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Find top SQL resources from global universities, cool projects, and learning materials for data analytics. Admin: @coderfun Useful links: heylink.me/DataAnalytics Promotions: @love_data
Больше📈 Аналитический обзор Telegram-канала SQL Programming Resources
Канал SQL Programming Resources (@sqlanalyst) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 76 022 подписчиков, занимая 1 692 место в категории Технологии и приложения и 4 139 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 76 022 подписчиков.
Согласно последним данным от 30 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 387, а за последние 24 часа — 39, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.34%. В первые 24 часа после публикации контент обычно набирает 1.23% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 1 779 просмотров. В течение первых суток публикация набирает 934 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как row, sql, customer_id, logic, desc.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Find top SQL resources from global universities, cool projects, and learning materials for data analytics.
Admin: @coderfun
Useful links: heylink.me/DataAnalytics
Promotions: @love_data”
Благодаря высокой частоте обновлений (последние данные получены 01 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
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| 01 июля | +9 |
Посты канала
🚀 𝗙𝗿𝗲𝗲 𝗦𝗤𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 📊💻
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| 2 | 📌 Question 88: Find Customers Who Purchased in Every Month of a Year
Table: orders (customer_id, order_date)
SELECT
customer_id
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2025
GROUP BY customer_id
HAVING COUNT(
DISTINCT EXTRACT(MONTH FROM order_date)
) = 12;
📌 Question 89: Find the Most Frequently Bought Product After Product A
Table: order_items (order_id, product_id, sequence_no)
SELECT
b.product_id,
COUNT(*) AS purchase_count
FROM order_items a
JOIN order_items b
ON a.order_id = b.order_id
AND b.sequence_no = a.sequence_no + 1
WHERE a.product_id = 'Product_A'
GROUP BY b.product_id
ORDER BY purchase_count DESC
LIMIT 1;
📌 Question 90: Calculate Customer Lifetime in Days
Tables: customers (customer_id) orders (customer_id, order_date)
SELECT
customer_id,
MAX(order_date) - MIN(order_date) AS lifetime_days
FROM orders
GROUP BY customer_id;
❤️ Double Tap For More | 810 |
| 3 | 🚀 SQL Scenario-Based Interview Questions with Answers (Part 9)
💼 FAANG & Product Company SQL Interview Scenarios
📌 Question 81: Find Users Who Were Active on 3 Consecutive Days
Table: user_activity (user_id, activity_date)
WITH activity AS (
SELECT DISTINCT
user_id,
activity_date
FROM user_activity
),
groups AS (
SELECT
user_id,
activity_date,
activity_date -
ROW_NUMBER() OVER (
PARTITION BY user_id
ORDER BY activity_date
) * INTERVAL '1 day' AS grp
FROM activity
)
SELECT
user_id,
COUNT() AS consecutive_days
FROM groups
GROUP BY user_id, grp
HAVING COUNT() >= 3;
📌 Question 82: Find the Top 5 Selling Products in Each Category
Tables: products (product_id, category) sales (product_id, quantity)
WITH product_sales AS (
SELECT
p.category,
s.product_id,
SUM(s.quantity) AS total_quantity
FROM sales s
JOIN products p
ON s.product_id = p.product_id
GROUP BY p.category, s.product_id
)
SELECT *
FROM (
SELECT *,
DENSE_RANK() OVER (
PARTITION BY category
ORDER BY total_quantity DESC
) AS rnk
FROM product_sales
) t
WHERE rnk <= 5;
📌 Question 83: Find Customers Whose Latest Order Is Their Highest Value Order
Table: orders (customer_id, order_date, amount)
WITH customer_orders AS (
SELECT *,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY order_date DESC
) AS latest_order,
RANK() OVER (
PARTITION BY customer_id
ORDER BY amount DESC
) AS highest_order
FROM orders
)
SELECT
customer_id,
order_date,
amount
FROM customer_orders
WHERE latest_order = 1
AND highest_order = 1;
📌 Question 84: Calculate Rolling 30-Day Revenue
Table: sales (sale_date, amount)
SELECT
sale_date,
SUM(amount) OVER (
ORDER BY sale_date
RANGE BETWEEN INTERVAL '29 days' PRECEDING
AND CURRENT ROW
) AS rolling_30_day_revenue
FROM sales;
📌 Question 85: Find Products Purchased by Exactly One Customer
Table: orders (customer_id, product_id)
SELECT
product_id
FROM orders
GROUP BY product_id
HAVING COUNT(DISTINCT customer_id) = 1;
📌 Question 86: Find the Longest Inactive Period for Each Customer
Table: orders (customer_id, order_date)
WITH gaps AS (
SELECT
customer_id,
order_date,
order_date -
LAG(order_date) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS inactive_days
FROM orders
)
SELECT
customer_id,
MAX(inactive_days) AS longest_gap
FROM gaps
GROUP BY customer_id;
📌 Question 87: Calculate Revenue Share by Product Category
Tables: products (product_id, category) sales (product_id, amount)
SELECT
category,
SUM(amount) AS revenue,
ROUND(
100.0 * SUM(amount) /
SUM(SUM(amount)) OVER (),
2
) AS revenue_share
FROM sales s
JOIN products p
ON s.product_id = p.product_id
GROUP BY category
ORDER BY revenue DESC; | 619 |
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📢 Share with your friends and classmates. | 780 |
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| 6 | SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL:
1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.
