SQL pattern recognition for consecutive activity detection

💡 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

🚀 Day 26/30 – SQL Challenge | Symmetric Pairs Today’s challenge was a really interesting one — finding symmetric pairs in a dataset. 🔍 What is a Symmetric Pair? Two rows are considered symmetric if: 👉 The first row’s X matches the second row’s Y 👉 And the first row’s Y matches the second row’s X In simple terms, pairs like (20, 21) and (21, 20) mirror each other. 💡 Key Learnings ✅ Understood how to compare rows within the same table ✅ Learned how to avoid duplicate outputs by maintaining order ✅ Handled tricky edge cases like pairs where both values are the same (e.g., 20,20) ✅ Improved logical thinking for real-world data relationships 📊 Sample Output • 20 20 • 20 21 • 22 23 🔥 This problem helped me realize how important data relationships and pairing logic are in real-world scenarios like matching transactions, network connections, and bidirectional mappings. #Day25 #30DaysSQLChallenge #SQL #LearningInPublic #HackerRank #Analytics

𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗕𝗘𝗚𝗜𝗡𝗡𝗘𝗥𝗦 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 (𝗣𝗮𝗿𝘁 𝟮) After building the fundamentals in Part 1, it’s time to move into advanced SQL concepts — the ones actually used in real-world data analysis  In this notebook (SQL Part 2), I covered: - GROUP BY & HAVING — Data summarization - Joins — Combining multiple tables - Subqueries — Query inside query - CASE Statements — Conditional logic - Window Functions — Advanced analytics - CTEs (Common Table Expressions) — Cleaner queries #SQL #DataScience #Analytics #LearningInPublic #AdvancedSQL

🚀 Advanced SQL Challenge: Mutual Relationships in Noisy Data Real-world data is messy — duplicates, repeated actions, and incomplete relationships are everywhere. This problem focuses on identifying true mutual connections (A ↔ B) while handling: Duplicate follow events Time-based interactions Self-follow and invalid cases Graph-like relationship noise 💡 It’s not just about joins — it’s about data cleaning + relationship modeling, which is a core skill tested in advanced SQL interviews If you can solve this correctly, you're thinking beyond SQL syntax and into real-world data systems. 🔗 Check it out here: https://lnkd.in/gH-uQMrU

Working with Self-Joins in SQL Self-joins can be a bit tricky to understand at first, but they are incredibly powerful when you need to compare rows within the same table. Here’s a simple way to understand and use self-joins: A self-join is a regular join, but the table is joined with itself. Use Cases:   - Comparing Rows: Compare rows within the same table.   - Hierarchical Data: Query hierarchical data, such as organizational charts or family trees. Self-joins can be powerful tools for analyzing relationships within the same table. Experiment with self-joins to see how they can help you query your data more effectively. Here is a code snippet to help you understand how `Self-Join` works: 👇 Found this helpful? Repost it! 🔁 Follow Akash AB for Practical Data Engineering #sql #datascience #dataengineering #dataanalytics #selfjoin

🚀#Day33/50 #LeetCodeChallenge Today I learned a powerful SQL pattern — Consecutive Rows / Sequence Detection 💡 Problem: Find numbers that appear at least 3 times consecutively in a table. 👉 This problem helped me understand how real-world data patterns are identified and analyzed. 🔍 Key Learnings: Using LAG() to access previous rows Difference between Self Join vs Window Functions How SQL can detect sequences and repeated patterns ⚡ Approaches I explored: Self Join (classic approach) Window Functions (optimized using LAG) 📌 Most important takeaway: Consecutive problems in SQL = ➡️ LAG / LEAD ➡️ ROW_NUMBER ➡️ Self Join 🧠 Practice Problems: Finding consecutive numbers Detecting sequences (like 1-2-3, 7-8) Real-world use cases (login streaks, attendance tracking) 🔥 Getting more confident with SQL patterns every day! #Day33 #SQL #DataAnalytics #LearningInPublic #100DaysOfCode #WindowFunctions #SelfJoin

🧹 Day 14: Clean Data is Happy Data! Today’s SQL session was all about String Manipulation. In the real world, text data is rarely perfect. It has extra spaces, inconsistent casing, or needs to be combined for better reporting. My toolkit for today: UPPER/LOWER: Standardizing categories for consistent filtering. TRIM: Removing those pesky leading and trailing spaces that break queries. CONCAT: Merging columns (like Product + Category) into readable strings. REPLACE: Bulk-updating text patterns (e.g., changing "phone" to "device"). SUBSTRING/LEFT/RIGHT: Extracting specific parts of a string for shorter identifiers. Formatting might seem small, but it’s the difference between a messy database and a professional report! 📊 #SQL #DataCleaning #DataAnalytics #100DaysOfCode #DatabaseManagement #TechSkills

don't wrt query 🚀 Day 34 – Top 50 SQL Questions Today’s problem: Product Price at a Given Date 🔍 Objective: Find the price of each product on a specific date. 💡 Approach: Used a correlated subquery to get the latest price before the given date Handled missing data using default values Focused on retrieving time-based records efficiently 📈 Key Insight: Selecting the most recent valid record is a key pattern in SQL interview questions. #SQL #DataAnalytics #LeetCode #ProblemSolving #Day34

Day 2 of My Data Journey – From Confusion to Clarity with SQL Good morning everyone! Yesterday, I had a simple goal — get better at SQL. But today, I realized it’s not just about writing queries… it’s about thinking like data. I spent time practicing: . JOINs – understanding how different tables connect . GROUP BY – turning raw data into insights . Window Functions – getting closer to real-world scenarios At first, things felt confusing 🤯 But as I practiced more, patterns started making sense… and that’s where the magic happens Big Learning Today: SQL is not about syntax — it’s about how you approach a problem and break it down step by step. Still learning. Still improving. But one thing is clear — consistency beats everything Let’s keep going 🚀 #SQL #DataEngineering #DataAnalytics

This query runs perfectly. No errors. Clean logic. Looks correct. But the result is NOT reliable. Look closely at the query in the image. It tries to get the latest successful payment per customer using ROW_NUMBER(). Seems right… but there’s a hidden issue. When multiple payments happen on the same day, this query can return inconsistent results across runs. This is a classic production bug — everything works, but the output is non-deterministic. Your challenge: What is wrong with this query? How would you fix it? Comment your SQL below. Follow Data Rejected for real-world SQL challenges. Repost if this could help someone preparing for interviews or debugging production issues. Subscribe on YouTube for full SQL breakdowns. #SQL #DataEngineering #Analytics #DataAnalytics #LearnSQL #SQLTips #QueryOptimization #BigData #TechCareers #DataScience #DataRejected