SQL Challenge: Emotionally Consistent Users Analysis

❄️ 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

❄️ 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 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 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

❄️ LeetCode Daily Challenge 📅 Day 40 of 50 Days SQL Challenge Today’s challenge focused on subscription funnel analysis — a common real-world business analytics use case 📈 📌 Problem: Analyze Subscription Conversion 🔗Problem Link: https://lnkd.in/gSEZWxJb 💡 Problem Breakdown: Identify users who converted from free trial to paid subscription: ✔ Find users who moved from free_trial to paid ✔ Calculate average daily activity duration during free trial ✔ Calculate average daily activity duration during paid period ✔ Round averages to 2 decimal places 40 days of consistent SQL practice completed ✅ Daily practice is turning concepts into intuition 💪 Let’s grow one query at a time 🚀 💬 How would you approach this? Single query with conditional aggregation or separate CTEs? Drop your thoughts below 👇 #LeetCode #SQL #DataEngineering #AzureDataEngineer #ProductAnalytics #SQLPractice #LearningInPublic #50DaysChallenge #DataAnalytics

❄️ 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

❄️ 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

🚀 Day 27 – 30 Days SQL LeetCode Challenge Almost at the finish line! 🏁 Today’s problem was a great example of real-world analytics queries 📊 📌 Today's Problem: Market Analysis I (LeetCode #1158) 🧠 Problem Statement: For each user, find: • Their join date • The number of orders placed in 2019 💡 Key SQL Concepts Used: • LEFT JOIN • Conditional aggregation (CASE WHEN) • COUNT() • GROUP BY 📚 What I Practiced Today: ✔ Conditional counting using CASE WHEN ✔ Keeping all users using LEFT JOIN ✔ Aggregating filtered data 🔥 This pattern is used in: • Business dashboards • KPI tracking • Customer activity analysis 🔗 GitHub Repository: https://lnkd.in/e8aV37dA #SQL #LeetCode #DataAnalytics #BusinessAnalytics #30DaysOfSQL #LearningInPublic

🚀 SQL Practice – Day 4 | LeetCode Solved the Article Views problem today, focusing on data filtering using WHERE conditions and handling DISTINCT values effectively. 💡 This practice helped reinforce my understanding of writing clean, optimized SQL queries and deriving accurate insights from data. Staying consistent and improving step by step! #SQl #LeetCode #100Days

🚀 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