Master Product Data Analytics

These are the most basic and essential SQL keywords. They form the foundation of most queries.

• SELECT: Specifies the columns you want to retrieve from a table.
• FROM: Specifies the table you want to retrieve data from.
• WHERE: Filters the data based on a specified condition.

Example:

                                
                                    SELECT user_id, name, email
                                    FROM users
                                    WHERE country = 'USA';
                                
                            

This query retrieves the `user_id`, `name`, and `email` columns from the `users` table, but only for rows where the `country` column is equal to 'USA'.

References:

• W3Schools: SQL SELECT Statement
• W3Schools: SQL WHERE Clause
• Wikipedia: SELECT (SQL)

JOINs are used to combine data from two or more tables based on a related column between them.

• INNER JOIN: Returns rows when there is a match in both tables.
• LEFT (OUTER) JOIN: Returns all rows from the left table, and the matched rows from the right table. If there is no match, it returns NULL for the columns from the right table.
• RIGHT (OUTER) JOIN: Returns all rows from the right table, and the matched rows from the left table. If there is no match, it returns NULL for the columns from the left table.
• FULL (OUTER) JOIN: Returns all rows when there is a match in either the left or right table. If there is no match, it returns NULL for the columns from the non-matching table.

Example:

                                
                                    SELECT orders.order_id, users.name
                                    FROM orders
                                    INNER JOIN users ON orders.user_id = users.user_id;
                                
                            

This query returns the `order_id` from the `orders` table and the `name` from the `users` table, but only for rows where the `user_id` is the same in both tables (i.e., only for orders that are associated with a user).

References:

• W3Schools: SQL JOIN
• Wikipedia: Join (SQL)

The `GROUP BY` statement groups rows that have the same values in one or more columns into a summary row. Aggregate functions are often used with `GROUP BY` to perform calculations on each group.

• COUNT: Counts the number of rows in each group.
• SUM: Calculates the sum of a numeric column in each group.
• AVG: Calculates the average of a numeric column in each group.
• MIN: Returns the minimum value of a column in each group.
• MAX: Returns the maximum value of a column in each group.

Example:

                                
                                    SELECT country, COUNT(*) AS num_users
                                    FROM users
                                    GROUP BY country;
                                
                            

This query groups the rows in the `users` table by the `country` column and then counts the number of users in each country.

References:

• W3Schools: SQL GROUP BY Statement
• W3Schools: SQL COUNT, AVG, and SUM Functions
• W3Schools: SQL MIN and MAX Functions
• Wikipedia: GROUP BY (SQL)

The `ORDER BY` clause sorts the result set in ascending or descending order based on one or more columns.

Example:

                                
                                    SELECT user_id, name, registration_date
                                    FROM users
                                    ORDER BY registration_date DESC;
                                
                            

This query retrieves the `user_id`, `name`, and `registration_date` columns from the `users` table and sorts the results in descending order of `registration_date`.

References:

• W3Schools: SQL ORDER BY Clause
• Wikipedia: ORDER BY (SQL)

The `HAVING` clause is used to filter the results of a `GROUP BY` query based on aggregate functions. It's similar to the `WHERE` clause, but it operates on grouped data rather than individual rows.

Example:

                                
                                    SELECT country, COUNT(*) AS num_users
                                    FROM users
                                    GROUP BY country
                                    HAVING COUNT(*) > 100;
                                
                            

This query groups the rows in the `users` table by `country`, counts the number of users in each country, and then filters the results to include only countries with more than 100 users.

References:

• W3Schools: SQL HAVING Clause
• Wikipedia: HAVING (SQL)

Window functions perform calculations across a set of rows that are related to the current row. They are similar to aggregate functions, but instead of returning a single value for each group, they return a value for each row.

• ROW_NUMBER: Assigns a unique sequential integer to each row within the partition.
• RANK: Assigns a rank to each row within the partition, with the same rank for equal values. Gaps may appear in the sequence.
• DENSE_RANK: Similar to RANK, but without gaps in the ranking sequence.
• LAG: Accesses data from a previous row in the result set.
• LEAD: Accesses data from a following row in the result set.
• NTILE: Divides the rows within a partition into a specified number of groups (e.g., quartiles, deciles).

Example:

                                
                                    SELECT
                                        order_id,
                                        order_date,
                                        amount,
                                        RANK() OVER (PARTITION BY customer_id ORDER BY order_date) as order_rank
                                    FROM orders;
                                
                            

This query assigns a rank to each order within each customer's set of orders, based on the order date.

References:

• W3Schools: SQL Window Functions (Note: W3Schools' coverage of window functions is limited)
• Wikipedia: Window function (SQL)

Subqueries are queries nested within another query. They can be used to perform operations in multiple steps or to filter data based on the results of another query.

