Avoid SQL Mistakes: GROUP BY, SELECT *, NULLs

I was stuck on a simple SQL problem today… 😵💫 This is the problem from Ankit Bansal 100 days of sql course "Find the nth Sunday after a given date” I completely could not understand the problem at all Then I realized — 👉 The problem wasn’t SQL 👉 It was my understanding of how dates actually work 💡 The Breakthrough In MySQL: Sunday = 1 Monday = 2 Tuesday = 3 Wednesday = 4 Thursday = 5 Friday = 6 Saturday = 7 So the real question becomes: “How many days should I add to reach the next Sunday?” 🔥 The formula that changed everything: (8 - DAYOFWEEK(date)) % 7 🧠 Why this works? We are basically doing: 👉 (Target Day + 7 - Current Day) % 7 For Sunday: Target = 1 So: 👉 (1 + 7 - current_day) % 7 👉 = (8 - current_day) % 7 📊 Example: Date = Saturday (7) (8 - 7) % 7 = 1 → Next day is Sunday ✅ ⚠️ One Important Catch: If the date itself is Sunday: (8 - 1) % 7 = 0 ❌ But the question says: 👉 “STRICTLY AFTER” So we fix it: CASE WHEN DAYOFWEEK(date) = 1 THEN 7 ELSE (8 - DAYOFWEEK(date)) END 🚀 Final Insight: 👉 “Next Sunday + (n-1) weeks” That’s it. That’s the entire problem. 💭 What I learned today: Most SQL problems are not about syntax… They’re about thinking clearly. If you're learning SQL: Don’t memorize queries. Understand the logic. That’s where the real power is 💥 #SQL #DataEngineering #LearningInPublic #MySQL #ProblemSolving #AIEngineering

SQL remains one of the most in-demand and timeless skills in tech, and for good reason. Nearly every application that stores data relies on it. This beginner-friendly Introduction to SQL covers everything needed to get started working with databases: → What SQL is and why it matters (ANSI standard, RDBMS basics) → Core statements: SELECT, INSERT INTO, UPDATE and DELETE → Filtering data with WHERE, AND, OR and NOT → Sorting and grouping with ORDER BY and GROUP BY → Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN and FULL JOIN → Aggregate functions: COUNT, SUM, AVG, MIN and MAX → Constraints: PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE and CHECK → Creating and modifying databases and tables with CREATE, ALTER and DROP → Advanced topics: UNION, HAVING, EXISTS, ANY, ALL and subqueries → Built-in functions for dates, text formatting and rounding → A full SQL statement syntax reference Whether starting from zero or filling in knowledge gaps, this is a solid foundation for anyone working with data. Save this post and share it with a teammate or student who is just getting started with databases. #SQL #Database #DataEngineering #LearnSQL #DataAnalysis #MySQL #PostgreSQL #TechSkills #CodingForBeginners #DataScience #BackendDevelopment #RDBMS #TechEducation #SoftwareDevelopment #100DaysOfCode #Programming #DataManagement #TechCommunity #LearnToCode #DatabaseManagement

Recently ran through LeetCode’s SQL 50 to rigorously brush up on my database logic. It’s one thing to write a query that returns the right answer; it’s another to write one that scales efficiently under the hood. Here are a few core technical insights and optimizations I found most valuable to revisit: ⚡ EXECUTION & OPTIMIZATION • Favor GROUP BY over Window Functions: Window functions compute values per row without collapsing the dataset, leading to massive memory duplication in large tables. GROUP BY efficiently compresses data into unique combinations first. • Eliminate Multi-Pass Scans: Relying on nested IN and MAX() subqueries forces the database engine to evaluate and scan the same table multiple times. • The "Aggregate-Sort-Limit" Pattern: The most computationally efficient way to find a top record is often to compute the metric, sort it, and slice the top off (GROUP BY ... ORDER BY ... LIMIT 1). You can also leverage your ORDER BY clause to handle secondary tie-breakers natively in a single pass. 🧠 LOGIC & EDGE CASES • The "Aggregate Hack" for NULLs: An empty set is not the same as a NULL value. If an API requires a 1-row NULL result instead of an empty table, wrapping the target in an aggregate function like MAX() or MIN() forces the database to return a single NULL row even if no matches are found. • Rate = Average: Calculating a binary success rate is mathematically identical to taking the average of 1s and 0s. There's rarely a need to count the numerator and denominator separately. • Know Your Ranking Functions: DENSE_RANK() is crucial when finding top unique values (like salaries) because it assigns the same rank to ties without skipping subsequent numbers (1, 1, 2). Using RANK() (1, 1, 3) or ROW_NUMBER() will break your logic on ties. A great exercise in writing cleaner, more highly optimized SQL. Next up: tackling the Advanced SQL 50 track. 📊 #SQL #DataScience #DataEngineering #LeetCode #DatabaseOptimization #PostgreSQL #SQL50

