SQL TRAP: PARTITION BY vs GROUP BY

SQL doesn’t have a fixed number of “queries” or “joins”—it’s a language, not a limited set of commands. But you can group things in a way that’s easy to understand for interviews and real work. 🔹 1. Types of SQL Queries 🟢 Main Categories (5 types) 1. DQL (Data Query Language)** * Used to fetch data * Example: ```sql SELECT * FROM employees; ``` 2. DML (Data Manipulation Language)** * Used to modify data * Commands: ```sql INSERT INTO employees VALUES (1, 'John'); UPDATE employees SET name='Sam' WHERE id=1; DELETE FROM employees WHERE id=1; ```3. DDL (Data Definition Language)** * Used to define structure ```sql CREATE TABLE employees (id INT, name VARCHAR(50)); ALTER TABLE employees ADD salary INT; DROP TABLE employees; ```4. DCL (Data Control Language)** * Permissions ```sql GRANT SELECT ON employees TO user1; REVOKE SELECT ON employees FROM user1; ``` **5. TCL (Transaction Control Language)** * Manage transactions ```sql COMMIT; ROLLBACK; SAVEPOINT sp1; 👉 So broadly, **5 types of SQL queries** 🔹 2. Types of SQL Joins 🟢 Main Joins (5 types) 1. INNER JOIN 👉 Returns only matching records from both tables ```sql SELECT e.name, d.dept_name FROM employees e INNER JOIN departments d ON e.dept_id = d.id; ``` 2. LEFT JOIN (LEFT OUTER JOIN) 👉 Returns all records from left table + matched from right ```sql SELECT e.name, d.dept_name FROM employees e LEFT JOIN departments d ON e.dept_id = d.id; ``` 3. RIGHT JOIN (RIGHT OUTER JOIN) 👉 Returns all records from right table + matched from left ```sql SELECT e.name, d.dept_name FROM employees e RIGHT JOIN departments d ON e.dept_id = d.id; ``` 4. FULL JOIN (FULL OUTER JOIN) 👉 Returns all records from both tables ```sql SELECT e.name, d.dept_name FROM employees e FULL JOIN departments d ON e.dept_id = d.id; ``` 5. CROSS JOIN 👉 Returns all combinations (Cartesian product) ```sql SELECT e.name, d.dept_name FROM employees e CROSS JOIN departments d; `` 🔹 3. Visual Understanding | Join Type | Result | | ---------- | ------------------------- | | INNER JOIN | Matching data only | | LEFT JOIN | All left + matching right | | RIGHT JOIN | All right + matching left | | FULL JOIN | Everything | | CROSS JOIN | All combinations | 🔹 4. Real-Time Example (Interview Style) Tables: Employees | id | name | dept_id | | -- | ---- | ------- | **Departments** | id | dept_name | Scenario: 👉 “Show all employees even if department is missing” ✔ Use: ```sql LEFT JOIN 👉 “Show only employees with valid departments” ✔ Use: ```sql INNER JOIN ``` 🔹 5. Key Tips (Very Important) * Always use **JOIN with ON condition** * Use aliases (`e`, `d`) for readability * Avoid **CROSS JOIN** in real projects (huge data) * LEFT JOIN is most used in real-time projects #SQL #StructuredQueryLanguage #DataAnalytics #DataScience #Database #TechLearning #Programming #Coding #LearnSQL #ITJobs

𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝘆 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗢𝗿𝗱𝗲𝗿 In SQL, queries are executed in a specific order, which can be quite different from the order in which the clauses are written. Here’s the logical order of SQL query execution: 1. FROM Specifies the tables from which to retrieve or manipulate data. 2. WHERE Filters rows based on specified conditions. Only rows that meet the conditions proceed to the next stage. 3. GROUP BY Groups rows into sets based on column(s) specified. Any aggregate functions (like SUM, COUNT, etc.) will now apply to each group. 4. HAVING Applies filters to groups created by GROUP BY. Only groups meeting these conditions move forward. 5. SELECT Determines which columns and expressions to return. Executes any functions or expressions listed in the SELECT clause. Deduplication of rows (DISTINCT) happens here if specified. 6. ORDER BY Sorts the result based on specified column(s) and sort direction (ASC or DESC). Does not impact the final rows selected, only the display order. 7. LIMIT  Restricts the number of rows returned by the query. Useful for pagination or getting a specific subset of rows. 𝗟𝗲𝘁𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝘁𝗵𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝘆  SELECT department, COUNT(employee_id) AS total_employees FROM employees WHERE status = 'active' GROUP BY department HAVING total_employees > 5 ORDER BY total_employees DESC LIMIT 10; 𝗧𝗵𝗶𝘀 𝗾𝘂𝗲𝗿𝘆 𝘄𝗼𝘂𝗹𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗼𝗿𝗱𝗲𝗿: 1. FROM employees 2. WHERE status = 'active' 3. GROUP BY department 4. HAVING total_employees > 5 5. SELECT department, COUNT(employee_id) AS total_employees 6. ORDER BY total_employees DESC 7. LIMIT 10 This Ankit Bansal's YouTube video gives even more clarity on the SQL execution . SQL Order of Execution (Logical Explanation) | https://lnkd.in/gbdeWjFd Image credits : Nikki Siapno 🔷 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗠𝗼𝗿𝗲 𝗦𝗤𝗟 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 ? 👉 Go through these Playlists : 1. SQL Tips and Tricks https://lnkd.in/gK_qsy2M 2. SQL Medium Complex Interview Problems https://lnkd.in/gj3pFXcP 3. LeetCode SQL Hard Problems https://lnkd.in/gHJdu5Cw 4. Complex SQL Questions for Interview Questions !! https://lnkd.in/g_4uyzHT ♻️ I share cloud , data analysis/data engineering tips, real world project breakdowns, and interview insights through my free newsletter. 🤝 Subscribe for free here → https://lnkd.in/ebGPbru9 ♻️ Repost to help others grow 🔔 Follow Abhisek Sahu for more #sql #engineering #dataanalysis #dataengineering #dataanalyst

📌Imagine you have a SQL interview in 24 hours. You’ve done the work, but you don't feel 100% prepared or confident yet. The nerves are kicking in. 👨💻 I’ve put together the Ultimate Last Minute SQL Preparation Kit to help bridge that gap and boost your success rate. 🟡 𝐓𝐡𝐞 "𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧" 𝐑𝐞𝐯𝐢𝐬𝐢𝐨𝐧 (3 𝐇𝐨𝐮𝐫𝐬) This comprehensive video is your "one stop shop" for a complete SQL refresh. It covers most important questions and scenarios you'll face in product and service based companies. Watch here: Last Minute SQL Interview Prep Kit: Complete Revision in 3 Hours 👉 https://lnkd.in/evH8VM_p (Ankit Bansal ) 𝐊𝐞𝐲 𝐓𝐨𝐩𝐢𝐜𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: 🔷 Finding & Deleting Duplicates: Using GROUP BY [01:19] and ROW_NUMBER() [02:45]. 🔷 Set Operators: Difference between UNION and UNION ALL [05:01]. 🔷 Window Functions: Deep dive into RANK, DENSE_RANK, and ROW_NUMBER [34:51]. 🔷 Advanced Joins: Self joins for manager employee hierarchies [53:16] and complex NULL handling in joins [31:02]. 🔷 Pivoting Data: Converting rows to columns using CASE WHEN and SUM [01:00:05]. 🟡 𝐒𝐨𝐥𝐯𝐞 75 𝐇𝐚𝐧𝐝 𝐏𝐢𝐜𝐤𝐞𝐝 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Use these three focused resources to test your logic on real data sets: 🔷 50 Questions on Hospital Database: https://lnkd.in/gNM9KjW8 🔷 20 Medium Leetcode SQL Questions: https://lnkd.in/geStfM_3 🔷 5 Hard SQL Questions (NamasteSQL): https://lnkd.in/gtYQRN4j 👉 I’m also attaching this well-structured SQL scenario based question document, which will help you understand the concepts more clearly. Document Credits: Respective Owner 📚 Explore my free newsletter article for valuable insights, and don’t forget to subscribe to receive more content like this! 🤝https://lnkd.in/erjAQ3Sq 📌 Save & Share ♻️ this post . 👨💻 Follow Abhisek Sahu for more #sql #dataanalyst #dataanalysis #dataengineering #datascientist #businessanalyst #interview #jobs #career