2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.
3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.
4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
- Create (INSERT): Adds new records to a table.
- Read (SELECT): Retrieves data from one or more tables.
- Update (UPDATE): Modifies existing records in a table.
- Delete (DELETE): Removes records from a table.
5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.
6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
- Primary Key: Uniquely identifies each record in a table.
- Foreign Key: Establishes a relationship between two tables.
- Unique: Ensures that all values in a column are unique.
- Not Null: Specifies that a column cannot contain NULL values.
7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).
8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.
9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.
10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.
Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation.
SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075 | 1 120 |
| 7 |  💻 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗢𝗥 𝗙𝗥𝗘𝗘 | 𝟱 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗦𝗤𝗟 🚀
Want to become a Data Analyst, Data Scientist, or Software Engineer? Start by mastering SQL—one of the most in-demand skills in the tech industry!
These 5 FREE websites will help you learn SQL from scratch through interactive lessons, quizzes, and hands-on practice.
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🚀 Start Learning SQL Today and Build a Strong Foundation for Your Tech Career! | 1 108 |
| 8 |  🚀 𝗡𝗩𝗜𝗗𝗜𝗔 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 | 𝗟𝗲𝗮𝗿𝗻 𝗙𝗿𝗼𝗺 𝗔𝗜 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗟𝗲𝗮𝗱𝗲𝗿𝘀
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🚀 Start Learning Today. Earn Your Certificate. Build Your Future in AI! | 1 355 |
| 9 | 📌 Question 69: Find Orders Above Monthly Average
Table: orders (order_id, amount, order_date)
WITH monthly_avg AS (
SELECT
DATE_TRUNC('month', order_date) AS month,
AVG(amount) AS avg_amount
FROM orders
GROUP BY DATE_TRUNC('month', order_date)
)
SELECT
o.order_id,
o.amount,
o.order_date
FROM orders o
JOIN monthly_avg m
ON DATE_TRUNC('month', o.order_date) = m.month
WHERE o.amount > m.avg_amount;
📌 Question 70: Calculate Customer Repeat Rate by Month
Table: orders (customer_id, order_date)
WITH customer_orders AS (
SELECT
DATE_TRUNC('month', order_date) AS month,
customer_id,
COUNT() AS order_count
FROM orders
GROUP BY month, customer_id
)
SELECT
month,
ROUND(
100.0 *
COUNT(CASE WHEN order_count > 1 THEN 1 END)
/ COUNT(),
2
) AS repeat_rate
FROM customer_orders
GROUP BY month
ORDER BY month;
🎯 Concepts Covered:
✅ Window Functions
✅ Streak Analysis
✅ Customer Segmentation
✅ Weekly & Monthly KPIs
✅ Revenue Analytics
✅ Business Intelligence
✅ Advanced Aggregations
✅ Real Interview Scenarios
❤️ Double Tap For More | 1 107 |
| 10 | 🚀 SQL Scenario-Based Interview Questions with Answers: Part-7
📌 Question 61: Find the Longest Purchase Streak
Table: orders (customer_id, order_date)
Requirement: Find the longest consecutive daily purchase streak for each customer.
