Documenting Queries for Future You

🚀 𝗠𝘆 𝗦𝗤𝗟 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 🗓️𝗗𝗮𝘆 𝟯𝟭 📌𝗧𝗼𝗽𝗶𝗰:𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗖𝗧𝗘𝘀 (Common Table Expressions) Today, I explored 𝗖𝗧𝗘𝘀 (𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀) — one of the most useful SQL features for writing clean, readable, and maintainable queries. 📌𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗖𝗧𝗘? A Common Table Expression (CTE) is a temporary result set created using the "WITH" clause, which can be referenced within a query. 📌𝗪𝗵𝘆 𝘂𝘀𝗲 𝗖𝗧𝗘𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝘀𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀? - Improves readability - Makes queries easier to debug and maintain - Helps organize complex logic in a step-by-step way - Can improve performance in some scenarios - Useful when the same result needs to be referenced clearly within a query 📌𝗖𝗼𝗺𝗺𝗼𝗻 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 𝗼𝗳 𝗖𝗧𝗘𝘀 - Simplifying complex queries - Replacing nested subqueries - Performing step-by-step data transformations - Working with hierarchical or recursive data ✨𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 CTEs make SQL queries more structured, professional, and easier to manage, especially when dealing with complex logic #SQL #SQLLearning #CTE #CommonTableExpressions #DataAnalytics #DataAnalyst #LearningJourney #40DaysOfCode #SQLJourney

🚀 𝗗𝗮𝘆 𝟮𝟴 𝗼𝗳 𝗠𝘆 𝗦𝗤𝗟 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 — 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 (𝗣𝗮𝗿𝘁 𝟯) — Diving deeper into SQL, today I explored the difference between 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝗡𝗼𝗻-𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 — a key concept for writing efficient queries 🔍 🔷 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝘆 ✔ Depends on the outer query ✔ Executes for each row ✔ Best for row-wise or group-level comparisons ⚠ May impact performance on large datasets 🔷 𝗡𝗼𝗻-𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝘆 ✔ Independent of outer query ✔ Executes only once ✔ Faster and more efficient ✔ Ideal for overall comparisons 💡 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: 👉 Row-Level Logic ➝ Correlated Subquery 👉 Single Aggregate ➝ Non-Correlated Subquery ✨ 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: 🔸 Choosing the right subquery improves performance significantly 🔸 Correlated subqueries can often be replaced with JOINs for better efficiency 🔸 Query optimization is as important as correctness in real-world scenarios 🔸 Understanding execution flow helps in debugging complex queries Every day is a step closer to mastering SQL and thinking more like a data engineer 📊 #SQL #DataAnalytics #LearningJourney #Database #Coding

I used to memorize SQL JOINs… and still get them wrong. Until I realized something important: SQL joins are not syntax problems, they are relationship problems. INNER JOIN, LEFT JOIN, RIGHT JOIN all describe how two tables interact: INNER JOIN (JOIN)→ only what overlaps LEFT JOIN → everything on the left + matches RIGHT JOIN → everything on the right + matches Once I mapped it using Customers vs Orders and a simple Venn diagram, it finally clicked. Now SQL feels less like code… and more like logic. And in real-world data systems, that mindset matters more than memorization. #SQL #SQLJoins #DataAnalytics #DataEngineering #Database #LearningSQL #DataScience #BusinessIntelligence #Analytics #TechEducation #Programming #BackendDevelopment #RelationalDatabase #DataSkills #TechCareer

✅ Solved a SQL problem on LeetCode — Day 40 of my SQL Journey 💪 Not every customer leaves suddenly… Some show signs before they churn. ⚠️ Today’s problem was about identifying churn risk customers — users who are still active but showing warning behaviour. I used aggregation and event analysis to: • Track each user’s latest subscription status • Identify downgrade behaviour over time • Compare current spend with historical maximum • Measure total subscription duration • Filter users based on combined risk signals What I practised: • Working with event-based data using GROUP BY • Using CASE WHEN to capture behavioural signals • Extracting latest values with ordered aggregation • Applying multiple conditions to detect patterns What stood out — Churn doesn’t happen instantly… it builds up through small changes. A downgrade here, a drop in spending there. That’s where the real insight lies. SQL isn’t just about analysing what happened. It’s about spotting what might happen next. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic

