Solved SQL Problem on LeetCode with Pattern Recognition

✅ Solved a SQL problem on LeetCode — Day 38 of my SQL Journey 💪 Most queries give you answers… But some force you to define the question first.❓ Today’s problem was about identifying loyal customers — not just frequent buyers, but those who show consistent behaviour over time. I worked on analysing customer patterns to: • Ensure at least 3 meaningful purchase actions • Check long-term engagement (30+ days activity) • Filter out unstable behaviour through the refund ratio • Combine all conditions cleanly using GROUP BY + HAVING What stood out to me — loyalty isn’t a single metric. A customer can purchase often, yet still not be reliable. Because behaviour is not about volume… it’s about consistency. That’s where the real insight lies. Also noticed something important — boolean logic inside aggregations (like SUM(condition)) works smoothly in MySQL, but small differences like integer division can break results in other SQL environments. Tiny detail. Big impact. SQL isn’t just about querying data. It’s about defining reality through logic. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic

✅ Solved a SQL problem on LeetCode — Day 49 of my SQL Journey 💪 Consistency isn’t always visible… But patterns reveal it over time 📈 Today’s problem was about identifying users with persistent behaviour — not just active users, but those who show the same action consistently across days. I worked on analysing behaviour patterns to: • Track user actions across consecutive days • Group continuous activity using ROW_NUMBER() logic • Identify streaks by adjusting date gaps • Filter users with meaningful streak length (≥ 5 days) • Select the strongest pattern per user using RANK() What I practised: • Window functions for streak detection • Handling sequential data with date logic • GROUP BY + HAVING for filtering patterns • Translating consistency into measurable conditions What stood out — Activity can be random… Consistency is never random. When actions repeat over time, They stop being noise and become behaviour. That’s where real insights start. SQL doesn’t just analyse data. It uncovers patterns hidden in time. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic

🚀 Day 34/100 – SQL Practice (LeetCode) Today I solved the “Daily Leads and Partners” problem. 🔹 Approach: - Identified that the dataset contains duplicate records - Goal was to calculate unique leads and unique partners for each date_id and make_name - Used GROUP BY to organize data and COUNT(DISTINCT ...) to count only unique values 🔹 SQL Solution: SELECT date_id, make_name, COUNT(DISTINCT lead_id) AS unique_leads, COUNT(DISTINCT partner_id) AS unique_partners FROM DailySales GROUP BY date_id, make_name; 🔹 What I Learned: ✔️ Difference between DISTINCT and COUNT(DISTINCT) ✔️ Handling duplicate data efficiently ✔️ Importance of grouping before aggregation ✔️ Writing cleaner and optimized SQL queries 💡 Key Takeaway: 👉 When dealing with duplicates and you need unique counts, COUNT(DISTINCT column) is your best friend. Consistency + Practice = Growth 📈 #Day34 #100DaysOfCode #SQL #LeetCode #DataAnalytics #LearningJourney

🚀 Day 44 of My SQL Learning Journey Today I solved a SQL problem involving aggregation and distinct counting 🔥 🔹 Problem: Find number of unique leads and partners per day 🔗 Problem Link: https://lnkd.in/gXnubrbd 🔹 Solution: SELECT date_id, make_name, COUNT(DISTINCT lead_id) AS unique_leads, COUNT(DISTINCT partner_id) AS unique_partners FROM DailySales GROUP BY date_id, make_name; 🔹 Key Learning: Using multiple COUNT(DISTINCT) Grouping by multiple columns Real-world reporting queries 💡 SQL helps transform raw data into meaningful insights! Consistency continues 🚀 #SQL #LeetCode #90DaysOfCode #CodingJourney #DataAnalytics

☀️Hello mates!! Day 02 solving the SQL 50 LeetCode (02/50) Today's problem is #584: "Find Customer Refree"👨💻 This is an Easy-level problem that focuses on filtering data using SQL condition. I used the WHERE clause, along with proper NULL handling, and it successfully passed all the test case✅️ How my solution Works: ->The WHERE clause is used to select Customer whose refree_id is not equal to 2. ->The OR condition ensure that Customer with no referee (NULL values) are also included in the result. ->Since the problem doesn't require any joins or aggregations, the query stays simple, clean and easy to understand. ->This approach reflect real-world SQL querying practices used in Data Analytics and Data Engineering task. Database Used:MYSQL See you all tomorrow with another exciting LeetCode problem #LeetCode #SQL50 #SQLQuery #DataAnalytics #Database #Coding #SQLPractice #Coding

✅ 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

For a while now, I have been writing about APIs—how systems communicate and exchange data. But there is something just as important that often gets less attention: What happens after the data arrives? That’s where SQL comes in. APIs move data. SQL makes sense of it. From simple queries to complex joins, SQL is the language that turns raw data into insight. So for the next couple of posts, I will be diving into SQL: • Writing efficient queries • Understanding database design • Avoiding common mistakes • Real-world use cases If you have been working with APIs but haven’t explored SQL deeply yet, this is your sign. Let's build better systems—end to end.

✅ Solved a SQL problem on LeetCode — Day 39 of my SQL Journey 💪 Some customers order anytime… But some follow patterns you can actually measure 📊 Today’s problem: identify “Golden Hour Customers” — people who consistently order during peak hours and leave high ratings. I used a single-pass aggregation query to: • Count total orders per customer • Flag orders placed during peak hours ⏰ • Calculate peak-hour percentage • Compute average rating while handling NULLs • Filter customers across all conditions at once What I practised: • CASE WHEN inside aggregations • Applying multiple conditions with HAVING • Time-based filtering using HOUR() • Combining behavioural and quality metrics in one query What stood out — Good customers aren’t just active… they’re consistent in when and how they engage. That’s where the real insight lies. SQL isn’t just about breaking problems into steps. Sometimes it’s about bringing everything together — cleanly. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic

🚀 𝗗𝗮𝘆 𝟱𝟴 𝗼𝗳 𝗦𝗤𝗟 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 – 𝗥𝗼𝘄 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 𝘂𝘀𝗶𝗻𝗴 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗧𝗘 Today’s SQL challenge focused on a classic yet insightful problem: expanding rows based on their own values. 📌 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁 Given a table of numbers, each value n represents how many times it should appear in the output. 👉 Example: 1 → appears 1 time 2 → appears 2 times 3 → appears 3 times 💡 𝗠𝘆 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: I used a Recursive CTE to iteratively build the result set. Starting with the base value, I kept adding rows until each number was repeated the required number of times. This approach felt intuitive almost like translating a loop directly into SQL #Day57 #SQL #DataEngineering #RecursiveCTE #LearningInPublic #ProductAnalytics #Zepto

🚀 Day 42/100 – LeetCode SQL Practice ✅ Problem Solved: Average Time of Process per Machine 💡 My Approach: Each process has a start and end timestamp I joined the table with itself to match: 👉 start row with corresponding end row (same machine_id & process_id) Then calculated: 👉 Time = end - start Finally: 👉 Took average time per machine using AVG() 👉 Used ROUND(..., 3) to format output to 3 decimal places 🧠 What I Learned Today: How to use SELF JOIN in SQL Understanding how to pair related rows (start & end) Using aggregate functions like AVG() Importance of data formatting using ROUND Writing clean queries with GROUP BY 📈 Key Takeaway: “Break the problem → pair related data → compute → aggregate.” #Day42 #100DaysOfCode #LeetCodeSQL #SQL #DataAnalytics #CodingJourney #ProblemSolving