Nick Holt’s Post

Most people think learning SQL is just about writing queries. But the real gap shows up in the mistakes we repeat every day. 10 common SQL mistakes that quietly hurt performance: • Using SELECT * instead of only needed columns • Ignoring indexes and slowing down queries • Writing JOINs without understanding relationships • Not handling NULL values properly • Confusing WHERE with HAVING • Forgetting LIMIT while testing queries • Not optimizing subqueries • Poor query readability and formatting • Assuming data is always clean • Not testing on large datasets The issue isn’t SQL. It’s how we think while writing it. Most people focus on getting the output. Few focus on getting it right, fast, and scalable. Good SQL = correct + efficient + readable + reliable That’s what separates beginners from professionals. #SQL #DataAnalytics #Learning #Tech #Mistakes #CareerGrowth

About 5 years ago, I started learning SQL. At first, it felt overwhelming — so many commands, joins, subqueries, and ways to structure data. But over time, I realized that learning SQL became much faster once I changed my approach. Here are a few methods that helped me speed up the process: 1️⃣ Learn by solving real problems Instead of only reading theory, I practiced with datasets and tried to answer business questions. Real scenarios make concepts stick. 2️⃣ Master the fundamentals first SELECT, WHERE, GROUP BY, ORDER BY, JOINs — once the basics became natural, advanced topics were much easier to understand. 3️⃣ Practice consistently Even 20–30 minutes a day was more effective than one long study session per week. Repetition builds confidence. 4️⃣ Read other people’s queries Looking at how experienced analysts write SQL taught me cleaner logic and more efficient solutions. 5️⃣ Break complex queries into smaller steps Using CTEs and temporary logic blocks made difficult tasks much easier to manage and debug. 6️⃣ Focus on understanding data, not memorizing syntax Syntax can always be checked. What matters most is understanding how tables relate and how to think logically. Looking back, SQL was one of the most valuable skills I invested time in learning. It opened doors to analytics, automation, and better decision-making. If I were starting again today, I’d still use the same strategy: practice daily, stay curious, and build projects with real data. What helped you learn SQL faster? #SQL #DataAnalytics #Learning #CareerGrowth

The five mistakes I see almost every beginner make when learning SQL. I've worked with enough students now to know that certain mistakes come up again and again. None of them are signs of low ability. They're just patterns - and once you know to watch for them, they're easy to avoid. The first mistake is trying to memorise syntax before understanding logic and purpose. SQL is a language for interrogating data, and if you understand the question you're trying to ask, the syntax becomes much easier to look up and retain. If you're just memorising commands without knowing why, nothing sticks. The second is avoiding messy datasets. Courses give you clean, perfectly structured data. Real databases don't. If you only ever practise on tidy data, you'll be completely unprepared for the NULLs, duplicates, and inconsistent formatting that show up in every real role. The third one is never reading the error messages properly. I see students panic the moment they get an error message, but they can be actually quite helpful - they usually tell you exactly what went wrong and where. Train yourself to read them carefully before Googling or asking for help. The fourth is letting joins ruin your day. Joins feel intimidating at first, so a lot of beginners panic when needed to use them. My advice is to tackle them head on, and only focus on inner and left joins. They're the core of relational data work and the sooner you get comfortable with them, the better. Number five - and probably the most costly - is covering topics in isolation. Yes I can do joins, yes I can do date functions, yes I can do unions, CTEs, string functions whatever. But in real life, you need to combine all these things. Don't put off putting it all together to achieve a goal. Any of these sound familiar? Drop a comment - I'd like to know if any of these have been giving you a headache. #learnsql #sql #dataanalyst #datacareer #sqltips

🚀 SQL Learning Journey — Leveling Up My Thinking After covering the fundamentals of SQL, I’ve started realizing something important: Writing queries is not the hardest part… 👉 Thinking through the problem is. Initially, I focused on syntax — SELECT, WHERE, GROUP BY, etc. But now, while solving questions, I’m learning how to: • Break complex problems into smaller steps • Decide when to use Subqueries vs Joins • Understand how data flows inside a query • Think logically before writing a single line of SQL 📊 Recent Progress: Solved SQL problems where instead of memorizing queries, I focused on understanding the logic behind them. 💡 Currently Exploring: • Joins (connecting multiple tables) • Advanced Subqueries • Query optimization mindset 🎯 Goal: To become confident in solving real-world data problems, not just practice questions. One thing I’ve learned so far: 👉 “If you can think clearly, you can write the query.” Still learning. Still improving. Open to tips, feedback, and connections in Data Analytics 🤝 #SQL #DataAnalytics #LearningInPublic #ProblemSolving #CareerGrowth