Day 26 & 27 – SQL Learning Journey Subqueries — a concept that looks difficult at first but becomes very straightforward once you understand the logic behind it. A subquery is simply a query inside another query. Instead of solving everything in one complex statement, you break the problem into smaller steps and let SQL handle it cleanly. Here are the core types I learned: • Single-row subquery Returns one value and is used with operators like =, <, > • Multiple-row subquery Returns multiple values and works with IN, ANY, ALL • Correlated subquery Runs for each row of the outer query and depends on it What I realized : The problem is not subqueries — it’s how we approach them. Once the thinking is clear, they become one of the most useful tools in SQL. 1) Independent (non-correlated) → runs once 2) Dependent (correlated) → runs per row “Only correlated subqueries depend on the outer query.” They help simplify logic, improve readability, and handle real-world use cases more effectively. #SQL #LearningJourney #DataAnalytics #SQLDeveloper #TechSkills

🗄️ 5 Basic SQL Queries Every Developer Must Know If you're starting your data journey — or just need a quick refresher — these are the building blocks of SQL that you'll use every single day. ✅ SELECT — Read/fetch data from a table ✅ WHERE — Filter rows based on conditions ✅ ORDER BY — Sort your results (ASC or DESC) ✅ INSERT INTO — Add new records to a table ✅ UPDATE — Modify existing records ✅ DELETE — Remove records you no longer need Together, these form the CRUD pattern: 📌 Create → INSERT 📌 Read → SELECT 📌 Update → UPDATE 📌 Delete → DELETE 💡 Pro tip: Always use a WHERE clause with UPDATE and DELETE. Running them without it affects every row in the table — a mistake no one wants to make twice! 😅 SQL is one of the most in-demand skills in tech, and the good news? The basics take just a few hours to learn. Start small, practice on real datasets, and build from there. What was the first SQL query you ever wrote? Drop it in the comments! 👇 #SQL #Database #DataEngineering #LearnSQL #BackendDevelopment #TechTips #Programming #DataScience #CodingLife #100DaysOfCode

Going deep on SQL fundamentals reveals something surprising. The most common mistakes aren't complex JOIN errors or subquery disasters. They're the basics — and they catch everyone. Here are 5 SQL mistakes seen over and over again 👇 1. Using = instead of LIKE for pattern matching ❌ WHERE name = 'R%' ✅ WHERE name LIKE 'R%' 2. Using = NULL instead of IS NULL ❌ WHERE commission = null ❌ WHERE commission = NULL ✅ WHERE commission IS NULL 3. Putting ORDER BY before WHERE ORDER BY must always be the last clause in a query. Always. 4. Forgetting DISTINCT when duplicates ruin the output Seeing repeated rows? SELECT DISTINCT is often the fix. 5. Using DELETE without a WHERE clause This deletes ALL records from the table. Not just the one intended. All of them. 😬 --- Beyond mistakes, here are concepts worth understanding deeply: → DDL vs DML — CREATE/ALTER/DROP vs INSERT/UPDATE/DELETE → COUNT() vs COUNT(*) — one ignores NULLs, one doesn't → CHAR vs VARCHAR — fixed vs variable storage → HAVING vs WHERE — filtering groups vs filtering rows → Equi-Join vs Natural Join — how common columns appear in output SQL isn't hard. But the details matter more than most people think. #SQL #MySQL #DatabaseDevelopment #TechEducation #ComputerScience #Coding #LearningSQL #DataEngineering

SQL Looked Easy at First. Then Came Joins. The class that almost broke me and the lesson that came out of it. I will be honest with you. There was a moment in my last class where I genuinely considered whether this was for me. SQL started simple enough. Selecting columns, pulling records - manageable. Then the complexity arrived, fast and unannounced. SELECT, FROM - "This is fine." Extracting columns and records. Straightforward. I was feeling confident. WHERE, ORDER BY, GROUP BY, HAVING - "Okay, I am still here." Filtering and sorting data. It was getting tougher but I was keeping up. JOINS and Subqueries - "Wait. What?" Combining tables. Nesting queries inside queries. My brain had to work in ways it had never worked before. "Imagine writing a full query, staring at the screen and being too scared to hit Run."😅 That was me. More than once. And somehow that made me laugh and push through. 💡 What SQL Taught Me The biggest shift was learning to slow down before I type a single line. Understanding what the result should look like before writing the query is everything. Because in SQL, you can run a query, get a result that looks perfectly fine and still be completely wrong. That is the part nobody warns you about. Break the question down. Picture the output. Then query. Stressful? Absolutely. Worth it? Without a doubt. Every tool in this training has pushed me past a wall I did not know I had. SQL just happened to build the tallest one yet. Still standing. Still going. 🚀 Where did SQL start to click for you? You can share below Pushed through with the guidance of Obumneme Udeinya #SQL #DataAnalysis #LearningInPublic #SQLJoins #DataAnalyst #LearningInPublic #GrowthMindset #BeginnersJourney #LMTechHub #Cohort6

