Pratyasha Bhanu’s Post

Good day, Everyone! 😳 “My query returned 2534 rows… then suddenly 0.” Yesterday, one of my students ran a simple SQL filter: 👉 WHERE LOWER(LTRIM(RTRIM(status))) != 'cancelled' Before applying the filter: ✅ 2534 rows After applying the filter: ❌ 0 rows He looked at me and said: “Sir… SQL is broken.” I smiled and asked him one question: 👉 “How many of those 2534 rows have NULL in status?” Silence. 💡 Here’s what actually happened: In SQL, NULL is not a value. It’s unknown. So when you write: 👉 NULL != 'cancelled' It doesn’t return TRUE. It doesn’t return FALSE. It returns… ❓ UNKNOWN And SQL’s WHERE clause only keeps TRUE. 🚨 Result? All NULL rows are silently removed. And if the remaining rows were actually 'cancelled'… 👉 You get 0 rows 🎯 That one concept leads to powerful interview questions: 1️⃣ Why does NULL != 'cancelled' fail? 2️⃣ What are the 3 logical states in SQL? 3️⃣ How do you fix this condition? 4️⃣ When should you use IS NULL vs COALESCE? 5️⃣ How do you debug such issues in real projects? 🔥 Real learning: “Most SQL bugs are not syntax errors… they are logic errors.” If you’re learning SQL or teaching it — understanding NULL will save you hours of debugging. 💬 Comment “NULL” and I’ll share the correct query fix + explanation. #SQL #DataEngineering #MicrosoftFabric #Learning #DataAnalytics #AnalyticsExplainedByHarish

✅ Solved a SQL problem on StrataScratch — Day 57 of my SQL Journey 💪 Text looks simple…   until you try to count words inside it 👀  Today’s challenge: count exact occurrences of specific words — not substrings, but precise matches.  The approach:  • Normalised text using LOWER()   • Used REGEXP with word boundaries (\b) for exact matching   • Replaced matches and compared string lengths   • Derived counts using length difference logic   • Combined results using UNION  What I practised:  • REGEXP for pattern matching   • String manipulation with LENGTH & REPLACE   • Handling edge cases like “bull” vs “bullish”   • Translating text problems into SQL logic  What stood out —  Text data looks simple,   But precision changes everything.  Small variations completely change the meaning.  That’s where careful querying matters.  SQL isn’t limited to numbers —   It can handle text if you think right.  Consistent learning, one query at a time 🚀  #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

🚀 Day 39/100 – LeetCode SQL Challenge 🔹 Problem: Find Valid Emails Today’s challenge was about filtering valid email addresses using SQL based on specific rules. 📌 Problem Requirements: Exactly one @ symbol Must end with .com Before @ → only letters, numbers, underscore Domain name → only letters 💡 My Approach: Used REGEXP for pattern matching Defined a strict pattern to validate email format Applied filtering conditions directly in SQL query Sorted the result using ORDER BY user_id 🧠 What I Learned: Difference between LIKE and REGEXP How to write regex patterns in SQL Importance of escaping special characters like . Real-world use of SQL in data validation ⚡ Key Insight: Simple-looking problems can test your understanding of string patterns and edge cases deeply. ✅ Step by step improvement in SQL skills! #Day39 #LeetCode #SQL #DSA #CodingJourney #PlacementPreparation

SQL is 50 years old and still the most underated skill in tech. 👀 I've seen people with 5 years of experience who can't write a proper JOIN. And I've seen analysts with zero CS degrees outperform engineers just because they mastered SQL. Quick cheat sheet that took me years to internalize: 📋 🔹 Use WHERE before GROUP BY to filter rows early 🔹 HAVING is for filtering AFTER aggregation 🔹 Window functions (ROW_NUMBER, RANK, LAG) > subqueries 90% of the time 🔹 EXPLAIN ANALYZE is your best friend before pushing any query to prod 🔹 Index your JOIN columns. Always. 🙏 SQL doesn't care about your framework or your favourite language. Bad queries will tank your app regardless. ⚡ What SQL trick took you way too long to learn? 👇 #SQL #Database #BackendDevelopment #SoftwareDevelopment #DataEngineering

