Tamas Szabo’s Post

📊 𝐒𝐐𝐋 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐓𝐡𝐚𝐭 𝐂𝐨𝐯𝐞𝐫𝐬 𝐀𝐥𝐦𝐨𝐬𝐭 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 While revising SQL, I went through a comprehensive cheat sheet and realized one thing: You don’t need to memorize everything — you just need clarity on the 𝐜𝐨𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤𝐬 𝐨𝐟 𝐒𝐐𝐋. Here’s a quick breakdown that can level up your SQL skills: 🔹 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 (𝐃𝐌𝐋) ● SELECT, INSERT, UPDATE, DELETE → Work with data 🔹 𝐃𝐚𝐭𝐚 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 (𝐃𝐃𝐋) ● CREATE, ALTER, DROP, TRUNCATE → Manage database structure 🔹 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 & 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 ● WHERE, ORDER BY → Control what data you see 🔹 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 ● COUNT, SUM, AVG, MIN, MAX ● GROUP BY, HAVING → Analyze data at scale 🔹 𝐉𝐨𝐢𝐧𝐬 (𝐌𝐨𝐬𝐭 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐑𝐞𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬) ● INNER, LEFT, RIGHT, FULL → Combine multiple tables 🔹 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 ● Subqueries, CTEs, Window Functions ● CASE statements for conditional logic 🔹𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 ● String functions like CONCAT, TRIM, REPLACE ● Date functions like CURRENT_DATE, DATEDIFF 𝐒𝐐𝐋 𝐢𝐬 𝐧𝐨𝐭 𝐚𝐛𝐨𝐮𝐭 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐥𝐨𝐧𝐠 𝐪𝐮𝐞𝐫𝐢𝐞𝐬 — it’s about understanding how data flows and how to extract insights efficiently. The more you practice on real datasets, the more these concepts start making sense. 📌 If you're learning 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐨𝐫 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, mastering SQL is 𝐧𝐨𝐧-𝐧𝐞𝐠𝐨𝐭𝐢𝐚𝐛𝐥𝐞. #SQL #DataAnalytics #DataScience #DataEngineering #Database #LearningInPublic #Analytics

📊 𝐒𝐐𝐋 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐓𝐡𝐚𝐭 𝐂𝐨𝐯𝐞𝐫𝐬 𝐀𝐥𝐦𝐨𝐬𝐭 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 While revising SQL, I went through a comprehensive cheat sheet and realized one thing: You don’t need to memorize everything — you just need clarity on the 𝐜𝐨𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤𝐬 𝐨𝐟 𝐒𝐐𝐋. Here’s a quick breakdown that can level up your SQL skills: 🔹 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 (𝐃𝐌𝐋) ● SELECT, INSERT, UPDATE, DELETE → Work with data 🔹 𝐃𝐚𝐭𝐚 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 (𝐃𝐃𝐋) ● CREATE, ALTER, DROP, TRUNCATE → Manage database structure 🔹 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 & 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 ● WHERE, ORDER BY → Control what data you see 🔹 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠 ● COUNT, SUM, AVG, MIN, MAX ● GROUP BY, HAVING → Analyze data at scale 🔹 𝐉𝐨𝐢𝐧𝐬 (𝐌𝐨𝐬𝐭 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐑𝐞𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬) ● INNER, LEFT, RIGHT, FULL → Combine multiple tables 🔹 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 ● Subqueries, CTEs, Window Functions ● CASE statements for conditional logic 🔹𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 ● String functions like CONCAT, TRIM, REPLACE ● Date functions like CURRENT_DATE, DATEDIFF 𝐒𝐐𝐋 𝐢𝐬 𝐧𝐨𝐭 𝐚𝐛𝐨𝐮𝐭 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐥𝐨𝐧𝐠 𝐪𝐮𝐞𝐫𝐢𝐞𝐬 — it’s about understanding how data flows and how to extract insights efficiently. The more you practice on real datasets, the more these concepts start making sense. 📌 If you're learning 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐨𝐫 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, mastering SQL is 𝐧𝐨𝐧-𝐧𝐞𝐠𝐨𝐭𝐢𝐚𝐛𝐥𝐞. ⏩ If you found this informative, 📥 𝐬𝐚𝐯𝐞 𝐚𝐧𝐝 𝐫𝐞𝐩𝐨𝐬𝐭 𝐢𝐭🔁. ⏩ Follow Dhruv Kumar 🛎 for more such content #SQL #DataAnalytics #DataScience #DataEngineering #Database #LearningInPublic #Analytics

