✅ SQL CASE Statement 🎯

The CASE statement lets you apply conditional logic inside SQL queries — like if/else in programming.

1️⃣ Basic CASE Syntax

SELECT name, salary,
  CASE  
    WHEN salary > 80000 THEN 'High'  
    WHEN salary BETWEEN 50000 AND 80000 THEN 'Medium'  
    ELSE 'Low'  
  END AS salary_level  
FROM employees;

✅ Categorizes salaries as High, Medium, or Low.

2️⃣ CASE in ORDER BY
Sort based on custom logic.

SELECT name, department  
FROM employees  
ORDER BY  
  CASE department  
    WHEN 'HR' THEN 1  
    WHEN 'Engineering' THEN 2  
    ELSE 3  
  END;

✅ HR shows up first, then Engineering, then others.

3️⃣ CASE in WHERE Clause
Control filtering logic conditionally.

SELECT *  
FROM orders  
WHERE status =  
  CASE  
    WHEN customer_type = 'VIP' THEN 'priority'  
    ELSE 'standard'  
  END;

4️⃣ Nested CASE (Advanced)

SELECT name, marks,  
  CASE  
    WHEN marks >= 90 THEN 'A'  
    WHEN marks >= 75 THEN  
      CASE WHEN marks >= 85 THEN 'B+' ELSE 'B' END  
    ELSE 'C'  
  END AS grade  
FROM students;

🎯 Use CASE When You Want To:
• Create labels or buckets
• Replace multiple IF conditions
• Make results more readable

📝 Practice Tasks:
1. Add a column that shows ‘Pass’ or ‘Fail’ based on marks
2. Create a salary band (Low/Medium/High) using CASE
3. Use CASE to sort products as 'Electronics' first, then 'Clothing'

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Date :- 11th January 2026

✅ SQL Programming: Handling NULL Values 🛠️

Missing data is common in databases. COALESCE() helps you fill in defaults and avoid null-related issues.

1️⃣ What is COALESCE?
Returns the first non-null value in a list.

SELECT name, COALESCE(phone, 'Not Provided') AS contact  
FROM customers;

✅ If phone is NULL, it shows ‘Not Provided’.

2️⃣ COALESCE with Calculations
Prevent nulls from breaking math.

SELECT name, salary, COALESCE(bonus, 0) AS bonus,  
       salary + COALESCE(bonus, 0) AS total_income  
FROM employees;

✅ If bonus is NULL, treat it as 0 to compute total.

3️⃣ Nested COALESCE
Use multiple fallback options.

SELECT name, COALESCE(email, alt_email, 'No Email') AS contact_email  
FROM users;

✅ Checks email, then alt_email, then default text.

4️⃣ COALESCE in WHERE clause
Filter even when data has nulls.

SELECT *  
FROM products  
WHERE COALESCE(category, 'Uncategorized') = 'Electronics';

🎯 Use COALESCE When You Want To:
• Replace NULLs with defaults
• Keep math & filters working
• Avoid errors in reports or dashboards

📝 Practice Tasks:
1. Replace nulls in city with ‘Unknown’
2. Show total amount = price + tax (tax may be null)
3. Replace nulls in denoscription with ‘No Info Available’

✅ Solution for Practice Tasks 👇

1️⃣ Replace NULLs in city with 'Unknown'

SELECT name, COALESCE(city, 'Unknown') AS city  
FROM customers;

2️⃣ Show total amount = price + tax (tax may be NULL)

SELECT product_name, price, COALESCE(tax, 0) AS tax,  
       price + COALESCE(tax, 0) AS total_amount  
FROM products;

3️⃣ Replace NULLs in denoscription with 'No Info Available'

SELECT product_name, COALESCE(denoscription, 'No Info Available') AS denoscription  
FROM products;

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✅ SQL Window Functions 🧠🪟

Window functions perform calculations across rows that are related to the current row — without collapsing the result like GROUP BY.