WITH purchase_days AS (
SELECT DISTINCT
customer_id,
order_date
FROM orders
),
streaks AS (
SELECT
customer_id,
order_date,
order_date -
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY order_date
) * INTERVAL '1 day' AS grp
FROM purchase_days
)
SELECT
customer_id,
COUNT(*) AS longest_streak
FROM streaks
GROUP BY customer_id, grp
ORDER BY longest_streak DESC;
📌 Question 62: Find Products Purchased by More Than 80% of Customers
Tables:
products (product_id)
orders (customer_id, product_id)
SELECT
product_id
FROM orders
GROUP BY product_id
HAVING COUNT(DISTINCT customer_id) >=
(
SELECT COUNT(DISTINCT customer_id) * 0.80
FROM orders
);
📌 Question 63: Find the Highest Revenue Day for Each Month
Table: sales (sale_date, amount)
WITH daily_sales AS (
SELECT
DATE(sale_date) AS sale_day,
SUM(amount) AS revenue
FROM sales
GROUP BY DATE(sale_date)
)
SELECT
month,
sale_day,
revenue
FROM (
SELECT
DATE_TRUNC('month', sale_day) AS month,
sale_day,
revenue,
DENSE_RANK() OVER (
PARTITION BY DATE_TRUNC('month', sale_day)
ORDER BY revenue DESC
) AS rnk
FROM daily_sales
) t
WHERE rnk = 1;
📌 Question 64: Calculate Customer Purchase Frequency
Table: orders (customer_id, order_date)
Requirement: Average number of days between consecutive orders.
WITH purchase_gap AS (
SELECT
customer_id,
order_date,
LAG(order_date) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS previous_order
FROM orders
)
SELECT
customer_id,
ROUND(
AVG(order_date - previous_order),
2
) AS avg_days_between_orders
FROM purchase_gap
WHERE previous_order IS NOT NULL
GROUP BY customer_id;
📌 Question 65: Find Customers Who Bought Only One Product Category
Tables:
orders (customer_id, product_id)
products (product_id, category)
SELECT
customer_id
FROM orders o
JOIN products p
ON o.product_id = p.product_id
GROUP BY customer_id
HAVING COUNT(DISTINCT category) = 1;
📌 Question 66: Calculate Revenue by Week
Table: sales (sale_date, amount)
SELECT
DATE_TRUNC('week', sale_date) AS week,
SUM(amount) AS revenue
FROM sales
GROUP BY DATE_TRUNC('week', sale_date)
ORDER BY week;
📌 Question 67: Find Customers with the Highest Average Order Value
Table: orders (customer_id, amount)
SELECT
customer_id,
ROUND(AVG(amount), 2) AS avg_order_value
FROM orders
GROUP BY customer_id
ORDER BY avg_order_value DESC
LIMIT 10;
📌 Question 68: Find the Month with the Highest Number of New Customers
Table: users (user_id, signup_date)
SELECT
DATE_TRUNC('month', signup_date) AS month,
COUNT(*) AS new_customers
FROM users
GROUP BY DATE_TRUNC('month', signup_date)
ORDER BY new_customers DESC
LIMIT 1; | 884 |
| 11 |  𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍
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| 12 | 🚀 SQL Business Scenario Interview Questions with Answers Part 6
📌 Question 51: Find Customers Who Haven't Ordered in the Last 90 Days
Tables: customers (customer_id), orders (customer_id, order_date)
SELECT c.customer_id
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id
GROUP BY c.customer_id
HAVING MAX(order_date) < CURRENT_DATE - INTERVAL '90 days'
OR MAX(order_date) IS NULL;
📌 Question 52: Calculate Average Revenue Per User ARPU
Tables: users (user_id), orders (user_id, amount)
SELECT
ROUND(
SUM(amount) * 1.0 /
COUNT(DISTINCT u.user_id),
2
) AS arpu
FROM users u
LEFT JOIN orders o
ON u.user_id = o.user_id;
📌 Question 53: Find the Fastest Selling Product
Table: inventory (product_id, launch_date)
Table: sales (product_id, sale_date)
Requirement: Find the product with the fewest days from launch to first sale.