If you have a SQL interview coming up and need a quick yet comprehensive refresh, this guide is for you. From window functions to complex joins, it covers all the essentials to boost your confidence in under 24 hours.

🎯 Looking for a high-impact SQL refresh? This is it. From finding duplicates to advanced pivoting, this kit covers the heavy hitters of technical interviews. I love the emphasis on "Scenario Based" questions, because in the real world, data is rarely clean and the questions are rarely simple. What's the toughest SQL question you've ever faced in an interview?  If you're currently in the job hunt, this is a must-save! Great share Abhisek Sahu #Database #SQLTips #PortfolioBuilding 

Everyone says "learn SQL." Nobody gives you the actual cheat sheet. I analyzed 50+ analyst job descriptions and real queries from production dashboards. The same 10 functions appeared in almost every single one. Here they are. Syntax included. Copy and use today. 🟢 FILTERING ① WHERE SELECT * FROM orders WHERE status = 'completed' Filter rows before aggregation. This is your first instinct for any query. ② HAVING SELECT city, COUNT(*) FROM users GROUP BY city HAVING COUNT(*) > 100 Filter AFTER aggregation. WHERE filters rows. HAVING filters groups. Know the difference. ③ CASE WHEN SELECT order_id, CASE WHEN amount > 1000 THEN 'high' WHEN amount > 100 THEN 'medium' ELSE 'low' END AS tier FROM orders IF/ELSE logic inside SQL. Use it to create categories, flags, labels on the fly. 🟢 AGGREGATION ④ GROUP BY + COUNT / SUM / AVG SELECT region, COUNT(*) AS users, AVG(revenue) AS avg_rev FROM sales GROUP BY region The foundation of every report. Group rows → calculate metrics per group. ⑤ DISTINCT SELECT COUNT(DISTINCT customer_id) FROM orders Count unique values. Without DISTINCT you count duplicates and your numbers are wrong. 🟢 JOINS ⑥ LEFT JOIN SELECT o.order_id, c.name FROM orders o LEFT JOIN customers c ON o.customer_id = c.id Keep ALL rows from left table. Match what you can from right. Unmatched → NULL. This is 80% of all joins you will ever write. ⑦ INNER JOIN SELECT p.name, s.quantity FROM products p INNER JOIN stock s ON p.id = s.product_id Keep ONLY rows that match in BOTH tables. Use when you need strict matches with no NULLs. 🟢 WINDOW FUNCTIONS ⑧ ROW_NUMBER() SELECT *, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC) AS rn FROM orders Number rows within each group. Filter WHERE rn = 1 to get the latest order per user. Use this daily. ⑨ SUM() OVER / AVG() OVER SELECT date, revenue, SUM(revenue) OVER (ORDER BY date) AS running_total FROM daily_sales Running totals and moving averages without GROUP BY. Your row-level data stays intact. ⑩ LAG() / LEAD() SELECT date, revenue, LAG(revenue) OVER (ORDER BY date) AS prev_day FROM daily_sales Access the previous or next row's value. Calculate day-over-day change in one line. 📌 QUERY TEMPLATE (covers 90% of tasks): SELECT   dimension,   COUNT(*) AS cnt,   SUM(metric) AS total FROM table_a a LEFT JOIN table_b b ON a.id = b.a_id WHERE a.date >= '2024-01-01' GROUP BY dimension HAVING COUNT(*) > 10 ORDER BY total DESC LIMIT 20 This single template handles: → Segmented reports → Top-N analysis → Filtered aggregations → Multi-table analytics The learning path: → Week 1: ①–③ (filtering) → Week 2: ④–⑤ (aggregation) → Week 3: ⑥–⑦ (joins) → Week 4: ⑧–⑩ (window functions) 4 weeks. 10 functions. 90% of analyst SQL covered. Save this cheat sheet. Share with someone starting their analytics journey. 👇 #sql #dataanalytics #analytics #cheatsheet #career #datascience #programming