1️⃣ ROW_NUMBER() – Assigns a unique row number per partition

SELECT name, department,
  ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;

➤ Gives ranking within each department

2️⃣ RANK() & DENSE_RANK() – Ranking with gaps (RANK) or without gaps (DENSE_RANK)

SELECT name, salary,
  RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;

3️⃣ LAG() & LEAD() – Access previous or next row value

SELECT name, salary,
  LAG(salary) OVER (ORDER BY salary) AS prev_salary,
  LEAD(salary) OVER (ORDER BY salary) AS next_salary
FROM employees;

➤ Compare salary trends row-wise

4️⃣ SUM(), AVG(), COUNT() OVER() – Running totals, moving averages, etc.

SELECT department, salary,
  SUM(salary) OVER (PARTITION BY department) AS dept_total
FROM employees;

5️⃣ NTILE(n) – Divides rows into n equal buckets

SELECT name, salary,
  NTILE(4) OVER (ORDER BY salary DESC) AS quartile
FROM employees;

💡 Why Use Window Functions:
• Perform row-wise calculations
• Avoid GROUP BY limitations
• Enable advanced analytics (ranking, trends, etc.)

🧪 Practice Task:
Write a query to find the top 2 earners in each department using ROW_NUMBER().

💬 Tap ❤️ for more!

✅ SQL Real-World Use Cases 💼🧠

SQL is the backbone of data analysis and automation in many domains. Here’s how it powers real work:

1️⃣ Sales & CRM
Use Case: Sales Tracking & Pipeline Management
• Track sales per region, product, rep
• Identify top-performing leads
• Calculate conversion rates
SQL Task:

SELECT region, SUM(sales_amount)  
FROM deals  
GROUP BY region;

2️⃣ Finance
Use Case: Monthly Revenue and Expense Reporting
• Aggregate revenue by month
• Analyze profit margins
• Flag unusual transactions
SQL Task:

SELECT MONTH(date), SUM(revenue - expense) AS profit  
FROM finance_data  
GROUP BY MONTH(date);

3️⃣ HR Analytics
Use Case: Employee Attrition Analysis
• Track tenure, exits, departments
• Calculate average retention
• Segment by age, role, or location
SQL Task:

SELECT department, COUNT(*)  
FROM employees  
WHERE exit_date IS NOT NULL  
GROUP BY department;

4️⃣ E-commerce
Use Case: Customer Order Behavior
• Find most ordered products
• Time between repeat orders
• Cart abandonment patterns
SQL Task:

SELECT customer_id, COUNT(order_id)  
FROM orders  
GROUP BY customer_id  
HAVING COUNT(order_id) > 5;

5️⃣ Healthcare
Use Case: Patient Visit Frequency
• Find frequent visitors
• Analyze doctor performance
• Calculate average stay duration
SQL Task:

SELECT patient_id, COUNT(*) AS visits  
FROM appointments  
GROUP BY patient_id;

6️⃣ Marketing
Use Case: Campaign Performance by Channel
• Track leads, clicks, conversions
• Compare cost-per-lead by platform
SQL Task:

SELECT channel, SUM(conversions)/SUM(clicks) AS conv_rate  
FROM campaign_data  
GROUP BY channel;

🧪 Practice Task:
Pick a dataset (orders, users, sales)
→ Write 3 queries: summary, trend, filter
→ Visualize the output in Excel or Power BI

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✅ Useful Platform to Practice SQL Programming 🧠🖥️

Learning SQL is just the first step — practice is what builds real skill. Here are the best platforms for hands-on SQL:

1️⃣ LeetCode – For Interview-Oriented SQL Practice
• Focus: Real interview-style problems
• Levels: Easy to Hard
• Schema + Sample Data Provided
• Great for: Data Analyst, Data Engineer, FAANG roles
✔ Tip: Start with Easy → filter by “Database” tag
✔ Popular Section: Database → Top 50 SQL Questions
Example Problem: “Find duplicate emails in a user table” → Practice filtering, GROUP BY, HAVING

2️⃣ HackerRank – Structured & Beginner-Friendly
• Focus: Step-by-step SQL track
• Has certification tests (SQL Basic, Intermediate)
• Problem sets by topic: SELECT, JOINs, Aggregations, etc.
✔ Tip: Follow the full SQL track
✔ Bonus: Company-specific challenges
Try: “Revising Aggregations – The Count Function” → Build confidence with small wins

