Level Up Your SQL with Window Functions

Struggling with slow SQL queries? Yeah… we’ve all been there. Here’s the cheat sheet I wish I had earlier — simple tweaks, big performance gains. Stop using "SELECT *" like it’s your birthright. Start thinking like a real data professional. ✔ Faster queries ✔ Cleaner logic ✔ Better performance Good SQL isn’t just about getting results… it’s about getting them FAST. Save this. You’ll need it. 🚀 #SQL #DataAnalytics #LearnSQL #TechSkills #Database #CodingTips #DataScience

Today I focused on improving my SQL skills and practiced writing queries for real-world datasets. What I worked: • Joins (INNER, LEFT) • Aggregations (SUM, COUNT, AVG) • Filtering data using WHERE Looking forward to building more projects and sharing them here. #SQL #DataAnalytics #LearningInPublic #Consistency #Growth

🚀 Day 13/30 – Subqueries in SQL Ever felt your SQL queries are getting messy? 🤯 👉 That’s where subqueries come in. 💡 Think of it like this: Solve a small problem first → use that result to solve the bigger one. 🔥 What I learned today: ✔ Subquery runs inside the main query ✔ Helps in dynamic filtering ✔ Makes complex logic simple & clean 🧠 3 Types you must know: 🔹 Scalar → single value 🔹 Nested → multiple values 🔹 Correlated → runs for each row ⚡ Real insight: If you understand subqueries well, you’ll write SQL like a pro analyst 💻 📌 Consistency > Perfection Day by day, getting better 🚀 #SQL #DataAnalytics #LearnSQL #LinkedInLearning

Day 28 SQL Learning Journey I explored the INSERT INTO SELECT statement in SQL and it’s a powerful way to move data between tables efficiently. In simple terms, it allows you to copy data from one existing table and insert it into another existing table, all within a single query. How it works: You select data from a source table Then insert it directly into a target table Key things to remember: The column structure must match (same number of columns and compatible data types) It adds new rows to the target table without affecting the existing data Why this is useful: 1. Backing up specific records 2. Migrating data between tables 3. Saving time when working with large datasets #SQL #Data Analyics #NightStudy #LearningJourney #DataSkills #TechGrowth

🧠 SQL Challenge 4/100 (IN 🆚 EXISTS) 🚀 🚀 Find customers who never placed any order 👉 Return: customer_id, name ⚠️ Catch There is a NULL in Orders.customer_id A very common approach will return 0 rows 😶 🔥 Caption This looks like a basic question… but one NULL breaks most solutions. If your query uses NOT IN, double check it 👀 Do you know the correct way? Drop your answer 👇 #SQL #DataEngineering #LearnSQL #Analytics #TechCareers

📊 SQL Learning Journey Today, I learned a new concept in SQL: HAVING clause So far, I’ve covered: ✔️ SELECT & filtering using WHERE (AND / OR, LIKE) ✔️ ORDER BY for sorting ✔️ GROUP BY for grouping data ✔️ INNER JOIN for combining tables 🔍 Today’s focus: HAVING At first, it felt a bit confusing, but here’s the simple way I understood it: 👉 WHERE filters rows 👉 HAVING filters grouped data 💡 Example: If I group users by plan, HAVING helps me filter only those plans where users are more than a certain number. This small concept made a big difference in understanding how to analyze grouped data more effectively. Step by step, getting more comfortable with SQL. 📈 #SQL #DataAnalytics #LearningJourney #SQLPractice

A SQL feature I don’t see used often: LATERAL (but very useful) While exploring some advanced SQL patterns, I came across LATERAL. It’s simple in idea, but powerful when dealing with row-wise logic. 🔹 What it does LATERAL lets a subquery refer to columns from the current row of the main query. 🔹 Example use case Get the latest order for each customer: SELECT c.customer_id, o.order_id, o.order_date FROM customers c CROSS APPLY ( SELECT order_id, order_date FROM orders o WHERE o.customer_id = c.customer_id ORDER BY order_date DESC FETCH FIRST 1 ROW ONLY ) o; 🔹 Why not a normal join? We can solve this using analytic functions or joins, but LATERAL makes it more direct for row-by-row dependent queries. 💡 What I found useful It simplifies queries where the inner logic depends on each row of the outer query — especially for “top N per group” type problems. Still exploring more use cases — Have you used LATERAL in your queries? #OracleSQL #SQL #DataEngineering #AdvancedSQL #DatabaseDevelopment

Most beginners write queries without thinking… and end up pulling useless data. That’s where the WHERE clause changes everything. It’s not just about filtering rows. It’s about asking the right question from your data. Instead of dumping the whole dataset, you focus only on what matters: → = exact match → != or <> not equal → > < comparisons → >= <= range filtering Good analysis is not about more data — it’s about relevant data. I’m currently learning SQL for data science, and understanding the WHERE clause made me realize how important precise filtering is in real-world analysis. #SQL #DataScience #LearningSQL #DataAnalytics #DataAnalysis #MySQL #SQLQueries #TechLearning #DataSkills #AnalyticsJourney

SQL Challenge 9/100 : Best Time to Trade Stock 📈 (Part-4) 💪 Difficulty - Hard 🔗 Part 3: Refer old post You are given a table with stock prices 📊 🎯 Problem Let’s make it tougher 👇 👉 You can make at most 2 transactions 🔁 👉 You cannot hold multiple stocks at the same time 🚫📦 👉 Must sell before buying again 🔄 ❓ Your Task Write a SQL query to: ✅ Return maximum profit 💰 ✅ Also return the buy & sell days for both transactions\ ⚠️ Rules: Max 2 transactions only 🔢 No overlapping 🚫 Buy < Sell always ⏳ No hardcoding ❌ -- Create table CREATE TABLE stock_prices (   day INT,   price INT ); -- Insert data INSERT INTO stock_prices (day, price) VALUES (1, 100), (2, 180), (3, 260), (4, 310), (5, 40), (6, 535), (7, 695); Tagging Ankit Bansal Sumit Mittal for better reach Sanjay Gatti #SQL #DataEngineering #SQLChallenge #Analytics #LearnSQL 🚀

Learning SQL made me realize something important 👇🏻 Data is useless if you can’t query it properly. Even a simple query like: • SELECT • WHERE • ORDER BY can completely change how you see data. Still practicing, but it's getting interesting 🚀 #SQL #DataAnalytics #Database #LearningSQL