Ajaychandra Arekal’s Post

🚨 You’re Writing SQL Top-to-Bottom… But SQL Doesn’t Run That Way Most people think SQL executes like this 👇 SELECT FROM WHERE GROUP BY HAVING ORDER BY Sounds logical… right? ❌ Wrong. 🧠 Here’s the ACTUAL SQL Execution Order: 1️⃣ FROM → Identify tables 2️⃣ JOIN → Combine data 3️⃣ WHERE → Filter rows 4️⃣ GROUP BY → Aggregate 5️⃣ HAVING → Filter groups 6️⃣ SELECT → Choose columns 7️⃣ DISTINCT → Remove duplicates 8️⃣ ORDER BY → Sort results 9️⃣ LIMIT → Restrict output 💡 Why this matters: Ever faced these issues? • “Why can’t I use an alias in WHERE?” • “Why is my aggregation giving wrong results?” • “Why is HAVING working but WHERE isn’t?” 👉 It’s all about execution order. ⚡ Real insight: SQL is not just a language… It’s a logical processing system. Once you understand the flow: ✔️ Debugging becomes easier ✔️ Queries become more efficient ✔️ You stop writing trial-and-error SQL #SQL #DataAnalytics #LearnSQL #DataEngineering #AnalyticsTips

SQL Cheat Sheet👇 SQL isn’t just about writing queries it’s about understanding how they execute. Every query follows a flow: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT This means data is first picked, then filtered, grouped, and only at the end selected and sorted. Once you understand this sequence along with basics like JOINs and aggregations, your queries become more accurate and efficient. #SQL #DataAnalytics #DataEngineering #Learning #TechSkills

SQL looked simple to me at first, just a few commands like SELECT, WHERE, and JOIN. However I realised how deep it actually is. The more I explore, the more ways I see to structure and retrieve data depending on the problem. It’s the kind of skill that never really loses its value with practice. It feels like something that never really gets old, no matter how much you practice. #SQL #DataSkills

Most people think SQL runs like this: 1. SELECT 2. FROM 3. WHERE 4. GROUP BY 5. HAVING 6. ORDER BY Nice… clean… logical… ❌ But SQL actually wakes up every morning and decides to do things in its own chaotic order: 1. FROM – “Let me see the tables first.” 2. WHERE – “Okay… who survives?” 3. GROUP BY – “Now everybody stand in groups.” 4. HAVING – “Hmm… some groups still look suspicious.” 5. SELECT – “Alright now I’ll show what you asked for.” 6. ORDER BY – “Let’s make it look organized.” So every time someone writes: SELECT * FROM table Just remember… SQL already looked at the table, filtered it, grouped it, judged it… …and THEN decided to show you the result. Moral of the story: SQL doesn’t read your query top → bottom. It reads it like a detective solving a mystery 🕵️♂️ #SQL #DataAnalytics #LearningSQL #DataScience

SQL window functions changed how I think about data. Before I learned them, I was writing subqueries for everything. Clunky. Repetitive. Hard to read. Then I discovered window functions, and the same logic became cleaner, faster, and easier for anyone to follow. The one I kept reaching for: ROW_NUMBER() It assigns a unique rank to each row within a group. Simple idea. Powerful in practice. Real example: find the most recent order per customer. Without window functions: → Write a subquery to get max date per customer → Join it back to the original table → Hope nothing breaks With ROW_NUMBER(): → Partition by customer → Order by date descending → Filter where row = 1 Same result. Half the code. Much easier to explain to a colleague. I used this constantly when building SQL pipelines, pulling the latest record per entity from multi-source business data. It saved time and made my queries reviewable. If you're writing SQL regularly and haven't touched window functions yet, ROW_NUMBER() is where I'd start. Small function. Big shift in how you think. Which SQL concept clicked everything into place for you? Drop it below 👇 #SQL #DataAnalytics #DataScience #LearningInPublic

Most SQL beginners write queries like this: SELECT * FROM (  SELECT user_id, SUM(revenue) AS total  FROM orders  GROUP BY 1 ) t WHERE t.total > 1000; It works. But it's hard to read. Here's the same query using a CTE: WITH user_revenue AS (  SELECT user_id, SUM(revenue) AS total  FROM orders  GROUP BY 1 ) SELECT * FROM user_revenue WHERE total > 1000; Same result. Way easier to read. So what IS a CTE? Think of it like giving your subquery a name and moving it to the top. That's it. Why? → Your query reads top to bottom like a story → Each step has a clear, meaningful name → You can chain multiple CTEs together → Debugging becomes much easier Bonus: Recursive CTEs let you walk through hierarchical data — like org charts or folder trees — in pure SQL. If nested subqueries are giving you headaches, try CTEs. You won't go back. #SQL #DataAnalytics #DataEngineering #LearningInPublic