3️⃣ Mode Analytics – Real-World SQL in Business Context
• Focus: Business intelligence + SQL
• Uses real-world datasets (e.g., e-commerce, finance)
• Has an in-browser SQL editor with live data
✔ Best for: Practicing dashboard-level queries
✔ Tip: Try the SQL case studies & tutorials

4️⃣ StrataScratch – Interview Questions from Real Companies
• 500+ problems from companies like Uber, Netflix, Google
• Split by company, difficulty, and topic
✔ Best for: Intermediate to advanced level
✔ Tip: Try “Hard” questions after doing 30–50 easy/medium

5️⃣ DataLemur – Short, Practical SQL Problems
• Crisp and to the point
• Good UI, fast learning
• Real interview-style logic
✔ Use when: You want fast, smart SQL drills

📌 How to Practice Effectively:
• Spend 20–30 mins/day
• Focus on JOINs, GROUP BY, HAVING, Subqueries
• Analyze problem → write → debug → re-write
• After solving, explain your logic out loud

🧪 Practice Task:
Try solving 5 SQL questions from LeetCode or HackerRank this week. Start with SELECT, WHERE, and GROUP BY.

💬 Tap ❤️ for more!

✅ Data Analytics Roadmap for Freshers in 2025 🚀📊

1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.

2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.

3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.

4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)

5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.

6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)

7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.

8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns

9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics

🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst

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1️⃣ Pick Solid Datasets

• Public: Kaggle, UCI ML Repo, data.gov
• Business-like: e-commerce, churn, marketing spend, HR attrition
• Size: 5k–200k rows, relatively clean

2️⃣ Create 3 Signature Projects

• SQL: Customer Cohort & Retention (joins, window functions)
• BI: Executive Sales Dashboard (Power BI/Tableau, drill-through, DAX/calculated fields)
• Python: Marketing ROI & Attribution (pandas, seaborn, A/B test basics)

3️⃣ Tell a Story, Not Just Charts

• Problem → Approach → Insight → Action
• Add one business recommendation per insight

4️⃣ Document Like a Pro

• README: problem, data source, methods, results, next steps
• Screenshots or GIFs of dashboards
• Repo structure: /data, /notebooks, /sql, /reports

5️⃣ Show Measurable Impact

• “Reduced reporting time by 70% with automated Power BI pipeline”
• “Identified 12% churn segment with a retention playbook”

6️⃣ Make It Easy to Review

• Share live dashboards (Publish to Web), short Loom/YouTube walkthrough
• Include SQL snippets
• Pin top 3 projects on GitHub and LinkedIn Featured

7️⃣ Iterate With Feedback

• Post drafts on LinkedIn, ask “What would you improve?”
• Apply suggestions, track updates in a CHANGELOG
🎯 Goal: 3 projects, 3 stories, 3 measurable outcomes.

💬 Double Tap ❤️ For More!

Core SQL Interview Questions. With answers

1 What is SQL
• SQL stands for Structured Query Language
• You use it to read and manage data in relational databases
• Used in MySQL, PostgreSQL, SQL Server, Oracle

2 What is an RDBMS
• Relational Database Management System
• Stores data in tables with rows and columns
• Uses keys to link tables
• Example. Customer table linked to Orders table using customer_id

3 What is a table
• Structured storage for data
• Rows are records
• Columns are attributes
• Example. One row equals one customer

4 What is a primary key
• Uniquely identifies each row
• Cannot be NULL
• No duplicate values
• Example. user_id in users table

5 What is a foreign key
• Links one table to another
• Refers to a primary key in another table
• Allows duplicate values
• Example. user_id in orders table

6 Difference between primary key and foreign key
• Primary key ensures uniqueness
• Foreign key ensures relationship
• One table can have one primary key
• One table can have multiple foreign keys

7 What is NULL
• Represents missing or unknown value
• Not equal to zero or empty string
• Use IS NULL or IS NOT NULL to check

8 What are constraints
• Rules applied on columns
• Maintain data quality
• Common constraints
– NOT NULL
– UNIQUE
– PRIMARY KEY
– FOREIGN KEY
– CHECK