WITH first_sale AS (
SELECT
product_id,
MIN(sale_date) AS first_sale_date
FROM sales
GROUP BY product_id
)
SELECT
i.product_id,
(first_sale_date - launch_date) AS days_to_sell
FROM inventory i
JOIN first_sale f
ON i.product_id = f.product_id
ORDER BY days_to_sell
LIMIT 1;
📌 Question 54: Find Customers Who Purchased in Every Quarter
Table: orders (customer_id, order_date)
WITH customer_quarters AS (
SELECT
customer_id,
COUNT(
DISTINCT DATE_TRUNC('quarter', order_date)
) AS quarter_count
FROM orders
GROUP BY customer_id
),
total_quarters AS (
SELECT COUNT(
DISTINCT DATE_TRUNC('quarter', order_date)
) AS total_quarters
FROM orders
)
SELECT customer_id
FROM customer_quarters
CROSS JOIN total_quarters
WHERE quarter_count = total_quarters;
📌 Question 55: Find Revenue Contribution of Top 10 Customers
Table: orders (customer_id, amount)
WITH customer_revenue AS (
SELECT
customer_id,
SUM(amount) AS revenue
FROM orders
GROUP BY customer_id
)
SELECT
SUM(revenue) AS top10_revenue,
ROUND(
100.0 * SUM(revenue) /
(
SELECT SUM(amount)
FROM orders
),
2
) AS contribution_pct
FROM (
SELECT revenue
FROM customer_revenue
ORDER BY revenue DESC
LIMIT 10
) t;
📌 Question 56: Find Products Never Returned
Tables:
products (product_id)
sales (order_id, product_id)
returns (order_id)
SELECT DISTINCT
p.product_id
FROM products p
LEFT JOIN sales s
ON p.product_id = s.product_id
LEFT JOIN returns r
ON s.order_id = r.order_id
WHERE r.order_id IS NULL;
📌 Question 57: Calculate Daily Revenue Growth
Table: sales (sale_date, amount)
WITH daily_sales AS (
SELECT
sale_date,
SUM(amount) AS revenue
FROM sales
GROUP BY sale_date
)
SELECT
sale_date,
revenue,
ROUND(
100.0 *
(
revenue -
LAG(revenue) OVER(
ORDER BY sale_date
)
/
LAG(revenue) OVER(
ORDER BY sale_date
),
2
) AS growth_pct
FROM daily_sales;
📌 Question 58: Find the Most Loyal Customers
Table: orders (customer_id, order_date)
Requirement: Customers who placed orders in the highest number of distinct months.
SELECT
customer_id,
COUNT(
DISTINCT DATE_TRUNC('month', order_date)
) AS active_months
FROM orders
GROUP BY customer_id
ORDER BY active_months DESC
LIMIT 10;
📌 Question 59: Find Products With Zero Sales
Tables:
products (product_id)
sales (product_id)
SELECT
p.product_id
FROM products p
LEFT JOIN sales s
ON p.product_id = s.product_id
WHERE s.product_id IS NULL;
📌 Question 60: Calculate Average Orders Per Customer
Table: orders (customer_id)
SELECT
ROUND(
COUNT(*) * 1.0 /
COUNT(DISTINCT customer_id),
2
) AS avg_orders_per_customer
FROM orders;
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| 13 |  📊 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 | 𝗡𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗡𝗲𝗲𝗱𝗲𝗱! 🚀
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| 14 | 🚀 SQL Scenario Based Interview Questions with Answers: Part-5
📌 Question 41: Find Customers Whose Spending Decreased for 3 Consecutive Months
Table: orders (customer_id, amount, order_date)
WITH monthly_spend AS (
SELECT
customer_id,
DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY customer_id, DATE_TRUNC('month', order_date)
),
spend_trend AS (
SELECT *,
LAG(revenue,1) OVER(PARTITION BY customer_id ORDER BY month) AS prev1,
LAG(revenue,2) OVER(PARTITION BY customer_id ORDER BY month) AS prev2
FROM monthly_spend
)
SELECT customer_id, month
FROM spend_trend
WHERE revenue < prev1
AND prev1 < prev2;
📌 Question 42: Find the Median Order Amount for Each Month
Table: orders (order_id, amount, order_date)
SELECT
DATE_TRUNC('month', order_date) AS month,
PERCENTILE_CONT(0.5)
WITHIN GROUP (ORDER BY amount) AS median_order
FROM orders
GROUP BY DATE_TRUNC('month', order_date);
📌 Question 43: Find Customers Who Ordered on Every Weekend
Table: orders (customer_id, order_date)
SELECT