Learning SQL isn’t just about memorizing commands; it’s about learning how to talk to data so it actually tells you something useful. For those just starting out, SQL (Structured Query Language) can feel like a wall of code. But at its core, it is simply a way to filter through the noise. Most beginners jump straight into complex joins, but the real "superpower" for a Data Analyst is mastering the Logical Order of Execution. The SQL "Brain" vs. The SQL "Code" When you write a query, you usually start with SELECT. However, the database engine doesn't start there. Understanding this order is the secret to debugging your code: 1. FROM / JOIN: The database first looks at where the data lives. 2. WHERE: It filters individual rows (e.g., "only show me active customers"). 3. GROUP BY: It bundles the data (e.g., "group these customers by region"). 4. HAVING: It filters those bundles (e.g., "only show regions with > 100 customers"). 5. SELECT: Only now does it pick the specific columns you asked for. 6. ORDER BY: Finally, it sorts the result. Pro Tip: If you try to use an alias (like SELECT Price * 1.1 AS NewPrice) in a WHERE clause, your code will break. Why? Because the WHERE step happens before the SELECT step has even created that alias! To the seasoned pros: What was the one SQL concept that finally "clicked" for you and changed the way you worked? Let's help the next generation of analysts in the comments! #SQL #DataAnalytics #LearningData #TechTips #DataScience #CareerAdvice

🚀🌟 SQL Learning Roadmap – Step by Step Journey to Mastering Databases I’ve started my journey to mastering SQL, and here’s the structured roadmap I’m following to build strong fundamentals and advance towards real-world applications. Sharing it step-by-step for anyone who is also starting 👇 🔰 Step 1: Basics Understanding the foundation is key. • What is SQL • Databases & Tables • Data Types • CRUD Operations (Create, Read, Update, Delete) 🔍 Step 2: Queries Learning how to retrieve and filter data efficiently. • SELECT Statements • WHERE Clauses • ORDER BY, GROUP BY • LIMIT, DISTINCT ⚙️ Step 3: Functions Using built-in functions to manipulate data. • Aggregate Functions: COUNT(), SUM(), AVG() • String Functions: UPPER(), LOWER(), CONCAT() • Date Functions: NOW(), DATE(), DATEDIFF() 🔗 Step 4: Joins Combining data from multiple tables. • INNER JOIN • LEFT JOIN • RIGHT JOIN • FULL JOIN • SELF JOIN 🧠 Step 5: Subqueries Writing queries inside queries for better logic. • Subqueries in SELECT, FROM, WHERE • Correlated Subqueries 🛡️ Step 6: Constraints Ensuring data integrity. • PRIMARY KEY • FOREIGN KEY • UNIQUE, NOT NULL, CHECK ⚡ Step 7: Indexes & Views Improving performance and simplifying queries. • Indexing for speed • Creating & Using Views 🧩 Step 8: Normalization Designing efficient databases. • 1NF, 2NF, 3NF • Avoiding Data Redundancy 🔄 Step 9: Transactions Managing data safely. • BEGIN, COMMIT, ROLLBACK • ACID Properties 📈 Step 10: Advanced SQL Leveling up skills. • Stored Procedures • Triggers • Window Functions • CTEs (WITH Clause) 🎯 Step 11: Practice & Projects Applying knowledge in real scenarios. • Build projects • Solve real-world problems • Keep learning consistently 💡 SQL is not just a skill, it’s a powerful tool for data-driven decision making. If you’re also learning SQL, let’s connect and grow together! 🤝 #SQL #DataAnalytics #DataScience #LearningJourney #Database #TechSkills #CareerGrowth #Students #LinkedInLearning