9 What are data types
• Define type of data stored
• Common types
– INT for numbers
– VARCHAR for text
– DATE for dates
– FLOAT or DECIMAL for decimals

10 Interview tip you must remember
• Always explain with a small example
• Speak logic before syntax
• Keep answers short and direct

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When preparing for an SQL project-based interview, the focus typically shifts from theoretical knowledge to practical application. Here are some SQL project-based interview questions that could help assess your problem-solving skills and experience:

1. Database Design and Schema
- Question: Describe a database schema you have designed in a past project. What were the key entities, and how did you establish relationships between them?
- Follow-Up: How did you handle normalization? Did you denormalize any tables for performance reasons?

2. Data Modeling
- Question: How would you model a database for an e-commerce application? What tables would you include, and how would they relate to each other?
- Follow-Up: How would you design the schema to handle scenarios like discount codes, product reviews, and inventory management?

3. Query Optimization
- Question: Can you discuss a time when you optimized an SQL query? What was the original query, and what changes did you make to improve its performance?
- Follow-Up: What tools or techniques did you use to identify and resolve the performance issues?

4. ETL Processes
- Question: Describe an ETL (Extract, Transform, Load) process you have implemented. How did you handle data extraction, transformation, and loading?
- Follow-Up: How did you ensure data quality and consistency during the ETL process?

5. Handling Large Datasets
- Question: In a project where you dealt with large datasets, how did you manage performance and storage issues?
- Follow-Up: What indexing strategies or partitioning techniques did you use?

6. Joins and Subqueries
- Question: Provide an example of a complex query you wrote involving multiple joins and subqueries. What was the business problem you were solving?
- Follow-Up: How did you ensure that the query performed efficiently?

7. Stored Procedures and Functions
- Question: Have you created stored procedures or functions in any of your projects? Can you describe one and explain why you chose to encapsulate the logic in a stored procedure?
- Follow-Up: How did you handle error handling and logging within the stored procedure?

8. Data Integrity and Constraints
- Question: How did you enforce data integrity in your SQL projects? Can you give examples of constraints (e.g., primary keys, foreign keys, unique constraints) you implemented?
- Follow-Up: How did you handle situations where constraints needed to be temporarily disabled or modified?

9. Version Control and Collaboration
- Question: How did you manage database version control in your projects? What tools or practices did you use to ensure collaboration with other developers?
- Follow-Up: How did you handle conflicts or issues arising from multiple developers working on the same database?

10. Data Migration
- Question: Describe a data migration project you worked on. How did you ensure that the migration was successful, and what steps did you take to handle data inconsistencies or errors?
- Follow-Up: How did you test the migration process before moving to the production environment?

11. Security and Permissions
- Question: In your SQL projects, how did you manage database security?
- Follow-Up: How did you handle encryption or sensitive data within the database?

12. Handling Unstructured Data
- Question: Have you worked with unstructured or semi-structured data in an SQL environment?
- Follow-Up: What challenges did you face, and how did you overcome them?

13. Real-Time Data Processing
   - Question: Can you describe a project where you handled real-time data processing using SQL? What were the key challenges, and how did you address them?
   - Follow-Up: How did you ensure the performance and reliability of the real-time data processing system?

Be prepared to discuss specific examples from your past work and explain your thought process in detail.

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✅ Basic SQL Queries Interview Questions With Answers 🖥️

1. What does SELECT do
• SELECT fetches data from a table
• You choose columns you want to see
Example: SELECT name, salary FROM employees;

2. What does FROM do
• FROM tells SQL where data lives
• It specifies the table name
Example: SELECT * FROM customers;

3. What is WHERE clause
• WHERE filters rows
• It runs before aggregation
Example: SELECT * FROM orders WHERE status = 'Delivered';

4. Difference between WHERE and HAVING
• WHERE filters rows before GROUP BY
• HAVING filters groups after aggregation
Example: WHERE filters orders, HAVING filters total_sales

5. How do you sort data
• Use ORDER BY
• Default order is ASC
Example: SELECT * FROM employees ORDER BY salary DESC;

6. How do you sort by multiple columns
• SQL sorts left to right
Example: SELECT * FROM students ORDER BY class ASC, marks DESC;

