Combine Two Tables with SQL JOIN Operations

Day 43 | LeetCode Learning Journal 🚀 Today I solved Customers Who Never Order using SQL joins and NULL filtering. This problem helped me understand how to find missing data across related tables! 🔑 Key Points: • Given Customers and Orders tables • Needed to find customers who never placed any order • Used LEFT JOIN to combine tables • Applied NULL check to filter non-ordering customers 🌱 What I Learned: • LEFT JOIN helps identify missing relationships • Importance of checking NULL values in SQL • Real-world use case: finding inactive users/customers • Improved understanding of JOIN + filtering logic #LeetCode #100DaysOfCode #DSA #SQL #CodingJourney #Day43 🚀

🚀 Day 51 | LeetCode Learning Journal Today I solved Rising Temperature using SQL. This problem helped me understand how to compare data across rows and work with dates effectively! 🔑 Key Points: • Used SELF JOIN to compare current and previous day • Applied DATEDIFF() to ensure consecutive dates • Checked temperature increase condition • Explored window function (LAG) as an alternative 🌱 What I Learned: • How to compare rows within the same table • Working with date functions in SQL • Importance of handling time-based data • Writing efficient queries using joins and window functions #LeetCode #100DaysOfCode #DSA #CodingJourney #SQL #Database #Day51 🚀

🚀 Day 45 | LeetCode Learning Journal Today I solved LeetCode 182 – Duplicate Emails using SQL aggregation. This problem helped me understand how to identify repeated data in a table efficiently! 🔑 Key Points: • Used a single Person table • Applied GROUP BY to group emails • Used COUNT() to count occurrences • Filtered duplicate emails using HAVING COUNT(*) > 1 🌱 What I Learned: • How to find duplicate records in SQL • Difference between WHERE and HAVING • Importance of aggregation functions • Writing clean and optimized SQL queries #LeetCode #100DaysOfCode #DSA #SQL #CodingJourney #Day45 🚀

🚀 Day 49 | LeetCode Learning Journal Today I solved Rank Scores using SQL. This problem introduced me to ranking functions and how to handle duplicate values efficiently in databases! 🔑 Key Points: • Used DENSE_RANK() for ranking • Applied ORDER BY to rank scores in descending order • Understood difference between RANK() and DENSE_RANK() • Ensured no gaps in ranking 🌱 What I Learned: • How ranking functions work in SQL • Handling duplicates while assigning ranks • Writing cleaner queries using window functions • Importance of choosing the right ranking method #LeetCode #100DaysOfCode #DSA #CodingJourney #SQL #Database #Day49 🚀

🚀 Day 34/50 – #LeetCode Challenge Today’s problem: Product Price at a Given Date 📊 This one really tested my understanding of handling time-based data in SQL. 💡 Key Learning: When dealing with historical data, it's important to focus on the latest valid record before a given date — not just any record. 🔍 Approach I used: Consider all products Find the most recent price change on or before the target date (2019-08-16) If no change exists → use default price = 10 Used concepts like MAX(date), GROUP BY, and JOIN 🧠 What I improved today: Writing cleaner SQL queries Handling edge cases (no previous data) Thinking in terms of real-world data scenarios 🔥 Consistency is building confidence step by step. #Day34 #LeetCode #SQL #DataStructures #CodingJourney #Consistency #PlacementPreparation

Solving the SQL 50 challenge on LeetCode has been an exciting and confidence-boosting journey. Each problem pushed me to think deeper, optimize queries, and strengthen my understanding of SQL concepts. From basic queries to advanced topics like joins, aggregations, subqueries, and window functions — this journey was truly enriching 💡 📘 What’s in my notes/document? A structured study guide covering: All 50 problems with solutions Clear explanations for each query Key SQL concepts like SELECT, JOINs, Aggregations, Window Functions, and more Practical patterns and best practices for real-world scenarios Once you get a solid grip on fundamentals, problems labeled medium or even hard start feeling like easy. It’s all about consistency and practice. #SQL #LeetCode #DataEngineering #LearningJourney #sql50

🚀 Day 11/30 of My LeetCode Journey (SQL Focus) Keeping the momentum going and diving deeper into SQL concepts! 📊🔥 🔹 **SQL Problem of the Day** 👉 *Rank Scores* Given a `Scores` table, write a query to rank the scores from highest to lowest. Rules: • Same scores share the same rank • No gaps in ranking (dense ranking) 💡 *Key Concept:* Using ranking functions like `DENSE_RANK()` (or alternative logic if not available). Understanding ranking functions is a game changer for analytical queries 📈 Day 11 done ✅ #LeetCode #30DaysChallenge #SQL #CodingJourney #Consistency #ProblemSolving #DataAnalytics #Learning

🚀 Day 26 – 30 Days SQL LeetCode Challenge Today’s problem was simple but highlights an important real-world scenario 👀 📌 Today's Problem: Article Views I (LeetCode #1148) 🧠 Problem Statement: Find all authors who viewed at least one of their own articles. 💡 Key SQL Concepts Used: • DISTINCT • Filtering with conditions • Comparing columns within the same row 📚 What I Practiced Today: ✔ Comparing values within the same table ✔ Removing duplicates using DISTINCT ✔ Writing clean filtering queries 🔥 This pattern is useful in: • Detecting self-actions (self-purchases, self-views) • Fraud detection • User behavior analysis 🔗 GitHub Repository: https://lnkd.in/e8aV37dA #SQL #LeetCode #DataAnalytics #30DaysOfSQL #LearningInPublic

🚀 Day 17/30 of My LeetCode Journey (SQL Focus) Another day, another step forward in mastering SQL 📊🔥 🔹 **SQL Problem of the Day** 👉 *Actors and Directors Who Cooperated At Least Three Times* Given an `ActorDirector` table, find all pairs of (actor_id, director_id) who have worked together at least three times. 💡 *Key Concept:* GROUP BY on multiple columns + HAVING COUNT() ≥ 3. Learning how to aggregate data across multiple columns is a powerful skill 💡 Day 17 done ✅ #LeetCode #30DaysChallenge #SQL #CodingJourney #Consistency #ProblemSolving #DataAnalytics #Learning

🚀 Day 50 | LeetCode Learning Journal Today I solve Delete Duplicate Emails using SQL. This problem helped me understand how to clean duplicate data efficiently in databases! 🔑 Key Points: • Used SELF JOIN to identify duplicate emails • Compared records using id to keep the smallest one • Deleted unwanted duplicate rows • Explored alternative solution using GROUP BY 🌱 What I Learned: • How to remove duplicate records in SQL • Importance of data cleaning in real-world applications • Working with self joins for comparison • Writing efficient delete queries #LeetCode #100DaysOfCode #DSA #CodingJourney #SQL #Database #Day50 🚀