I started learning SQL and instead of waiting to be perfect, I created my own notes while practicing on SQL Workbench. They’re simple, not perfect—but they cover almost everything a beginner needs. Here’s a quick gist of what I learned: • Database – A structured collection of data stored electronically • Table Structure – Data organized in rows (records) and columns (fields) • Creating Database/Table – Using SQL queries to define and store data • SQL Commands – DDL (structure), DML (data), DCL (access), TCL (transactions) • Insert Data – Adding records into tables using "INSERT" • Keys – Primary Key (unique identifier) Foreign Key (connects tables) • Constraints – Rules like NOT NULL, UNIQUE to maintain data accuracy • Operators – Arithmetic, comparison, logical operations in queries • Clauses – WHERE (filter data) GROUP BY (group data) HAVING (filter groups) ORDER BY (sort results) • Aggregate Functions – COUNT, SUM, AVG → used for analysis • Foreign Key Cascading – Automatically updates/deletes related data • Joins – Combine data from multiple tables (INNER, LEFT, etc.) • UNION – Merge results of multiple queries • Subqueries – Query inside another query for complex logic • Views (MySQL) – Virtual tables created from queries • SQL vs NoSQL – SQL = structured, relational NoSQL = flexible, non-relational ⚠️ These notes are not perfect, but they helped me build clarity. You don’t need perfect notes—you just need to start and stay consistent. I’ll also share the resources/videos I used. video link 1:-https://lnkd.in/gS4kAqmg video link 2:-https://lnkd.in/gTCU9nR2 If you're learning SQL, this might help you revise quickly. Let’s keep growing 🚀 #SQL #DataAnalytics #LearnSQL #Upskilling #MySQL #Students #LearningInPublic #TechSkills #Consistency #CareerGrowth

💡 Handling NULLs in SQL — A Must-Know Skill NULL values are one of the most confusing parts of SQL… And also one of the most important. 🔹 NULL ≠ 0 🔹 NULL ≠ empty string 🔹 NULL = unknown If not handled properly, they can break your queries and give wrong results. ✔ Use IS NULL / IS NOT NULL ✔ Use COALESCE() for default values ✔ Understand how NULL affects JOINs & Aggregations 📌 Mastering NULL handling = Writing bug-free SQL queries #SQL #DataAnalytics #DataScience #Database #LearnSQL #DataEngineering

Writing the same SQL query again and again? Use 𝗩𝗶𝗲𝘄𝘀. A View is like a 𝘀𝗮𝘃𝗲𝗱 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝘆 that you can treat like a table. Instead of rewriting complex queries, you just do: 𝗦𝗘𝗟𝗘𝗖𝗧 * 𝗙𝗥𝗢𝗠 𝗮𝗰𝘁𝗶𝘃𝗲_𝘂𝘀𝗲𝗿𝘀_𝘃𝗶𝗲𝘄; Clean. Simple. Reusable. Why Views are powerful in complex queries:  • Hide complicated joins and logic  • Reuse the same query across multiple places  • Provide a simplified “read-only” layer  • Restrict access to sensitive data (security layer) Real-world example: Instead of writing a big query joining users + orders + payments… Create a view 𝗼𝗻𝗰𝗲, and use it 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. Now the important part What happens when you INSERT, UPDATE, DELETE? For simple views (single table, no aggregation) You can perform insert/update/delete For complex views (joins, group by, etc.) Mostly read-only Because the database can’t always figure out how to map changes back to original tables. Types of Views: 🔹 Simple View → Based on one table 🔹 Complex View → Multiple tables, joins, functions 🔹 Materialized View → Stores data physically (faster reads ⚡) But here’s the catch: Views don’t store data (except materialized ones) So performance depends on the underlying query. Real insight Views don’t just simplify queries… They simplify how you think about data. Next time your SQL looks messy, don’t rewrite it… 𝗪𝗿𝗮𝗽 𝗶𝘁. #Database #SQL #PostgreSQL #RelationalDatabase #QueryOptimization #BackendDevelopment #SoftwareEngineering #Developers #Programming #SpringFramework #SpringBoot #ScalableSystems #Microservices #aswintech