7. What is LIMIT
• LIMIT restricts number of rows returned
• Useful for top N queries
Example: SELECT * FROM products LIMIT 5;

8. What is OFFSET
• OFFSET skips rows
• Used with LIMIT for pagination
Example: SELECT * FROM products LIMIT 5 OFFSET 10;

9. How do you filter on multiple conditions
• Use AND, OR
Example: SELECT * FROM users WHERE city = 'Delhi' AND age > 25;

10. Difference between AND and OR
• AND needs all conditions true
• OR needs one condition true

Quick interview advice
• Always say execution order: FROM → WHERE → SELECT → ORDER BY → LIMIT
• Write clean examples
• Speak logic first, syntax next¹

Double Tap ❤️ For More

✅ SQL Joins Interview Questions With Answers 🖥️

1. What is a JOIN in SQL. Explain with an example. 
• JOIN combines data from multiple tables
• Tables connect using a common column
• Usually primary key to foreign key
Example tables 
Customers 
customer_id, name 
Orders 
order_id, customer_id, amount 
Query 
SELECT c.name, o.amount 
FROM customers c 
INNER JOIN orders o 
ON c.customer_id = o.customer_id; 
Explanation 
• SQL matches customer_id in both tables
• Output shows only related customer order data

2. What is INNER JOIN. When do you use it. 
• INNER JOIN returns only matching rows
• Rows without match are removed
Example 
Find customers who placed orders 
Query 
SELECT c.customer_id, c.name 
FROM customers c 
INNER JOIN orders o 
ON c.customer_id = o.customer_id; 
Logic 
• Customers without orders are excluded
• Only matched records appear

3. What is LEFT JOIN. Explain with use case. 
• LEFT JOIN returns all rows from left table
• Matching rows from right table
• Non matches show NULL
Example 
Find all customers and their orders 
Query 
SELECT c.name, o.order_id 
FROM customers c 
LEFT JOIN orders o 
ON c.customer_id = o.customer_id; 
Logic 
• Customers without orders still appear
• order_id becomes NULL

4. Difference between INNER JOIN and LEFT JOIN. 
• INNER JOIN removes non matching rows
• LEFT JOIN keeps all left table rows
• LEFT JOIN shows NULL for missing matches
Interview tip 
Explain using one missing record example 

5. What is RIGHT JOIN. 
• Returns all rows from right table
• Matching rows from left table
• Rarely used in real projects
Example 
SELECT c.name, o.order_id 
FROM customers c 
RIGHT JOIN orders o 
ON c.customer_id = o.customer_id; 

6. What is FULL OUTER JOIN. 
• Returns all rows from both tables
• Matches where possible
• Non matches show NULL
Example 
SELECT c.name, o.order_id 
FROM customers c 
FULL OUTER JOIN orders o 
ON c.customer_id = o.customer_id; 
Use case 
• Data reconciliation
• Comparing two datasets

7. How do you find records present in one table but not in another. 
Find customers with no orders 
Query 
SELECT c.customer_id, c.name 
FROM customers c 
LEFT JOIN orders o 
ON c.customer_id = o.customer_id 
WHERE o.order_id IS NULL; 
Logic 
• LEFT JOIN keeps all customers
• WHERE filters non matched rows

8. Explain JOIN with WHERE clause. Common mistake. 
• WHERE runs after JOIN
• Wrong WHERE condition breaks LEFT JOIN
Wrong 
LEFT JOIN orders 
WHERE orders.amount > 1000 
Correct 
LEFT JOIN orders 
ON (link unavailable) = (link unavailable) 
AND orders.amount > 1000 

9. How do you join more than two tables. 
• JOIN step by step
• Each JOIN needs condition
Example 
SELECT c.name, o.order_id, p.product_name 
FROM customers c 
JOIN orders o 
ON c.customer_id = o.customer_id 
JOIN products p 
ON o.product_id = p.product_id; 

10. SQL execution order for JOIN queries. 
• FROM
• JOIN
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY

Interview advice 
• Always explain logic first
• Draw table flow in words
• Then write query

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✅ SQL GROUP BY and AGGREGATION Interview Questions 🎓