5 SQL window functions every analyst should know. Here's what each one actually does — in plain English. 👇 --- Window functions are the dividing line between SQL that fetches data and SQL that reasons about it. They let you calculate — across a group of rows — without collapsing those rows into one. That one capability changes everything. --- 𝟭. 𝗥𝗢𝗪_𝗡𝗨𝗠𝗕𝗘𝗥() — 𝗴𝗶𝘃𝗲 𝗲𝘃𝗲𝗿𝘆 𝗿𝗼𝘄 𝗮 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 Assigns a unique sequential number to each row in a partition. Use it when: → You need to dedupe rows ("keep only the first") → You want the latest record per customer / order / session → You need pagination logic Simple but underrated. The first window function I reached for in real work. --- 𝟮. 𝗥𝗔𝗡𝗞() — 𝗿𝗮𝗻𝗸 𝗿𝗼𝘄𝘀, 𝘁𝗶𝗲𝘀 𝗮𝗹𝗹𝗼𝘄𝗲𝗱 Like ROW_NUMBER but with a twist: tied values get the same rank. Use it when: → You're building leaderboards (top spenders, top products) → Ties matter — two customers spending exactly the same should both rank #1 → You want to find the "top N per group" In my Music Store project, I used RANK() to find the most popular genre in every country in a single query. --- 𝟯. 𝗟𝗔𝗚() — 𝗹𝗼𝗼𝗸 𝗯𝗮𝗰𝗸𝘄𝗮𝗿𝗱𝘀 Fetches the value from a previous row in the same partition. Use it when: → Calculating period-over-period change (this month vs last month) → Detecting customer churn (last purchase date vs current) → Building cohort or retention analysis This is the function that turns "a list of transactions" into "a story of behaviour over time." --- 𝟰. 𝗟𝗘𝗔𝗗() — 𝗹𝗼𝗼𝗸 𝗳𝗼𝗿𝘄𝗮𝗿𝗱𝘀 The opposite of LAG. Fetches the value from a future row. Use it when: → Checking what a customer did next (next purchase, next event) → Calculating time gaps between actions → Predicting churn windows ("if no LEAD within 30 days → likely lost") LAG and LEAD together let you compare any row to its neighbours — without writing a single self-join. --- 𝟱. 𝗦𝗨𝗠() 𝗢𝗩𝗘𝗥 (…) — 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘁𝗼𝘁𝗮𝗹𝘀 A cumulative sum that updates row by row. Use it when: → Calculating running revenue or running counts → Tracking cumulative customer value over time → Building burn-down or burn-up reports With ORDER BY inside the OVER clause, every row gets the running total "as of that point." No looping. No subqueries. One pass. --- 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 GROUP BY collapses rows into a summary. Window functions calculate across rows while keeping every row visible. That single difference is what makes them so powerful for analytics — because most business questions need both the detail and the aggregate, side by side. --- Save this for next time you write a query that feels like it needs a self-join. It probably doesn't. It probably needs a window function. Which one of these do you reach for most often? Curious to see what people use in production. #SQL #DataAnalytics #DataScience #SQLTips #BusinessIntelligence #DatabaseDesign

