each category.
• The HAVING clause is similar to WHERE but is used to filter groups created by the GROUP BY clause. This allows you to apply conditions to aggregated data.
• JOIN operations combine rows from two or more tables based on a related column between them. Understanding different types of joins (INNER JOIN, LEFT JOIN, etc.) is crucial for working with relational databases. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Data Manipulation Language (DML)

-- Insert data into a table
INSERT INTO table_name (column1, column2, ...)
VALUES (value1, value2, ...);

-- Update existing data
UPDATE table_name
SET column1 = value1, column2 = value2
WHERE condition;

-- Delete data from a table
DELETE FROM table_name
WHERE condition;

• The INSERT INTO statement is used to add new records to a table. Each value must correspond to a specific column in the table. This command is fundamental for populating your database with data.
• The UPDATE command modifies existing records in a table. The WHERE clause is critical here; without it, all records in the table will be updated, which is often not the desired outcome.
• The DELETE statement removes records from a table. Similar to UPDATE, the WHERE clause is necessary to target specific records. Without it, all records will be deleted. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Transactions

-- Start a transaction
BEGIN TRANSACTION;

-- Commit a transaction
COMMIT;

-- Rollback a transaction
ROLLBACK;

• Transactions group a series of SQL operations into a single, atomic unit of work. This ensures that either all operations succeed, or none of them do, which is crucial for maintaining data consistency.
• The COMMIT command finalizes the transaction, making all changes permanent. Once committed, the changes cannot be rolled back.
• If something goes wrong during a transaction, the ROLLBACK command undoes all changes made during the transaction. This is essential for error handling and ensuring data integrity. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Common Error Handling

-- Handling NULL values
SELECT column_name FROM table_name
WHERE column_name IS NULL;

-- Avoiding SQL injection (parameterised queries)
SELECT column_name FROM table_name
WHERE column_name = ?;

• NULL values represent missing or unknown data. Understanding how to handle NULLs is crucial because they can affect the outcome of queries, especially in conditions and joins.
• SQL injection is a common security vulnerability where attackers can manipulate your queries by injecting malicious code. Using parameterized queries prevents this by separating SQL logic from data. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Data Definition Language (DDL)

-- Create a new table
CREATE TABLE table_name (
    column1 datatype,
    column2 datatype,
    ...
);

-- Alter an existing table (add a column)
ALTER TABLE table_name
ADD column_name datatype;

-- Drop a table
DROP TABLE table_name;

• The CREATE TABLE statement is used to create a new table in the database. Each column is defined with a name and a data type, which specifies the kind of data it can hold (e.g., INTEGER, VARCHAR, DATE).
• The ALTER TABLE statement allows you to modify an existing table structure, such as adding new columns. This is essential when your database evolves, and you need to accommodate new types of data.
• The DROP TABLE command is used to delete an entire table from the database. This is irreversible, so it’s important to use it with caution. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Advanced Queries

-- Subqueries
SELECT column1 FROM table_name
WHERE column2 = (SELECT column3 FROM another_table WHERE condition);

-- UNION operation
SELECT column1 FROM table_name1
UNION
SELECT column1 FROM table_name2;

-- CASE statement for conditional logic
SELECT column1,
CASE
    WHEN condition1 THEN result1
    WHEN condition2 THEN result2
    ELSE result3
END AS alias_name
FROM table_name;

• A subquery is a query within another query. It allows you to perform more complex operations by breaking down tasks into smaller, manageable queries. Subqueries are powerful tools for dynamic data retrieval.
• The UNION operator combines the results of two or more SELECT statements into a single result set. This is useful when you need to merge data from different tables or queries.
• The CASE statement allows you to add conditional logic to your SQL queries. This is similar to IF-THEN-ELSE statements in programming languages, enabling dynamic results based on different conditions. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Useful SQL Functions

-- String functions
SELECT UPPER(column_name) FROM table_name;
SELECT LOWER(column_name) FROM table_name;
SELECT LENGTH(column_name) FROM table_name;

-- Date functions
SELECT CURRENT_DATE;
SELECT CURRENT_TIME;
SELECT EXTRACT(YEAR FROM date_column) FROM table_name;

