SQL HAVING Clause Explained with Examples

One of the most common SQL mistakes I see is Incorrect JOIN conditions. A query may run perfectly but the results can be completely wrong. Here’s how I avoid that: • Always check row count before and after JOIN • Use INNER JOIN first to validate logic • Verify key columns (IDs should match correctly) • Watch for duplicate rows after JOIN If your rows suddenly increase, your JOIN is likely the problem. In SQL, small mistakes can create big data issues. #SQL #DataAnalytics #DataAnalysis #UAEJobs

🚀 Day 33 – Top 50 SQL Questions Today’s problem: Consecutive Numbers 🔍 Objective: Find numbers that appear at least three times consecutively in a dataset. 💡 Approach: Used self joins on the Logs table Matched rows based on consecutive id values Compared num values across three rows Applied DISTINCT to ensure unique results 🛠️ SQL Query: SELECT DISTINCT l1.num AS ConsecutiveNums FROM Logs l1 JOIN Logs l2 ON l1.id = l2.id - 1 JOIN Logs l3 ON l1.id = l3.id - 2 WHERE l1.num = l2.num AND l2.num = l3.num; 📈 Key Insight: Self joins are highly effective for identifying sequential patterns in relational data. #SQL #DataAnalytics #LeetCode #ProblemSolving #Day33

Your SQL query works… but is it RIGHT? 👇 One of the biggest mistakes analysts make: Trusting results without validating them. Always ask: Does this number make sense? Can I cross-check it? Am I double-counting? SQL is not just querying. It’s thinking. Have you ever caught a wrong query result? 😅 #SQL #DataAnalytics #AnalyticsMindset #DataQuality

Day 6 of my SQL series Today I covered the HAVING clause — used to filter aggregated data. Key concept: WHERE filters rows HAVING filters grouped results Next: Real-world SQL problem #SQL #DataAnalytics #Learning #CareerGrowth

✅ Solved a SQL problem on StrataScratch — Day 54 of my SQL Journey 💪 User activity looks simple… until you try to measure it correctly ⏱️ Today’s problem was about calculating average session time — But sessions weren’t explicitly given. They had to be built from events. The approach: • Identified session boundaries using page_load and page_exit • Used MIN() and MAX() with CASE WHEN to capture valid timestamps • Calculated session duration using TIMESTAMPDIFF() • Filtered out invalid sessions (where load happens after exit) • Averaged session time per user What I practised: • Event-based session reconstruction • Conditional aggregation using CASE WHEN • Time difference calculations in SQL • Data cleaning before aggregation What stood out — Metrics don’t exist in raw data. You have to build them. A “session” isn’t stored anywhere… It’s something you define from behaviour. That’s where analysis actually begins. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

i thought SQL queries execute step by step but but that’s not how it actually works SQL doesn’t start with SELECT it starts with FROM here is the logical order SQL processes a query : 👇 1️⃣ SQL gets the data (FROM) 2️⃣ combines tables if needed (JOIN/ON) 3️⃣ filters rows (WHERE) 4️⃣ groups data (GROUP BY) 5️⃣ filters groups (HAVING) 6️⃣ selects columns (SELECT) 7️⃣ removes duplicates (DISTINCT) 8️⃣ sorts results (ORDER BY) 9️⃣ limits the output (LIMIT) once I understood this, writing queries felt a lot easier 😁 #dataanalyst #dataanalytics #sql #learninginpublic

The TOP function (or LIMIT, depending on your flavor of SQL) is your best friend for performance and data exploration. The Logic Flow: 1️⃣ Filter First: Use WHERE to grab only what you need. 2️⃣ Sort Always: Use ORDER BY so your "Top" rows actually mean something. 3️⃣ Slice Last: Use TOP / LIMIT to cut the noise. Pro-Tip: If you’re using SQL Server, don't forget WITH TIES. It ensures you don't accidentally exclude records that have the same value as your N^{th} row! Whether you’re optimizing a dashboard or just exploring a new schema, remember: Sort before you slice. 📊💻 #SQL #DataAnalytics #DataScience #Database #CodingTips #PerformanceOptimization

Here's an even more concise version ✅ 🔺 SQL Quick Reference ▪️ 1. Basics SELECT: Picks columns. FROM: Specifies the table. WHERE: Filters rows (LIKE, IN, BETWEEN, AND, OR). ORDER BY: Sorts results (ASC, DESC). AS (Alias): Renames columns or tables. ▪️ 2. Summarizing Functions: COUNT (), SUM(), AVG(, MIN(, MAX. GROUP BY: Groups rows for aggregate functions. HAVING: Filters after grouping. ▪️ 3. Combining INNER JOIN: Matching rows from both tables. LEFT JOIN: All from left, matches from right. RIGHT JOIN: All from right, matches from left. FULL JOIN: All rows from both tables. 🔺 Follow Saurabh Rai for more... #SQL #Data #Dataanalyst #Learning #Joins

Stop scrolling and Save This. Because no one has time for a 4-hour tutorial when you just need the data now. Here’s the 80/20 of SQL: 👁️ SELECT & WHERE → See only what you need 📊 GROUP BY → Summarize the mess 🔗 JOINs → Stop copy-pasting between Excel sheets 🧮 Aggregates → Count, Average, Sum (Done) 🛑 Golden Rule: Avoid SELECT * in production. Your DBA will thank you. What’s the one SQL command you use the most? #SQL #DataAnalytics #TechTips #CareerGrowth

Here's an even more concise version: 💥 SQL Quick Reference 1. Basics * SELECT: Picks columns. * FROM: Specifies the table. * WHERE: Filters rows (LIKE, IN, BETWEEN, AND, OR). * ORDER BY: Sorts results (ASC, DESC). * AS (Alias): Renames columns or tables. 2. Summarizing * Functions: COUNT(), SUM(), AVG(), MIN(), MAX(). * GROUP BY: Groups rows for aggregate functions. * HAVING: Filters after grouping. 3. Combining * INNER JOIN: Matching rows from both tables. * LEFT JOIN: All from left, matches from right. * RIGHT JOIN: All from right, matches from left. * FULL JOIN: All rows from both tables. hashtag #SQL hashtag #Data hashtag #Dataanalyst hashtag #Learning hashtag #Joins