Solved LeetCode SQL Challenge with LEFT JOIN and NULL Filtering

❄️ LeetCode Daily Challenge 📅 Day 28 of 50 Days SQL Challenge Continuing my SQL consistency journey with a Medium-level SQL problem focused on user behavior analysis and aggregation. 📌 Problem: Find Emotionally Consistent Users 🔗 Problem Link: https://lnkd.in/gwAXyQGJ 💡 Problem Summary: Identify users who are emotionally consistent based on their reactions: ✔ At least 5 reactions ✔ At least 60% of reactions are the same type ✔ Return dominant reaction + ratio 28 days of consistent SQL practice completed ✅ Consistency is turning into clarity 💪 Let’s grow one query at a time 🚀 #LeetCode #SQL #DataEngineering #Analytics #UserBehavior #DataAnalysis #WindowFunctions #DailyPractice #LearningInPublic #50DaysChallenge

🚀 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

✅ Solved a SQL problem on LeetCode — Day 40 of my SQL Journey 💪 Not every customer leaves suddenly… Some show signs before they churn. ⚠️ Today’s problem was about identifying churn risk customers — users who are still active but showing warning behaviour. I used aggregation and event analysis to: • Track each user’s latest subscription status • Identify downgrade behaviour over time • Compare current spend with historical maximum • Measure total subscription duration • Filter users based on combined risk signals What I practised: • Working with event-based data using GROUP BY • Using CASE WHEN to capture behavioural signals • Extracting latest values with ordered aggregation • Applying multiple conditions to detect patterns What stood out — Churn doesn’t happen instantly… it builds up through small changes. A downgrade here, a drop in spending there. That’s where the real insight lies. SQL isn’t just about analysing what happened. It’s about spotting what might happen next. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic

Day 9/50 – #SQLChallenge 🚀 Solved “Rising Temperature” problem on LeetCode. ✅ Approach: Used SELF JOIN to compare rows within the same table ✅ Key Concept: Comparing consecutive records using date difference 💡 Advanced Insight: Since SQL doesn’t have a direct way to access the previous row, a SELF JOIN helps simulate this behavior by pairing each record with its previous day. This pattern is widely used in time-series data analysis. 🔍 Takeaway: Understanding how to compare rows across time is crucial for real-world scenarios like tracking growth, trends, and performance metrics. Consistency is compounding 📈 #SQL #LeetCode #Database #Joins #CodingChallenge #ProblemSolving #LearningInPublic #DeveloperJourney

Day 87 of #100DaysOfCode Today I solved "Convert Sorted List to Binary Search Tree" on LeetCode using Two Pointers + Recursion. Key Idea: A sorted linked list can be converted into a height-balanced BST by always choosing the middle element as root. Approach: • Use slow & fast pointers to find the middle node • Middle node becomes the root • Left part → build left subtree • Right part → build right subtree Repeat this process recursively to construct the entire BST. Concepts Used: • Linked List • Binary Search Tree (BST) • Two Pointers (Slow & Fast) • Recursion Time Complexity: O(n log n) Space Complexity: O(log n) This problem helped me understand how to transform one data structure into another efficiently From lists → to trees… leveling up data structure mastery #Day87 #100DaysOfCode #LeetCode #BST #LinkedList #Recursion #Cpp #CodingJourney

🚀 Day 28/50 #LeetCodeChallenge Today’s problem: Biggest Single Number This problem was a brilliant exercise in combining Aggregation with Subqueries to filter out duplicates effectively. 🔥 💡 Problem Insight: The task was to find the largest number that appears only once in the table. If no unique number exists, the result should be null. 🧠 Key Learning: Identify Uniqueness: Used GROUP BY combined with HAVING COUNT(*) = 1 to isolate numbers that don't repeat. The Power of MAX(): Applied MAX() on the filtered subset to grab the highest value. Handling Nulls: Using MAX() on a subquery is a clean way to ensure a null output if the criteria aren't met, instead of getting an empty result set. ⚡ Big Realization: The real trick isn't just finding the maximum—it's knowing how to exclude data that doesn't fit your constraints (duplicates) before you look for the top value. 📈 Takeaway: SQL logic is often about "layers." Filtering the noise first makes finding the answer straightforward. Consistency > Difficulty 💯 #Day28 #LeetCode #SQL #DataAnalytics #LearningJourney #CodingChallenge #50DaysOfChallenge

We're turning natural language into SQL, charts, and business insights - live, from any database. Our submission for #CodeforPurpose: DataWhisperer - a Talk to Data platform powered by Groq's LLM API and a LangGraph multi-agent pipeline. The stack we built from scratch: Semantic Agent → Coder Agent → Critic Agent → Narrator Agent Connect PostgreSQL, MySQL, SQLite, CSV, Excel 3 analysis modes: Quick, Deep, Precise Full Trust Trace - see every reasoning step Auto chart generation with confidence scoring Short clip of the build process below 👇 #Datawhisperer #CodeforPurpose #NatWestGroupIndia NatWest Group

Day 18/100 of #100DaysOfCode 💻 Short query. Clear logic. Clean result. 🎯 Customer Placing the Largest Number of Orders:- Find the customer who placed the most orders from the Orders table. GROUP BY to count orders per customer → ORDER BY COUNT DESC to rank them → LIMIT 1 to grab the top one. Done. ✅ 💡 The real insight here - LIMIT 1 is one of those small but powerful tools. Instead of writing a subquery or CTE to find the max, just sort and slice. Cleaner and faster. Sometimes the best query is the shortest one. 🧩 Not every day is a Hard problem grind, some days you just stay consistent, show up, and keep the streak alive. That matters too. 😄 #SQL #100DaysOfCode #LearningInPublic #DataAnalytics #Consistency #DevJourney #LeetCode

🚀 Day 41/50 #LeetCodeChallenge Today’s Problem: Friend Requests II: Who Has the Most Friends 💡 Key Learnings: Strengthened understanding of UNION ALL to combine datasets Practiced GROUP BY + COUNT for aggregation Learned how to handle undirected relationships (counting both requester & accepter) Connected SQL logic with real-world graph concepts (user connections) 🧠 Approach: Combined requester_id and accepter_id into a single dataset, then calculated total friends for each user and identified the one with the highest connections. 🔥 Insight: This problem is similar to finding the degree of a node in a graph, commonly used in social network analysis. #SQL #LeetCode #ProblemSolving #LearningInPublic #50DaysOfCode

❄️ LeetCode Daily Challenge 📅 Day 34 of 50 Days SQL Challenge Today’s challenge was all about analyzing performance improvement over time — a very practical scenario in real-world analytics. 📌 Problem: Find Drivers with Improved Fuel Efficiency 🔗Problem Link: https://lnkd.in/gWqPXFQN 💡 Problem Breakdown: Identify drivers whose fuel efficiency improved: ✔ Calculate efficiency per trip (distance / fuel) ✔ Compare average efficiency between first half (Jan–Jun) and second half (Jul–Dec) ✔ Include only drivers with trips in both halves ✔ Compute improvement = second_half_avg - first_half_avg 34 days of consistent SQL practice completed ✅ Daily practice is turning concepts into intuition 💪 Let’s grow one query at a time 🚀 Drop your approach below 👇 #LeetCode #SQL #DataEngineering #Analytics #PerformanceAnalysis #SQLPractice #LearningInPublic #50DaysChallenge #DataAnalytics