CTEs (Common Table Expressions) are temporary result sets that you can reference within a `SELECT`, `INSERT`, `UPDATE`, or `DELETE` statement. They are defined using the `WITH` clause and are useful for breaking down complex queries into smaller, more manageable parts.

Example (Subquery):

                                
                                    SELECT order_id, amount
                                    FROM orders
                                    WHERE customer_id IN (SELECT customer_id FROM customers WHERE country = 'USA');
                                
                            

This query uses a subquery in the `WHERE` clause to select only orders from customers in the USA.

Example (CTE):

                                
                                    WITH USACustomers AS (
                                        SELECT customer_id
                                        FROM customers
                                        WHERE country = 'USA'
                                    )
                                    SELECT order_id, amount
                                    FROM orders
                                    WHERE customer_id IN (SELECT customer_id FROM USACustomers);
                                
                            

This query uses a CTE called `USACustomers` to define a temporary result set of customers from the USA, and then uses that CTE in the main query to select orders from those customers.

References:

• W3Schools: SQL Subqueries
• Wikipedia: Common Table Expression
• PostgreSQL Documentation: WITH Queries (Common Table Expressions) - This is a more in-depth resource.

SQL provides a variety of functions for manipulating strings, such as:

• CONCAT: Concatenates two or more strings.
• SUBSTRING: Extracts a substring from a string.
• LENGTH: Returns the length of a string.
• UPPER/LOWER: Converts a string to uppercase or lowercase.
• REPLACE: Replaces occurrences of a substring within a string.
• TRIM: Removes leading and/or trailing spaces from a string.

Example:

                                
                                    SELECT CONCAT(first_name, ' ', last_name) AS full_name
                                    FROM users;
                                
                            

References:

• W3Schools: SQL String Functions (Note: This is a general overview of SQL functions, including string functions)
• PostgreSQL Documentation: String Functions and Operators (Specific to PostgreSQL, but many functions are common across database systems)

SQL provides functions for working with dates and times, such as:

• NOW/CURRENT_DATE/CURRENT_TIMESTAMP: Returns the current date and/or time.
• DATE_PART/EXTRACT: Extracts a specific part of a date or time (e.g., year, month, day).
• DATE_ADD/DATE_SUB: Adds or subtracts a time interval from a date.
• DATEDIFF: Calculates the difference between two dates.

Example:

                                
                                    SELECT order_id, order_date
                                    FROM orders
                                    WHERE DATE_PART('year', order_date) = 2023;
                                
                            

References:

• W3Schools: SQL Dates
• PostgreSQL Documentation: Date/Time Functions and Operators (Specific to PostgreSQL, but many functions are common across database systems)

Most database systems provide a way to view the execution plan of a query, which shows how the database will execute the query. This can help you identify bottlenecks and areas for improvement.

Example (PostgreSQL):

                                
                                    EXPLAIN SELECT * FROM users WHERE country = 'USA';
                                
                            

References:

• PostgreSQL Documentation: EXPLAIN
• MySQL Documentation: EXPLAIN Output Format
• SQL Server Documentation: Displaying Graphical Execution Plans

Indexes are data structures that improve the speed of data retrieval operations on a database table. Creating appropriate indexes on frequently queried columns can significantly speed up queries.

Example:

                                
                                    CREATE INDEX idx_country ON users (country);
                                
                            

References:

• W3Schools: SQL CREATE INDEX Statement
• Wikipedia: Database Index

Here are some tips for writing efficient joins and filters:

• Use `INNER JOIN` instead of `WHERE` clause to join tables when possible.
• Filter data as early as possible in the query using `WHERE` and `HAVING` clauses.
• Avoid using `OR` conditions in `JOIN` clauses, as they can lead to poor performance.
• Use `EXISTS` instead of `COUNT(*)` to check for the existence of rows.

SQL provides functions for dealing with NULL values:

• COALESCE: Returns the first non-NULL expression in a list.
• IS NULL / IS NOT NULL: Checks if a value is NULL or not.
• NULLIF: Returns NULL if two expressions are equal, otherwise returns the first expression.

Example:

                                
                                    SELECT COALESCE(email, 'N/A') as email
                                    FROM users;
                                
                            

W3Schools: SQL NULL Values

You may need to convert data from one type to another (e.g., string to integer, date to string). SQL provides functions like `CAST` and `CONVERT` for this purpose.

Example:

                                
                                    SELECT CAST(order_date AS DATE)
                                    FROM orders;
                                
                            

W3Schools: SQL CAST Function (SQL Server syntax, but similar functions exist in other database systems)

Duplicate rows can skew your analysis. You can use `DISTINCT` or `GROUP BY` to identify and remove duplicates.

Example:

                                
                                    SELECT DISTINCT user_id
                                    FROM users;
                                
                            

W3Schools: SQL DISTINCT Statement