🚀 How to Find SQL Queries FAST (Without Memorizing Syntax) Struggling to write SQL queries quickly? Here’s a simple thinking framework that makes it easy 👇 --- 🧠 Step 1: Ask 3 Questions • What data do I need? • From where? • Any conditions? --- 🔑 Step 2: Convert Question → Commands 👉 “Show / Get / Display” → SELECT 👉 “Only / Filter / Specific” → WHERE 👉 “Combine tables” → JOIN 👉 “Total / Count / Average” → GROUP BY + Aggregates 👉 “Each / Per category” → GROUP BY 👉 “Filter after grouping” → HAVING 👉 “Sort / Highest / Lowest” → ORDER BY 👉 “Top N results” → LIMIT / TOP 👉 “Rename column” → AS 👉 “If-Else logic” → CASE 👉 “Range values” → BETWEEN 👉 “Pattern match” → LIKE 👉 “Check NULLs” → IS NULL --- 🧩 Step 3: Think Like LEGO 🧱 Example: “Get total salary of each department where salary > 50K and show highest first” ➡️ SELECT → WHERE → GROUP BY → AGGREGATE → ORDER BY --- ⚡ Golden Trick Convert English → SQL keywords: • “each” = GROUP BY • “only” = WHERE • “total” = SUM • “highest” = ORDER BY DESC --- 🎯 Final Cheat Code Every SQL query is just a combination of: SELECT • FROM • WHERE • JOIN • GROUP BY • HAVING • ORDER BY --- 💡 Master this mindset → You’ll write queries 10x faster! #SQL #DataAnalytics #LearningSQL #TechTips #InterviewPrep #DataScience

Most analysts use SQL to pull data. The best analysts use SQL to think. There's a difference between knowing SQL syntax and actually understanding how a query engine processes your logic. Early in my career, I wrote queries that worked. They returned the right numbers. But I didn't fully understand WHY they worked, and that meant I couldn't optimize them when the data got large. Here's what changed my thinking: STOP writing queries top-down (SELECT first). Start thinking bottom-up, from the WHERE clause inward. The engine filters BEFORE it aggregates, and that order matters enormously for performance. A few things I now apply to every complex query: Use CTEs (Common Table Expressions) instead of nested subqueries; they're readable, debuggable, and the optimiser handles them better in most engines. Filter early, aggregate late. Push your WHERE conditions as close to the raw data as possible. Always ask: "Am I doing this JOIN correctly, or am I accidentally creating a Cartesian product?" EXPLAIN your queries. The execution plan tells you more about your data than the output does. SQL isn't just a retrieval tool. It's a thinking framework. The analysts who treat it that way write better analyses, not just better queries. What's one SQL habit that genuinely improved your analysis quality? Drop it below, let's build a thread.  #SQL #DataAnalysis #DataEngineering #Analytics #QueryOptimization #DataAnalyst

🧠 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 (𝘞𝘩𝘢𝘵 𝘣𝘳𝘦𝘢𝘬𝘴 𝘚𝘘𝘓 𝘲𝘶𝘦𝘳𝘪𝘦𝘴 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘭𝘪𝘧𝘦) After teaching SQL, I’ve noticed something: Most query problems are not about syntax… They’re about logic, data, and assumptions. Here are 10 𝐜𝐨𝐦𝐦𝐨𝐧 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 𝐩𝐢𝐭𝐟𝐚𝐥𝐥𝐬 every analyst should know 👇 1️⃣ JOIN Problems Symptom: Too many rows after joining tables 👉 Cause: Wrong join logic (many-to-many explosion) 2️⃣ Case & Space Issues Symptom: Query returns 0 rows but data exists 👉 Cause: 'Nigeria' ≠ 'nigeria ' 3️⃣ Performance Issues Symptom: Query is fast on small data, slow in production 👉 Cause: Missing indexes 4️⃣ DISTINCT Misuse Symptom: Duplicates still appear 👉 Cause: DISTINCT works on full rows, not one column 5️⃣ NULL Problems Symptom: Calculations (like revenue) look too low 👉 Cause: NULL values break arithmetic 6️⃣ Date Filtering Errors Symptom: Missing records for a specific day 👉 Cause: Timestamp vs date mismatch 7️⃣ GROUP BY Mistakes Symptom: Aggregated results look incorrect 👉 Cause: Wrong grouping level 8️⃣ Aggregation Errors Symptom: “column must appear in GROUP BY” 👉 Cause: Mixing aggregated & non-aggregated fields 9️⃣ Unsafe DELETE Symptom: Important data lost 👉 Cause: No preview before deletion 🔟 Slow Subqueries Symptom: Query takes too long 👉 Cause: Inefficient IN instead of EXISTS or JOIN 🧠 Simple Debug Framework When your SQL looks wrong, check: 1️⃣ JOIN logic 2️⃣ Data quality (NULLs, casing, spaces) 3️⃣ Filters (dates, conditions) 4️⃣ Aggregation logic 5️⃣ Performance (indexes) 💡 One thing I tell my mentees: SQL is not hard because of syntax. It’s hard because small mistakes create big lies. #PostgreSQL #SQL #DataAnalytics #DataEngineering #BusinessIntelligence #Analytics

