Avoiding Incorrect SQL JOIN Conditions

SQL Caption: COUNT(*) can lie. Duplicates Wrong joins Bad grouping All can mislead your results. Always validate your counts. SQL truth: numbers need context. #SQL #DataQuality

Stop trying to filter aggregated data with a WHERE clause! 🛑 If you’ve ever tried to filter a GROUP BY result and watched your SQL query throw an error, you probably needed the HAVING clause. While WHERE filters individual rows before they are grouped, HAVING filters the groups after the aggregations are calculated. In my latest SQL tutorial, I break down: The fundamental difference between WHERE and HAVING. How to use HAVING with functions like SUM(), COUNT(), and AVG(). Real-world scenarios where this clause is a lifesaver. To Check out the full video click the link in the comments below #SQL #DataAnalytics #DataEngineering #LearningSQL #Database

🚀 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

The quickest way to lose trust in your work is one wrong number I’ve had queries where everything looked fine no errors clean output totals looked reasonable but when I compared it with a source report the numbers didn’t match turned out a join was duplicating rows since then I try to check totals against something I trust before sharing anything small step but it’s saved me a few times Do you usually validate your numbers against a source or rely on the query output? #dataanalytics #sql #datavalidation #analyticscareers #sydneydata

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

🚀 EXISTS in SQL 💡 What is EXISTS? ✔️ Check if a subquery returns any rows ✔️ Returns TRUE if at least 1 row exists ✔️ Returns FALSE if no rows 🔍 How it works: ➡️ Outer query runs ➡️ Inner query checks for matching rows ➡️ If a match is found, the row is included ➡️ If no match, the row is excluded 🗝️ Key Concept 👉 EXISTS does not care about values 👉 It only checks if rows exist 🧠 Even if the subquery returns NULL ➡️ EXISTS can still be TRUE #SQL #DataAnalytics #LearningSQL #TechTips #InterviewPrep #DataEngineering

This SQL query looks completely valid. But it will throw an error and here’s why most analysts don’t catch it. 😶 I’ve seen this mistake at every level, including myself early on. The fix is one word. WHERE filters rows: it runs before GROUP BY. So it has no idea what total_revenue is yet. The moment you reference an aggregate alias in WHERE, the query breaks. HAVING filters groups: it runs after GROUP BY. That is where aggregated filters belong. One wrong clause. Query fails. Report never runs. Wrong decision gets made. Save this before your next query. 🔖 Follow me for one SQL scenario every week. #SQL #BusinessAnalyst #DataAnalytics #LearningInPublic

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