🔍 SQL Joins - explained once and for all! If you've ever stared at a JOIN query and thought "wait, what's actually being returned here?" you're not alone. Here's a quick visual breakdown every data professional should bookmark 👇 ✅ INNER JOIN → Only matching rows from both tables ✅ FULL JOIN → Everything from both tables (matched + unmatched) ✅ FULL JOIN + WHERE NULL → Only the rows that DON'T match (outer exclusive) ✅ LEFT JOIN → All of A + matching rows from B ✅ LEFT JOIN + WHERE B.key IS NULL → Only rows in A with NO match in B ✅ RIGHT JOIN → All of B + matching rows from A ✅ RIGHT JOIN + WHERE B.key IS NULL → Only rows in B with NO match in A Mastering JOINs is the difference between writing queries that work... and writing queries that work efficiently. 💡 Save this post next time you're lost in a JOIN rabbit hole. 🐇 Which JOIN do you use the most in your day-to-day work? Drop it in the comments! 👇 #SQL #DataEngineering #DataScience #Analytics #Programming #TechTips #LearnSQL #DatabaseDesign #DataAnalytics #SoftwareEngineering #Coding #BigData #CareerGrowth #TechCommunity

SQL can seem intimidating at first, but most real-world queries rely on a few fundamental concepts. By mastering these 20 SQL concepts, you'll be ahead of many aspiring data analysts and developers: ✅ SELECT ✅ WHERE ✅ JOIN ✅ GROUP BY ✅ ORDER BY ✅ Subqueries ✅ HAVING ✅ INSERT / UPDATE / DELETE and more. Remember, don't try to learn everything in one day. Build queries, break them, debug them, and repeat. This practice is key to truly understanding SQL. Which SQL concept took you the longest to grasp? For me, JOINs and Subqueries were the toughest challenges. #SQL #DataAnalytics #DataEngineering #Database #LearningSQL #SQLQueries #TechSkills #Programming #CareerGrowth #DataAnalyst #SoftwareEngineering #BeginnersGuide

📅 Day 1 out of 100 — Solving SQL Problems Daily 🚀 #100DaysOfCode 📘 Focus: SQL | Data Analytics | Problem Solving 📈 One Question a Day → 100 Days of Consistency What started as a small habit turned into a powerful learning journey. Solving SQL problems daily helped me strengthen my fundamentals and think more logically about data. 💡 What I gained from this journey: Strong understanding of JOINs, Subqueries & Aggregations Improved query optimization & thinking approach Confidence in solving real-world data problems 🔍 Sample Problem: LeetCode 175. Combine Two Tables 👉 Task: Retrieve first name, last name, city, and state for every person. If no address exists, return NULL. 👉 Solution: SELECT p.firstName, p.lastName, a.city, a.state FROM Person p LEFT JOIN Address a ON p.personId = a.personId; 🧠 Key Learning: Using LEFT JOIN ensures all records are included, even when matching data is missing. Consistency really compounds. From writing basic SELECT queries to confidently solving real problems—this journey was worth it. Not stopping here… next goal: Advanced SQL + Real-world datasets + Analytics projects 📊 If you're thinking of starting, just begin with 1 question today. #SQL #DataScience #100DaysOfCode #Learning #Consistency #GenZEngineers #LeetCode

If you want to become a stronger data engineer, improving your SQL is one of the highest-leverage things you can do. Not just writing joins and aggregations, but understanding grain, debugging mismatched metrics, using window functions properly, and thinking about performance and data quality. I’ve just published a new Substack post called 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁. The aim was to create a reference-style guide that helps people build stronger fundamentals and identify what to learn next. I’ve put the link in the comments. Let me know your thoughts... #dataengineering #SQL #substack ----------------------------------- 💻 Follow Faisal for more data engineering-related content ♻️ Book some time with me here: https://lnkd.in/emA_BFqP 📫 Subscribe to my Substack to read about my thoughts on data: https://lnkd.in/evpBJc9n

Earlier this week, I was debugging a legacy SQL script. It was a mess of subqueries wrapped inside subqueries—like trying to unwrap an endless stack of boxes just to find one small item. It was hard to read, impossible to debug, and slowed the whole team down. The fix? I refactored the entire thing into clean CTEs (Common Table Expressions). Here is why I’ve made the switch: Readability: CTEs let you name your data blocks. You read the code from top to bottom, like a story, not from the inside out. Easy Debugging: You can test each "block" individually. No more untangling a web of parentheses. Team Speed: If a teammate can understand your query in 30 seconds instead of 30 minutes, you’ve just saved the company money. In Data Science Engineering, "clean" is often better than "clever." #SQL #DataEngineering #DataScience #CleanCode #TechTips

🚀 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