As a beginner in SQL, today’s practice really stretched me, but in a good way. I’m starting to realize that SQL isn’t just about writing commands, it’s about understanding the data and asking the right questions. Here’s what I worked on today: I found customers who rented more films than the average. At first, it felt confusing, but it helped me understand how to break a problem into steps and get meaningful results. I also made sure to show their names instead of just IDs, which made the result easier to understand. I connected different tables to get more complete information. For example, I was able to see not just customers, but also their city and country based on their rental activity. That really showed me how everything in a database is linked. I used Common Table Expressions (CTEs) to organize my query better. Instead of writing one long, messy query, I broke it into parts and it made everything much clearer. 💡 One thing I’m learning is that SQL is less about memorizing and more about thinking step by step. Still learning, still improving but definitely making progress. #SQL #BeginnerJourney #LearningSQL #DataAnalytics #Growth #LMLM Tech Hub

Understanding SQL Execution Order (Step-by-Step) If you’re learning SQL, this is something that can completely change how you write queries 👇 Most beginners think SQL runs like this: 👉 SELECT → FROM → WHERE ❌ But in reality, SQL follows a completely different execution order: ✔️ FROM + JOIN → Build the dataset ✔️ WHERE → Filter rows ✔️ GROUP BY → Create groups ✔️ HAVING → Filter groups ✔️ SELECT → Choose columns ✔️ DISTINCT → Remove duplicates ✔️ ORDER BY → Sort results ✔️ LIMIT → Restrict output 💡 Simple Memory Trick: 👉 Build → Reduce → Format Build → FROM, JOIN Reduce → WHERE, GROUP BY, HAVING Format → SELECT, DISTINCT, ORDER BY, LIMIT 📌 Why this matters? Because understanding this: Helps you debug queries faster Improves query optimization Makes you think like a real data professional If you're learning SQL, this is a must-know concept 🚀 👉 Save this for later 👉 Share with someone learning SQL #SQL #DataAnalytics #DataScience #LearnSQL #SQLTips #Database #Programming #TechLearning #DataEngineering #Analytics #Coding #LearnToCode #CareerGrowth #LinkedInLearning #TechCareer #SoftwareEngineering #DataProfessionals

Understanding SQL Execution Order (Step-by-Step) 🚀 If you're learning SQL, this concept can completely change how you write and debug queries 👇 Most beginners assume SQL executes like this: 👉 SELECT → FROM → WHERE ❌ But the actual execution order is: ✔️ FROM + JOIN → Build the dataset ✔️ WHERE → Filter rows ✔️ GROUP BY → Group data ✔️ HAVING → Filter grouped results ✔️ SELECT → Choose required columns ✔️ DISTINCT → Remove duplicates ✔️ ORDER BY → Sort results ✔️ LIMIT → Restrict output 💡 Simple way to remember: 👉 Build → Reduce → Format Build: FROM, JOIN Reduce: WHERE, GROUP BY, HAVING Format: SELECT, DISTINCT, ORDER BY, LIMIT 📌 Why this matters? Helps debug queries faster Improves query optimization Builds a strong foundation in data handling Understanding execution order helps you think like a developer, not just someone writing queries 💡 👉 Save this for future reference 👉 Share with someone learning SQL #SQL #Database #LearningInPublic #TechGrowth #DeveloperJourney

Learning SQL feels like solving a puzzle—each command is a new piece that makes the bigger picture clearer. Over the past few weeks, I’ve been steadily building my SQL skills, and I’m proud to share the progress I’ve made: ✔️ CREATE – building new tables and databases ✔️ ALTER – modifying structures and column types ✔️ UPDATE – changing existing data in rows ✔️ SELECT – retrieving and exploring data ✔️ DELETE – removing specific records ✔️ DROP – removing entire tables or columns ✔️ TRUNCATE – quickly clearing all rows and resetting identities Each step has boosted my confidence in managing and structuring data. It’s exciting to see how these building blocks come together to form a strong foundation in SQL. 🌱 My next milestone: diving deeper into Joins, Aggregate functions, and Constraints to unlock even more possibilities. I believe consistent learning and curiosity are the keys to growth, and I’m eager to continue this journey. 👉 If you’re also exploring SQL or working with databases, let’s connect and share insights! #SQL #LearningJourney #ContinuousLearning #DatabaseManagement #GrowthMindset