customer_id
FROM orders
WHERE EXTRACT(DOW FROM order_date) IN (0,6)
GROUP BY customer_id
HAVING COUNT(DISTINCT order_date) >= 8;
📌 Question 44: Find the Top 5% Highest Revenue Customers
Table: orders (customer_id, amount)
WITH revenue AS (
SELECT
customer_id,
SUM(amount) AS total_revenue
FROM orders
GROUP BY customer_id
)
SELECT *
FROM (
SELECT *,
NTILE(20) OVER (ORDER BY total_revenue DESC) AS bucket
FROM revenue
) t
WHERE bucket = 1;
📌 Question 45: Find the Most Frequently Returned Product
Tables:
sales (order_id, product_id)
returns (order_id)
SELECT
s.product_id,
COUNT(*) AS return_count
FROM sales s
JOIN returns r
ON s.order_id = r.order_id
GROUP BY s.product_id
ORDER BY return_count DESC
LIMIT 1;
📌 Question 46: Calculate Average Delivery Time
Table: deliveries (order_id, order_date, delivery_date)
SELECT
ROUND(
AVG(delivery_date - order_date),
2
) AS avg_delivery_days
FROM deliveries;
📌 Question 47: Find Users Who Logged In Every Day Last Week
Table: logins (user_id, login_date)
SELECT
user_id
FROM logins
WHERE login_date >= CURRENT_DATE - INTERVAL '6 day'
GROUP BY user_id
HAVING COUNT(DISTINCT login_date) = 7;
📌 Question 48: Find Products with Revenue Above Category Average
Tables:
products (product_id, category)
sales (product_id, amount)
WITH product_revenue AS (
SELECT
p.product_id,
p.category,
SUM(s.amount) AS revenue
FROM products p
JOIN sales s
ON p.product_id = s.product_id
GROUP BY p.product_id, p.category
)
SELECT *
FROM (
SELECT *,
AVG(revenue) OVER (
PARTITION BY category
) AS category_avg
FROM product_revenue
) t
WHERE revenue > category_avg;
📌 Question 49: Find the Busiest Day of the Week
Table: orders (order_date)
SELECT
TO_CHAR(order_date, 'Day') AS weekday,
COUNT(*) AS total_orders
FROM orders
GROUP BY weekday
ORDER BY total_orders DESC
LIMIT 1;
📌 Question 50: Calculate Customer Retention After First Purchase
Table: orders (customer_id, order_date)
WITH customer_orders AS (
SELECT
customer_id,
COUNT() AS total_orders
FROM orders
GROUP BY customer_id
)
SELECT
ROUND(
100.0 *
COUNT(CASE WHEN total_orders > 1 THEN 1 END)
/ COUNT(),
2
) AS retention_rate
FROM customer_orders;
🎯 Concepts Covered:
✅ Window Functions
✅ Percentiles
✅ NTILE()
✅ Retention Analysis
✅ Revenue Analytics
✅ Delivery KPIs
✅ Customer Behavior Analysis
✅ Advanced Business SQL
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| 16 | SELECT
EXTRACT(HOUR FROM order_timestamp) AS order_hour,
COUNT(*) AS total_orders
FROM orders
GROUP BY order_hour
ORDER BY total_orders DESC
LIMIT 1;
📌 Question 40: Rank Salespersons by Quarterly Revenue
Table: sales salesperson_id, amount, sale_date
WITH quarterly_sales AS (
SELECT
salesperson_id,
DATE_TRUNC('quarter', sale_date) AS quarter,
SUM(amount) AS revenue
FROM sales
GROUP BY 1,2
)
SELECT *,
DENSE_RANK() OVER (
PARTITION BY quarter
ORDER BY revenue DESC
) AS sales_rank
FROM quarterly_sales;
Double Tap ❤️ For More | 1 583 |
| 17 | SELECT
EXTRACT(HOUR FROM order_timestamp) AS order_hour,
COUNT(*) AS total_orders
FROM orders
GROUP BY order_hour
ORDER BY total_orders DESC
LIMIT 1;
📌 Question 40: Rank Salespersons by Quarterly Revenue
Table: sales salesperson_id, amount, sale_date
WITH quarterly_sales AS (
SELECT
salesperson_id,
DATE_TRUNC('quarter', sale_date) AS quarter,
SUM(amount) AS revenue
FROM sales
GROUP BY 1,2
)
SELECT *,
DENSE_RANK() OVER (
PARTITION BY quarter
ORDER BY revenue DESC
) AS sales_rank
FROM quarterly_sales;
Double Tap ❤️ For More
-----
1.38 ₽ · /balance_help | 1 |
| 18 | 🚀 SQL Scenario Based Interview Questions with Answers Part 4
📌 Question 31: Find Customers Who Increased Their Monthly Spending
Table: orders customer_id, amount, order_date
Requirement: Return customers whose spending increased compared to the previous month.
