SQL Challenge: Subscription Conversion Analysis

❄️ LeetCode Daily Challenge 📅 Day 29 of 50 Days SQL Challenge Today’s challenge was a perfect blend of time-based filtering, aggregation, and business logic — exactly what we deal with in real-world data scenarios. 📌 Problem: Find Golden Hour Customers 🔗Problem Link: https: https://lnkd.in/gPyRUtSG 💡 Problem Breakdown: Identify golden hour customers who: ✔ Placed at least 3 orders ✔ ≥ 60% of orders during peak hours (11:00-14:00 or 18:00-21:00) ✔ Have an average rating ≥ 4.0 29 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 #CustomerInsights #SQLPractice #WindowFunctions #LearningInPublic #50DaysChallenge #DataAnalytics

Day 10/50 – #SQLChallenge 🚀 Solved “Managers with at Least 5 Direct Reports” problem on LeetCode. ✅ Approach: Used subquery with GROUP BY and HAVING ✅ Key Concept: Aggregating data to filter groups based on count 💡 Advanced Insight: The HAVING clause is applied after grouping, making it ideal for filtering aggregated results (like COUNT). This is different from WHERE, which filters rows before grouping. Understanding this distinction is key when working with real-world data. 🔍 Takeaway: Combining subqueries with aggregation helps solve hierarchical data problems like identifying managers and their reporting structure. 10 days of consistency — building strong fundamentals 💪 #SQL #LeetCode #Database #CodingChallenge #ProblemSolving #LearningInPublic #DeveloperJourney

❄️ LeetCode Daily Challenge 📅 Day 21 of 50 Days SQL Challenge Continuing my SQL consistency journey with a Medium-level SQL problem focused on joins and conditional aggregation. 📌 Problem : Market Analysis I 🔗 Problem Link : https://lnkd.in/gK6uAEEJ 💡 Problem Summary : For each user, we need to find: ✔ Their join date ✔ Number of orders they made as a buyer in 2019 Important: Include users with 0 orders Count only orders from 2019 21 days of SQL practice done ✅ Consistency is building real confidence now 💪 Let’s grow one query at a time 🚀 #LeetCode #SQL #DataEngineering #Analytics #Database #WindowFunctions #DailyPractice #LearningInPublic #50DaysChallenge

❄️ 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 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

🚀 Day 30/100 – LeetCode SQL Challenge ✅ Problem Solved: Reformat Department Table Today’s problem focused on transforming row-based data into a structured column format — a common real-world data handling task. 🔍 What I Learned: How to use CASE WHEN in SQL for conditional aggregation Converting rows into columns (pivoting data) Importance of GROUP BY for summarizing data Handling missing values using NULL 💡 My Approach: Grouped data by department id Used SUM(CASE WHEN month = 'Jan' THEN revenue END) pattern Repeated this logic for all 12 months Ensured each month becomes a separate column 📊 This problem helped me understand how SQL can reshape data effectively for reporting and dashboards. 🔥 Consistency is the key — 30 days done, 70 more to go! #Day30 #100DaysOfCode #LeetCode #SQL #DataAnalytics #CodingJourney #PlacementPreparation

❄️ LeetCode Daily Challenge 📅 Day 30 of 50 Days SQL Challenge Today’s challenge was a deep dive into subscription analytics and churn prediction — something very relevant to real-world data scenarios. 📌 Problem: Find Churn Risk Customers 🔗Problem Link: https://lnkd.in/dw95yEPj 💡 Problem Breakdown: Identify churn risk customers who: ✔ Currently have an active subscription (last event is not cancel) ✔ Have at least one downgrade in their history ✔ Current plan revenue < 50% of their historical maximum ✔ Have been subscribed for at least 60 days 30 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 #ChurnAnalysis #CustomerRetention #SQLPractice #WindowFunctions #LearningInPublic #50DaysChallenge

✅ Solved a SQL problem on LeetCode — Day 46 of my SQL Journey 💪 Customers don’t just buy products… They buy patterns together 🛒 Today’s problem was about finding product categories that are frequently bought together — The logic behind “you might also like.” The approach: • Mapped each user to their purchased categories • Used a self-join to create category pairs • Applied category1 < category2 to avoid duplicates • Counted shared users per pair and filtered meaningful ones What I practised: • Self joins to relate data within the same dataset • Using DISTINCT for accurate counting • Applying GROUP BY with HAVING • Translating real behaviour into SQL logic What stood out — Recommendations aren’t random… They’re built on repeated patterns. When enough users repeat the same combination, It becomes a signal. That’s where the real insight lies. SQL doesn’t just store transactions. It uncovers relationships between them. Consistent learning, one query at a time 🚀 #SQL #LeetCode #DataAnalytics #LearningInPublic #SQLPractice

❄️ LeetCode Daily Challenge 📅 Day 19 of 50 Days SQL Challenge Continuing my SQL journey with another Hard-level SQL problem focused on session analytics and behavioral anomaly detection. 📌 Problem: Find Zombie Sessions 🔗 Problem Link: https://lnkd.in/gDEr9VEq 💡 Problem Summary: We need to identify zombie sessions — sessions where users appear active but show abnormal behavior patterns. A session qualifies as a zombie session if it satisfies ALL conditions: ✔ Session duration > 30 minutes ✔ At least 5 scroll events ✔ Click-to-scroll ratio < 0.20 ✔ No purchases during the session Finally, return the result ordered by: scroll_count (DESC), session_id (ASC) The idea was to aggregate event-level data into session-level metrics, then filter sessions based on behavioral thresholds. Another Hard SQL challenge completed — consistency continues 💪 Let’s grow one query at a time 🚀 #LeetCode #SQL #DataEngineering #Analytics #Database #WindowFunctions #DailyPractice #LearningInPublic #50DaysChallenge

🚀 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