SQL Challenge: Find Customers with No Orders

I used to write SELECT * in almost every SQL query. Honestly, I never thought much about it it worked, so I just kept doing it. But after working with bigger datasets, I started noticing things getting slower and harder to manage. That’s when I realized: I was pulling way more data than I actually needed Now instead of: SELECT * FROM sales; I write only what’s required: SELECT customer_id, revenue FROM sales; It may look like a small change, but it actually made a difference: ✔ Queries run faster ✔ Data is easier to read ✔ Less unnecessary load Still learning to write better SQL step by step. Curious did you also start with SELECT * or just me? #SQL #DataAnalytics #LearningJourney #Performance

If your SQL uses DISTINCT to fix duplicates, you might be hiding a bigger problem. DISTINCT is one of the most misused keywords in SQL. Yes — it removes duplicates. But here’s the problem: Duplicates usually don’t come from nowhere. They come from your logic. Most times, it’s caused by: • Wrong JOIN conditions • 1-to-many relationships • Poor understanding of the data So when you write: SELECT DISTINCT * FROM table; You’re not fixing the issue. You’re masking it. A better approach is: 1.Understand why duplicates exist 2.Fix the JOIN or aggregation 3.Control the data at the source Because clean output doesn’t always mean correct logic. #SQL #DataAnalysis #Analytics #DataEngineering #LearningInPublic

💡 Challenge: Write an SQL query to identify numbers that appear 3 or more times consecutively. 🔥 Let’s make it interactive: Drop your SQL solution in the comments 👇 Try solving using Window Functions 🧠 Bonus: Can you solve it without using LAG() or LEAD()? 💬 Why this matters? This type of problem tests your understanding of: Sequential data patterns Window functions Real-world scenarios like user activity tracking 📈 🔗 If you're learning SQL & Data Analytics, I regularly share problems like this! #SQL #DataAnalytics #LeetCode #LearningInPublic #TechChallenge #DataScience #CareerGrowth

If you're still relying heavily on subqueries and GROUP BY for everything in SQL, it might be time to level up. Window functions in SQL Server allow you to perform powerful calculations like rankings, running totals, and trend analysis-while still retaining row-level data. This article walks through practical examples using functions like ROW_NUMBER(), RANK(), and more, making it easier to understand and apply in real-world scenarios. 📖 A must-read for anyone working with data. 👉 https://lnkd.in/gQmFTK5t #SQLServer #DataAnalytics #DataEngineering #Developers #LearnSQL #SQL #DataScience #TechSkills #Analytics #Database

This SQL query runs perfectly. No errors. Clean logic. Looks correct. But the result is completely wrong. Look at the query in the image. It tries to calculate total revenue per customer. And at first glance, everything seems fine. But there’s a subtle mistake in the aggregation logic that completely distorts the output. This is exactly how production dashboards get incorrect numbers — not because queries fail, but because they silently return misleading results. Your challenge: What is wrong with this query? Write the correct SQL in the comments. Follow Data Rejected for real-world SQL challenges. Repost if this could help someone preparing for interviews or debugging production queries. Subscribe on YouTube for full SQL breakdowns. #SQL #DataEngineering #AnalyticsEngineering #DataAnalytics #Database #LearnSQL #SQLTips #TechCareers #QueryOptimization #BusinessIntelligence #DataRejected

I used to write this in almost every SQL query: 👉 SELECT * It felt easy. Fast. Convenient. And for small tables… it worked fine. But things changed when I started working with larger datasets. Suddenly I noticed: ❌ Queries taking longer to run ❌ Unnecessary columns being loaded ❌ Increased memory usage ❌ Slower dashboards And the problem wasn’t obvious at first. The mistake? I was asking the database to return everything… even when I only needed 2–3 columns. So instead of: 👉 SELECT * FROM customers I changed it to: 👉 SELECT name, country FROM customers That small change made a big difference: ✔ Less data scanned ✔ Faster execution ✔ Cleaner results ✔ Better performance overall 💡 What I learned: Databases don’t “guess” what you need. They do exactly what you ask. And SELECT * is basically saying: 👉 “Give me everything, I’ll figure it out later” 📌 Lesson: SELECT * is okay for quick exploration… but in real-world scenarios, it’s a bad habit. Always be intentional about the data you retrieve. This was one of those small changes that completely improved how I write SQL. #SQL #DataEngineering #SQLTips #DatabaseOptimization #LearnSQL #Analytics #DataAnalytics #TechLearning #BestPractices

Practiced SQL Joins today using a sample orders dataset. Created separate customer, product, and orders tables, then used joins to combine them and analyze the data more effectively. This is helping me understand how raw data can be structured and queried in a more practical way. #SQL #Joins #DataAnalytics #DataAnalyst #LearningSQL

SQL Execution Order (not how we write it, but how it actually runs) Most of us write queries like this: SELECT → FROM → WHERE → GROUP BY → ORDER BY But internally, SQL processes it very differently. SQL executes in this order: FROM JOIN WHERE GROUP BY HAVING SELECT DISTINCT ORDER BY LIMIT Here’s a simpler way to think about it FILTER → SHOW → SORT → LIMIT What this actually means • FILTER → FROM, JOIN, WHERE, GROUP BY, HAVING (Define data + reduce it step by step) • SHOW → SELECT, DISTINCT (Choose what you want to display) • SORT → ORDER BY (Organize the result) • LIMIT → LIMIT / TOP (Control how much data you return) Once we start thinking in execution order, we stop “trial and error” and start writing SQL with confidence. If you’re working with SQL daily, this mental model makes a huge difference. #SQL #DataAnalytics #LearnSQL #SQLTips #DataEngineering #Analytics

✅ Solved a SQL problem on StrataScratch — Day 53 of my SQL Journey 💪 Data isn’t always clean… Sometimes it comes packed inside a single column 📦 Today’s problem was about analysing business categories — But the twist? Multiple categories were stored in one field. The approach: • Split comma-separated categories into individual rows • Used SUBSTRING_INDEX() to extract each category • Generated sequence numbers to iterate through values • Aggregated total reviews per category • Sorted to identify the most reviewed categories What I practised: • String manipulation in SQL • Handling multi-value fields • Using LENGTH + REPLACE for dynamic splitting • Transforming unstructured data into an analysable format What stood out — Real-world data is rarely perfect. Sometimes the problem isn’t analysis… It’s preparing the data so analysis becomes possible. Once you break structure out of chaos, insights start to appear naturally. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

Explored the fundamentals of SQL Joins and their role in combining data across multiple tables 🔗📊 From INNER, OUTER, SELF to CROSS JOIN — understanding when and how to use each type is key to ensuring accurate insights and efficient queries ⚡📈 The right join can transform raw data into meaningful information, while the wrong one can lead to inaccurate results ❗ Mastering joins is not just about syntax, but about understanding data relationships effectively 🧠💡 #SQL #DataAnalytics #LearningJourney #DatabaseManagement