WITH monthly_spend AS (
SELECT
customer_id,
DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS total_spend
FROM orders
GROUP BY customer_id, DATE_TRUNC('month', order_date)
)
SELECT
customer_id,
month,
total_spend
FROM (
SELECT *,
LAG(total_spend) OVER (
PARTITION BY customer_id
ORDER BY month
) AS prev_spend
FROM monthly_spend
) t
WHERE total_spend > prev_spend;
📌 Question 32: Find Products Purchased Together Most Often
Table: order_items order_id, product_id
SELECT
a.product_id AS product_1,
b.product_id AS product_2,
COUNT(*) AS frequency
FROM order_items a
JOIN order_items b
ON a.order_id = b.order_id
AND a.product_id < b.product_id
GROUP BY 1,2
ORDER BY frequency DESC
LIMIT 10;
📌 Question 33: Find Users Active for 7 Consecutive Days
Table: user_activity user_id, activity_date
WITH activity AS (
SELECT
user_id,
activity_date,
activity_date -
ROW_NUMBER() OVER (
PARTITION BY user_id
ORDER BY activity_date
) * INTERVAL '1 day' AS grp
FROM user_activity
)
SELECT
user_id
FROM activity
GROUP BY user_id, grp
HAVING COUNT(*) >= 7;
📌 Question 34: Calculate Repeat Purchase Rate
Table: orders customer_id, order_id
SELECT
ROUND(
100.0 *
COUNT(CASE WHEN order_count > 1 THEN 1 END) /
COUNT(*),
2
) AS repeat_purchase_rate
FROM (
SELECT
customer_id,
COUNT(*) AS order_count
FROM orders
GROUP BY customer_id
) t;
📌 Question 35: Find the Longest Gap Between Two Orders
Table: orders customer_id, order_date
WITH gaps AS (
SELECT
customer_id,
order_date,
order_date -
LAG(order_date) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS gap_days
FROM orders
)
SELECT
customer_id,
MAX(gap_days) AS longest_gap
FROM gaps
GROUP BY customer_id;
📌 Question 36: Calculate Revenue Lost Due to Churn
Tables: customers customer_id, status, orders customer_id, amount
SELECT
SUM(amount) AS churned_revenue
FROM customers c
JOIN orders o
ON c.customer_id = o.customer_id
WHERE c.status = 'Churned';
📌 Question 37: Find the Fastest Growing Product
Table: sales product_id, amount, sale_date
WITH monthly_sales AS (
SELECT
product_id,
DATE_TRUNC('month', sale_date) AS month,
SUM(amount) AS revenue
FROM sales
GROUP BY 1,2
)
SELECT *
FROM (
SELECT *,
revenue -
LAG(revenue) OVER (
PARTITION BY product_id
ORDER BY month
) AS growth
FROM monthly_sales
) t
ORDER BY growth DESC
LIMIT 1;
📌 Question 38: Find Customers Who Purchased Every Product Category
Tables: products product_id, category, orders customer_id, product_id
SELECT
customer_id
FROM orders o
JOIN products p
ON o.product_id = p.product_id
GROUP BY customer_id
HAVING COUNT(DISTINCT category) = (
SELECT COUNT(DISTINCT category)
FROM products
);
📌 Question 39: Find Peak Ordering Hour
Table: orders order_id, order_timestamp | 1 383 |
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Share with yours friends who wants to start a career in Data Analytics | 1 323 |
| 20 | 🚀 SQL Scenario Based Interview Questions with Answers: Part-3
📌 Question 21: Find the First Purchase Date for Every Customer
Table: orders order_id, customer_id, order_date
SELECT
customer_id,
MIN(order_date) AS first_purchase_date
FROM orders
GROUP BY customer_id;
📌 Question 22: Calculate Customer Lifetime Value (CLV)
Table: orders customer_id, amount
Requirement: Total revenue generated by each customer.