Most analysts waste hours writing SQL that works… but doesn’t scale. I learned this the hard way at 2 AM, cleaning a messy dataset. The queries ran. But they were slow, messy, and nearly impossible to debug. That night forced me to rethink how I approach SQL. Here’s the shift that changed everything: → Start with WHERE clauses to filter early and reduce noise → Use CTEs to break complex logic into clear, readable steps → Apply window functions to analyze data without losing row-level detail → Leverage subqueries for precise, targeted comparisons Each of these tools solves a different problem. Together, they transform messy queries into structured, scalable logic. Because SQL isn’t just about writing code that runs. It’s about writing code that communicates your thinking. When your queries are clear, your insights come faster. And when your insights come faster, your value increases. Most analysts stop at basic SELECT statements. That’s where they plateau. The real edge comes from going deeper using the right technique at the right time. If you want to stand out in data, master these four skills. Which one do you rely on the most right now? #DataAnalytics #SQL #DataScience #Analytics #DataEngineering #TechCareers #LearnSQL #DataSkills #CareerGrowth #Upskill #DataCommunity #TechSkills #AnalyticsTips

🚀 SQL Isn’t Just Code — It’s a Way of Thinking I used to think SQL was just about writing queries… Until I realised it’s actually about how you think. For example this: SELECT customer_id, COUNT(order_id) FROM orders GROUP BY customer_id; Isn’t just “code”. It’s basically you saying: “Out of all this chaos… who’s actually consistent?” That’s the shift. SQL forces you to stop looking at everything and start focusing on what actually matters. It’s like going from: “I have so much data” to Okay… THIS is the story” 😌 And once you get used to that way of thinking, you start noticing something funny: A lot of decisions people make daily…are basically missing a WHERE clause. Too much noise. Not enough filtering. SQL might look technical on the surface but underneath, it’s just structured curiosity. #SQL #DataAnalytics #DataScience #Databases #AnalyticsMindset #ProblemSolving #TechCareers #QaEngineering

Advanced SQL is not hard. It’s just real-world problem solving. Think like this: • Missing IDs → Missing pages in a book • Percentile (CDF) → Where you stand in a class ranking • Compare tables → Matching two bank statements • Ranking with ties → Leaderboard with shared positions • Customers with no purchase → People who never visited your shop • Conditional count → Counting boys vs girls in a class • LAG function → Comparing today vs yesterday • Overlapping bookings → Double-booked meeting rooms • Above average salary → Top earners in a company • JSON aggregation → Packing data neatly for APIs Same SQL. Real engineering use cases. SQL is not about syntax. It’s about solving data problems at scale. Once you see the pattern → everything becomes predictable #SQL #AdvancedSQL #DataEngineering #DataAnalytics #LearnSQL #CodingTips #TechLearning #DataScience #DevCommunity

Advanced SQL is not hard. It’s just real-world problem solving. Think like this: • Missing IDs → Missing pages in a book • Percentile (CDF) → Where you stand in a class ranking • Compare tables → Matching two bank statements • Ranking with ties → Leaderboard with shared positions • Customers with no purchase → People who never visited your shop • Conditional count → Counting boys vs girls in a class • LAG function → Comparing today vs yesterday • Overlapping bookings → Double-booked meeting rooms • Above average salary → Top earners in a company • JSON aggregation → Packing data neatly for APIs Same SQL. Real engineering use cases. SQL is not about syntax. It’s about solving data problems at scale. Once you see the pattern → everything becomes predictable #SQL #AdvancedSQL #DataEngineering #DataAnalytics #LearnSQL #CodingTips #TechLearning #DataScience #DevCommunity

🗓️ SQL Challenge Day #37: Movie Rating 🔹 Solve two tricky ranking problems in one query! 🎬 🔹 Problem:   Part 1️⃣: Find user with most ratings (tie-break: lexicographically smaller name)   Part 2️⃣: Find movie with highest avg rating in Feb 2020 (tie-break: lexicographically smaller title)  🔹 Solution (UNION ALL):   (     SELECT u.name AS results     FROM Users u     JOIN (       SELECT user_id, COUNT(user_id) AS c       FROM MovieRating       GROUP BY user_id     ) mr ON u.user_id = mr.user_id     ORDER BY c DESC, u.name ASC     LIMIT 1   )   UNION ALL   (     SELECT m.title AS results     FROM Movies m     JOIN (       SELECT movie_id, AVG(rating) AS r       FROM MovieRating       WHERE DATE_FORMAT(created_at, '%Y-%m') = '2020-02'       GROUP BY movie_id     ) mav ON m.movie_id = mav.movie_id     ORDER BY r DESC, m.title ASC     LIMIT 1   );  ✅ Result: Accepted  💡 Key Takeaway:   **UNION ALL + subqueries** cleanly separates two distinct problems!   ⚠️ Critical details:   - `DATE_FORMAT(created_at, '%Y-%m') = '2020-02'` isolates February   - Dual ordering (`c DESC, name ASC`) handles tie-breaks correctly   - Parentheses around each SELECT are mandatory for LIMIT in UNION  👇 Your turn:   What’s your strategy for handling multi-part SQL problems? Do you always split them like this?  #SQL #LeetCode #DataEngineering #ProblemSolving #Coding #LearningInPublic #Database #DataAnalytics