SQL Cheatsheet: The Ultimate Guide That Saved My Time and Effort SQL used to feel like a headache to me I’d spend hours debugging slow queries, wondering where I went wrong. But once I understood how to write efficient SQL queries, everything changed What used to take hours now gets done in minutes and you can do the same 𝗕𝗔𝗦𝗜𝗖 𝗖𝗢𝗠𝗠𝗔𝗡𝗗𝗦 ➛𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 ↳ Retrieve Data with SELECT The SELECT command is the bread and butter of SQL. For example, to get the names of all employees, you can write: ➛SELECT name FROM employees ↳ Filter Results with WHERE Tired of manually searching through rows? The WHERE clause filters data For instance, to find all employees who work in the Sales department, use: ➛SELECT * FROM employees WHERE department = 'Sales' ↳ Insert Records with INSERT Adding new data to a table used to feel complicated, but INSERT made it straightforward To add a new employee named John, aged 30, write: ➛INSERT INTO employees (name, age) VALUES ('John', 30) ↳ Modify Data with UPDATE Made a mistake in your data? You can fix it without starting over If you want to change John’s age to 31, use: ➛UPDATE employees SET age = 31 WHERE name = 'John' ↳ Remove Records with DELETE Need to clean up your database? DELETE gets rid of unnecessary records quickly For example, to remove John from the employee list, write: ➛DELETE FROM employees WHERE name = 'John' 𝗔𝗗𝗩𝗔𝗡𝗖𝗘𝗗 𝗖𝗢𝗠𝗠𝗔𝗡𝗗𝗦 ➛𝗧𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗟𝗲𝘃𝗲𝗹 ↳ Combine Data with JOIN JOIN is a lifesaver when you need to work with multiple tables For example, to link customer orders with their details, you can write: ➛SELECT orders. id, customers. name FROM orders INNER JOIN customers ON orders.customer_id = customers. id ↳ Group Data with GROUP BY GROUP BY helps you analyze data by categories To count the number of employees in each department, write: ➛SELECT department, COUNT(*) FROM employees GROUP BY department ↳ Sort with ORDER BY Sorting data is a breeze with ORDER BY To find the highest earners by sorting salaries in descending order, use: ➛SELECT name, salary FROM employees ORDER BY salary DESC ↳ Filter Groups with HAVING When GROUP BY isn’t enough, HAVING steps in to filter groups. For instance, to identify departments with more than 5 employees, write: ➛SELECT depart, COUNT() FROM employees GROUP BY department HAVING COUNT() > 5 ↳ Use CTEs Break complex queries into manageable parts with CTEs ↳ Leverage Window Functions Perform calculations across rows while retaining the original data 𝗧𝗛𝗘 𝗕𝗜𝗚 𝗣𝗜𝗖𝗧𝗨𝗥𝗘 ➛𝗛𝗢𝗪 𝗜𝗧 𝗖𝗛𝗔𝗡𝗚𝗘𝗗 𝗠𝗬 𝗪𝗢𝗥𝗞 One of the key insights I learned was the SQL 𝐎𝐫𝐝𝐞𝐫 𝐨𝐟 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧, which completely changed how I approached query writing Understanding how SQL processes commands step by step allowed me to write better and faster queries 𝗡𝗢𝗪 𝗜𝗧’𝗦 𝗬𝗢𝗨𝗥 𝗧𝗨𝗥𝗡 ➛ Get the interview call: https://lnkd.in/ges-e-7J

💡 Database Journey for Every Data Professional and Data Teams Whether you’re a SQL Developer, Data Engineer, Data Analyst, Data Scientist, or Data Migration Specialist, this post can guide your path. 🔹 Start Simple Begin with one SQL tool—MySQL. Once you master it, you can easily learn other databases like PostgreSQL, Oracle, MSSQL, IBM DB2, depending on your project needs. Remember few sql scripts are differ in each Sql Databases if you compare one with other. 🔹 Expand to the Cloud Businesses today rely heavily on cloud databases. Some I’ve seen and used in projects include: GCP: Spanner, Cloud SQL AWS: Aurora, RDS SAS Databases (older data warehouse systems) These platforms are widely adopted and open up opportunities across industries. 👉 Call-to-Action: Which databases are you using right now? Share your thoughts—I’d love to learn from your experiences. #SQL #DataEngineering #DataScience #DatabaseManagement #CloudComputing #AWS #GCP #Oracle #PostgreSQL #MSSQL #IBMDB2 #Datamigration