SELECT
customer_id,
SUM(amount) AS lifetime_value
FROM orders
GROUP BY customer_id
ORDER BY lifetime_value DESC;
📌 Question 23: Find the Top 5 Products Contributing 80% of Revenue
Table: sales product_id, amount
WITH product_revenue AS (
SELECT
product_id,
SUM(amount) AS revenue
FROM sales
GROUP BY product_id
),
revenue_rank AS (
SELECT
product_id,
revenue,
SUM(revenue) OVER (ORDER BY revenue DESC) AS running_revenue,
SUM(revenue) OVER () AS total_revenue
FROM product_revenue
)
SELECT
product_id,
revenue,
ROUND(100.0 * running_revenue / total_revenue, 2) AS cumulative_pct
FROM revenue_rank
WHERE running_revenue <= total_revenue * 0.80;
📌 Question 24: Find Customers Who Purchased More Than 3 Different Products
Table: orders customer_id, product_id
SELECT
customer_id
FROM orders
GROUP BY customer_id
HAVING COUNT(DISTINCT product_id) > 3;
📌 Question 25: Find the Highest Revenue Order for Each Customer
Table: orders order_id, customer_id, amount
WITH ranked_orders AS (
SELECT *,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY amount DESC
) AS rn
FROM orders
)
SELECT
customer_id,
order_id,
amount
FROM ranked_orders
WHERE rn = 1;
📌 Question 26: Calculate Average Time Between Orders
Table: orders customer_id, order_date
WITH order_gap AS (
SELECT
customer_id,
order_date,
LAG(order_date) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS previous_order
FROM orders
)
SELECT
customer_id,
AVG(order_date - previous_order) AS avg_days_between_orders
FROM order_gap
WHERE previous_order IS NOT NULL
GROUP BY customer_id;
📌 Question 27: Find Users Who Abandoned Their Cart
Tables: cart user_id, product_id, orders user_id
Requirement: Users who added items to their cart but never completed a purchase.
SELECT DISTINCT
c.user_id
FROM cart c
LEFT JOIN orders o
ON c.user_id = o.user_id
WHERE o.user_id IS NULL;
📌 Question 28: Find Revenue Generated by New vs Returning Customers
Tables: users user_id, signup_date, orders user_id, amount, order_date
SELECT
CASE
WHEN DATE_TRUNC('month', signup_date) =
DATE_TRUNC('month', order_date)
THEN 'New'
ELSE 'Returning'
END AS customer_type,
SUM(amount) AS revenue
FROM users u
JOIN orders o
ON u.user_id = o.user_id
GROUP BY customer_type;
📌 Question 29: Find the Most Frequently Purchased Product Pair
Table: order_items order_id, product_id
SELECT
a.product_id AS product_1,
b.product_id AS product_2,
COUNT(*) AS pair_count
FROM order_items a
JOIN order_items b
ON a.order_id = b.order_id
AND a.product_id < b.product_id
GROUP BY
a.product_id,
b.product_id
ORDER BY pair_count DESC
LIMIT 1;
📌 Question 30: Calculate Revenue Contribution by Region
Tables: customers customer_id, region, orders customer_id, amount
SELECT
c.region,
SUM(o.amount) AS revenue,
ROUND(
100.0 * SUM(o.amount) /
SUM(SUM(o.amount)) OVER (),
2
) AS revenue_share
FROM customers c
JOIN orders o
ON c.customer_id = o.customer_id
GROUP BY c.region
ORDER BY revenue DESC;
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