-- Numeric functions
SELECT ROUND(column_name, decimals) FROM table_name;
SELECT ABS(column_name) FROM table_name;

• String functions allow you to manipulate text data directly within your queries. For example, UPPER() converts a string to uppercase, which is useful for standardizing text data.
• LOWER() converts a string to lowercase, useful for case-insensitive comparisons.
• LENGTH() returns the number of characters in a string. This can be helpful for validating or analyzing text data.
• Date functions help you work with date and time data. CURRENT_DATE returns the current date, which is often used in reporting and scheduling queries.
• CURRENT_TIME returns the current time, useful for timestamping events.
• EXTRACT() retrieves a specific part of a date, such as the year or month. This is valuable for time-based analysis, such as yearly trends.
• Numeric functions perform calculations on numeric data. ROUND() rounds a number to a specified number of decimal places, which is essential for financial calculations.
• ABS() returns the absolute value of a number, useful in situations where you need non-negative results. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Indexes and Keys

-- Create an index
CREATE INDEX index_name ON table_name (column_name);

-- Create a unique index
CREATE UNIQUE INDEX index_name ON table_name (column_name);

-- Add a primary key
ALTER TABLE table_name
ADD PRIMARY KEY (column_name);

-- Add a foreign key
ALTER TABLE table_name
ADD CONSTRAINT fk_name
FOREIGN KEY (column_name) REFERENCES other_table(column_name);

• An index is a database object that improves the speed of data retrieval operations on a table. By indexing columns that are frequently searched or sorted, you can significantly enhance query performance.
• A unique index ensures that all values in the indexed column are unique. This is often used to enforce the uniqueness of a primary key or other important fields.
• A primary key is a column or a set of columns that uniquely identifies each row in a table. It is a critical concept in relational database design, ensuring data integrity and efficient indexing.
• A foreign key establishes a relationship between two tables, ensuring that the values in the foreign key column match values in the referenced table's primary key. This is fundamental for maintaining referential integrity in relational databases. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> Common SQL Clauses

-- DISTINCT clause
SELECT DISTINCT column_name FROM table_name;

-- LIMIT clause
SELECT * FROM table_name LIMIT number_of_rows;

-- OFFSET clause
SELECT * FROM table_name LIMIT number_of_rows OFFSET offset_value;

• The DISTINCT clause eliminates duplicate records in your result set. This is especially useful when you need to list unique values from a column.
• The LIMIT clause restricts the number of rows returned by a query. This is helpful for previewing large datasets or fetching a specific subset of results.
• The OFFSET clause, used in combination with LIMIT, allows you to skip a specific number of rows before starting to return rows. This is useful for pagination in web applications. </aside>

<aside> <img src="/icons/code_orange.svg" alt="/icons/code_orange.svg" width="40px" /> SQL Best Practices

• Use meaningful table and column names: Ensure that names are descriptive and easy to understand. This makes your database schema self-explanatory and easier to maintain.

Explanation: Well-named tables and columns reduce the cognitive load on developers and analysts, making it easier to understand the data structure and intent behind each query.

• Normalize your database: Avoid data redundancy by normalizing your tables. Normalization improves data integrity and reduces storage costs.

Explanation: Normalization involves organizing your data so that redundancy is minimized, typically by splitting large tables into smaller, related tables. This leads to more efficient updates and queries.

• Indexing: Use indexes on columns that are frequently used in WHERE clauses to improve performance.

Explanation: Indexes make data retrieval faster, but they also consume space and can slow down write operations. Knowing when and where to use indexes is key to maintaining a balanced database.

• Avoid SELECT : Instead, specify the columns you need to retrieve.

Explanation: SELECT * can lead to inefficient queries, especially in large tables. By specifying only the columns you need, you reduce the amount of data processed and transferred.

• Keep SQL queries simple and readable: Break down complex queries into smaller subqueries or use comments for clarity.

Explanation: Readable queries are easier to debug and maintain. Using subqueries and comments helps to break down complex logic, making it easier for others (and your future self) to understand.

Professional Practices: Following these best practices is essential for writing efficient, maintainable SQL code. In the full course, you’ll learn advanced techniques for query optimization, indexing strategies, and database design.

</aside>

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