1. What is GROUP BY in SQL.
• GROUP BY groups rows with same values
• Used with aggregate functions
• One row per group in output

Example

Find total salary per department

FROM employees
GROUP BY department;

Logic
• Rows grouped by department
• SUM runs on each group

2. Why do we use aggregate functions.
• To summarize data
• To calculate totals, averages, counts

Common functions
• COUNT
• SUM
• AVG
• MIN
• MAX

3. What happens if you use GROUP BY without aggregation.
• Output shows unique combinations of grouped columns

Example

FROM employees
GROUP BY department;

Logic
• Acts like DISTINCT

4. Difference between WHERE and HAVING.
• WHERE filters rows
• HAVING filters groups
• WHERE runs before GROUP BY
• HAVING runs after GROUP BY

Example
Find departments with total salary above 5,00,000

FROM employees
GROUP BY department
HAVING SUM(salary) > 500000;

5. Can you use WHERE with GROUP BY.
• Yes
• WHERE filters raw data before grouping

Example

Ignore inactive employees

FROM employees
WHERE status = 'Active'
GROUP BY department;

6. Common GROUP BY interview error.

Why does this query fail

FROM employees
GROUP BY department;

Answer
• Non aggregated column must be in GROUP BY
• name is missing

Correct query

FROM employees
GROUP BY department;

7. What's the difference between COUNT(*) COUNT(column)?
• COUNT(*) counts all rows
• COUNT(column) skips NULL values

Example

SELECT COUNT(delivery_date) FROM orders;

8. Find total orders per customer.

FROM orders
GROUP BY customer_id;

Logic
• One row per customer
• COUNT runs per customer group

9. Find customers with more than 5 orders.

FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 5;

Logic
• GROUP first
• Filter groups using HAVING

10. Execution order for GROUP BY queries.
• FROM
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY

Interview advice
• Say execution order clearly
• Explain using one simple example
• Avoid mixing WHERE and HAVING logic

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✅ SQL Window Functions Interview Questions with Answers ✍️

1. What are window functions in SQL?
• Window functions perform calculations across related rows
• They do not reduce rows
• Each row keeps its detail
Key syntax: OVER (PARTITION BY, ORDER BY)

2. Difference between GROUP BY and window functions
• GROUP BY collapses rows
• Window functions keep all rows
• Window functions add calculated columns

3. What is ROW_NUMBER?
• Assigns unique sequential number
• No ties allowed

Example: Rank employees by salary

SELECT name, salary, ROW_NUMBER() OVER (ORDER BY salary DESC) AS rn
FROM employees;

4. Difference between ROW_NUMBER, RANK, and DENSE_RANK
• ROW_NUMBER gives unique numbers
• RANK skips numbers on ties
• DENSE_RANK does not skip

Example salaries: 100, 100, 90

ROW_NUMBER → 1, 2, 3
RANK → 1, 1, 3
DENSE_RANK → 1, 1, 2

5. What is PARTITION BY?
• PARTITION BY splits data into groups
• Window function runs inside each group

Example: Rank employees per department

SELECT department, name, salary,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dept_rank
FROM employees;

6. Find top 2 salaries per department

SELECT * FROM (
SELECT department, name, salary,
DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rnk
FROM employees
) t
WHERE rnk <= 2;

7. What is LAG?
• Accesses previous row value
• Used for comparisons

Example: Day over day sales

SELECT date, sales, LAG(sales) OVER (ORDER BY date) AS prev_day_sales
FROM daily_sales;

8. What is LEAD?
• Accesses next row value

Example: Compare today with next day

SELECT date, sales, LEAD(sales) OVER (ORDER BY date) AS next_day_sales
FROM daily_sales;

9. Calculate day over day growth
SELECT date, sales - LAG(sales) OVER (ORDER BY date) AS growth
FROM daily_sales;

10. Common window function interview mistakes
• Forgetting ORDER BY inside OVER
• Using WHERE instead of subquery to filter ranks
• Mixing GROUP BY with window logic incorrectly

Execution order: FROM → WHERE → GROUP BY → HAVING → SELECT → WINDOW → ORDER BY

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