🚨 𝗬𝗼𝘂𝗿 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝘆 𝗶𝘀 𝗡𝗢𝗧 𝗲𝘅𝗲𝗰𝘂𝘁𝗲𝗱 𝘁𝗼𝗽 𝘁𝗼 𝗯𝗼𝘁𝘁𝗼𝗺. And this is exactly why many queries break. Most people write SQL like this: 𝘚𝘌𝘓𝘌𝘊𝘛 → 𝘍𝘙𝘖𝘔 → 𝘞𝘏𝘌𝘙𝘌 → 𝘎𝘙𝘖𝘜𝘗 𝘉𝘠 But that’s not how SQL runs. Here’s the actual execution order 👇 1️⃣ 𝗙𝗥𝗢𝗠 2️⃣ 𝗪𝗛𝗘𝗥𝗘 3️⃣ 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬 4️⃣ 𝗛𝗔𝗩𝗜𝗡𝗚 5️⃣ 𝗦𝗘𝗟𝗘𝗖𝗧 6️⃣ 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬 7️⃣ 𝗟𝗜𝗠𝗜𝗧 Now the important part: 👉 𝗦𝗘𝗟𝗘𝗖𝗧 𝗿𝘂𝗻𝘀 𝗮𝗹𝗺𝗼𝘀𝘁 𝗮𝘁 𝘁𝗵𝗲 𝗲𝗻𝗱. Which means: • You can’t use column aliases in WHERE • Aggregations don’t exist before GROUP BY • HAVING works on grouped data, not raw rows That’s why beginners get errors like: ❌ “column not found” ❌ “invalid aggregation” Here’s the truth: 𝗦𝗤𝗟 𝗶𝘀 𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝘁𝗼𝗽-𝗱𝗼𝘄𝗻. 𝗕𝘂𝘁 𝗲𝘅𝗲𝗰𝘂𝘁𝗲𝗱 𝗯𝗼𝘁𝘁𝗼𝗺-𝘂𝗽. Once you understand this, debugging SQL becomes 10x easier. How long did it take you to realize this? 😄👇 #SQL #DataEngineering #Analytics #Database #ETL

10 Practical SQL Tips Every Data Professional Should Know SQL is not just about writing queries—it’s about writing efficient, readable, and scalable queries. Here are some practical tips that have helped me: 1. Always start with clear logic before writing the query. 2. Avoid SELECT * — fetch only the columns you need. 3. Use aliases to make queries clean and readable. 4. Master JOINs (INNER, LEFT, RIGHT) — they solve most real problems. 5. Use WHERE vs HAVING correctly (row-level vs aggregated filtering). 6. Leverage window functions for advanced analysis (RANK, ROW_NUMBER, etc.). 7. Break complex queries using CTEs (WITH clause). 8. Always check for NULL handling (COALESCE, IS NULL). 9. Optimize performance using indexes and filters early. 10. Test queries on small data before scaling to large datasets. #SQL #DataAnalytics #BusinessIntelligence #DataTips #Learning #CareerGrowth

My #SQL queries were slow!!! until I understood 𝐉𝐎𝐈𝐍𝐬 properly. I used to think JOINs were just syntax. 𝐈𝐍𝐍𝐄𝐑, 𝐋𝐄𝐅𝐓, 𝐑𝐈𝐆𝐇𝐓, 𝐎𝐔𝐓𝐄𝐑… all looked the same. But in real queries? Choosing the wrong JOIN = wrong data + slow performance. Once I understood this, everything changed. Cleaner queries. Better results. Faster execution. If you’re learning SQL… 𝐌𝐚𝐬𝐭𝐞𝐫 𝐉𝐎𝐈𝐍𝐬 𝐞𝐚𝐫𝐥𝐲, 𝐈𝐭 𝐬𝐚𝐯𝐞𝐬 𝐲𝐨𝐮 𝐡𝐨𝐮𝐫𝐬 𝐥𝐚𝐭𝐞𝐫. #SQL #JOINS #DataAnalytics #Database #LearningInPublic #100DaysOfSQL

🚀 **Day 13 of 30 Days SQL Series** Today I solved **Queries Quality and Percentage** — and this one really helped me understand **conditional aggregation** better 👀 --- 💡 **Problem in simple words:** We are given a table with: * `query_name` * `position` * `rating` 👉 Task: * Find **quality** = average of (rating / position) * Find **poor_query_percentage** = % of queries where rating < 3 --- 🧠 **Where I got stuck (my mistake):** Initially, I tried something like: ```sql COUNT(rating < 3) ``` 👉 I thought it would count rows where rating is less than 3 ❌ But this was wrong --- 🚨 **Why it was wrong?** * `rating < 3` returns TRUE/FALSE * `COUNT()` does **not count condition directly** * It only counts **non-null values** 👉 So my logic was incorrect here --- 💡 **What I learned (Important 🔥):** To count conditionally, we need: ```sql COUNT(CASE WHEN rating < 3 THEN 1 END) ``` 👉 This works because: * When condition is true → returns 1 → counted ✅ * When false → returns NULL → ignored ✅ --- 🔥 **Key Learnings:** * `COUNT()` cannot directly handle conditions * **Conditional aggregation using CASE WHEN is a must-know concept** * `SUM()` and `COUNT()` can be combined with conditions to solve complex problems * Always be careful with **integer division** → use `1.0` or `100.0` * Small syntax mistakes can completely change the logic --- 📌 **Final Thought:** Sometimes the mistake is not in logic, but in how SQL interprets it — understanding that difference is the real game changer. --- #SQL #LearningInPublic #30DaysOfSQL #DataAnalytics #LeetCode #SQLPractice