🚀 Day 39 of My SQL Learning Journey Today I worked on a SQL problem involving pattern matching 🔥 🔹 Problem: Find users with valid email addresses 🔗 Problem Link: https://lnkd.in/gWAN__EG 🔹 Solution: SELECT * FROM Users WHERE mail REGEXP '^[A-Za-z][A-Za-z0-9_.-]*@leetcode\\.com$'; 🔹 Key Learning: Using REGEXP for pattern matching Validating strings using conditions Real-world use case: email validation 💡 SQL can handle complex string validations efficiently! Consistency continues 🚀 #SQL #LeetCode #90DaysOfCode #CodingJourney #ProblemSolving

✅ Solved a SQL problem on LeetCode — Day 44 of my SQL Journey 💪 IP addresses look structured… but validating them isn’t always simple 🌐 Today’s problem was about identifying invalid IP addresses — not just formatting issues, but logical errors inside each segment. I used string functions and conditional checks to: • Split IP into individual octets using string operations • Validate the number of segments (must be 4) • Check if each octet is within the valid range (0–255) • Detect non-numeric values using pattern matching • Count how often each invalid IP appears What I practised: • Working with string parsing in SQL • Using SUBSTRING_INDEX() for segment extraction • Applying REGEXP for pattern validation • Combining multiple validation rules in one query What stood out — Validation isn’t just about format… it’s about the rules behind the format. A value can look correct, but still logically invalid. That’s where careful checking matters. SQL isn’t just for analysis. It can also enforce data quality. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic

Day 2 of 7: Learning SQL in Public Today, I went deeper into the WHERE clause, which is used to filter data. Example: SELECT * FROM customers WHERE points > 300; This returns customers with more than 300 points. A few key things I learned: When working with strings (text), values must be in quotes Dates are also written inside quotes I also explored different operators used with the WHERE clause: AND → both conditions must be true OR → at least one condition must be true NOT → excludes a condition IN → checks if a value exists in a list BETWEEN → filters within a range LIKE → used to search for patterns in text Examples: Using AND WHERE points > 300 AND state = 'CA'; Using IN WHERE state IN ('CA', 'NY', 'TX'); Using BETWEEN WHERE points BETWEEN 100 AND 300; Using LIKE WHERE name LIKE 'A%'; -- starts with A WHERE name LIKE '_a%'; -- second letter is 'a' I also learned about REGEXP (Regular Expressions), which are more powerful than LIKE for complex pattern matching. Examples: WHERE name REGEXP '^A'; -- starts with A WHERE name REGEXP 'a$'; -- ends with a WHERE name REGEXP 'A|B'; -- contains A or B WHERE name REGEXP '[a-h]'; -- letters from a to h Key takeaway: Filtering data is where SQL becomes really powerful. On to Day 3 🚀 #SQL #DataAnalytics #LearningInPublic #TechJourney #Day2

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call me old fashioned but there’s still something thrilling about writing a sql query solo on a tight deadline. character building.

✅ Solved a SQL problem on LeetCode — Day 42 of my SQL Journey 💪 Reactions look random… but behaviour usually isn’t 🎯 Today’s problem was about identifying emotionally consistent users — people who tend to react the same way across different content, not just react more. The approach: • Counted total reactions per user and per reaction type • Used a window function inside aggregation to get totals in one pass • Calculated the ratio of the dominant reaction to total reactions • Filtered users where the ratio crossed 60% • Used RANK() to identify the most frequent reaction per user What I practised: • Using window functions inside aggregations • Applying RANK() to capture dominant behaviour • Ratio-based filtering instead of just counts • Translating consistency into a measurable SQL condition What stood out — Consistency isn’t about reacting more… it’s about reacting the same way, repeatedly. Once you frame it as a ratio problem, the pattern becomes clearly visible. That’s where the real insight lies. Also noticed — SQL syntax isn’t universal. Things like ::numeric or QUALIFY don’t work in MySQL. Small difference, but important to keep in mind. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #WindowFunctions #LearningInPublic