SQL Performance Optimization with TOP and LIMIT

Your SQL query works… but is it RIGHT? 👇 One of the biggest mistakes analysts make: Trusting results without validating them. Always ask: Does this number make sense? Can I cross-check it? Am I double-counting? SQL is not just querying. It’s thinking. Have you ever caught a wrong query result? 😅 #SQL #DataAnalytics #AnalyticsMindset #DataQuality

Day 3 of Building in Public. Today I revisited temp tables in SQL. Whether you’re cleaning messy data or optimizing a complex dashboard, Temporary Tables are the "scratchpads" every SQL pro needs in their toolkit. 🛠️ Think of them like a Post-it note for your database. Temp tables enable one to: ✅ Store intermediate results. ✅ Simplify massive queries. ✅ Boost performance by breaking down logic. Below is a sample of the temp table I created. #DataAnalytics #Buildinginpublic #Techcommunity #SQL

✅ Solved a SQL problem on StrataScratch — Day 54 of my SQL Journey 💪 User activity looks simple… until you try to measure it correctly ⏱️ Today’s problem was about calculating average session time — But sessions weren’t explicitly given. They had to be built from events. The approach: • Identified session boundaries using page_load and page_exit • Used MIN() and MAX() with CASE WHEN to capture valid timestamps • Calculated session duration using TIMESTAMPDIFF() • Filtered out invalid sessions (where load happens after exit) • Averaged session time per user What I practised: • Event-based session reconstruction • Conditional aggregation using CASE WHEN • Time difference calculations in SQL • Data cleaning before aggregation What stood out — Metrics don’t exist in raw data. You have to build them. A “session” isn’t stored anywhere… It’s something you define from behaviour. That’s where analysis actually begins. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

Today’s Practice: Exploring a database without touching the data. One thing I’ve been building with is that you don’t always need to query the data itself. You can query the structure of the database too. In this example, I’m using information_schema.columns, which is like a master list of every column in the database. The SELECT is asking to see the column names and their data types. The FROM is pointing to where that metadata lives. And the WHERE is narrowing it down to just one table in the public schema. So instead of pulling actual records, I’m exploring how the table is built. This is a great way to get familiar with new databases and understand what you’re working with before writing deeper queries. #SQL #DataAnalytics #DatabaseDesign

✅ Solved a SQL problem on StrataScratch — Day 52 of my SQL Journey 💪 Numbers don’t mean much… until you compare them over time 📊 Today’s problem was about calculating the month-over-month percentage change in revenue — a simple metric, but one that tells a powerful story. The approach: • Aggregated total revenue per month • Joined each month with its previous month • Calculated percentage change using the classic MoM formula • Handled NULL cases for the first month cleanly What I practised: • Time-based aggregation using DATE_FORMAT() • Self joins for period comparison • Percentage calculations in SQL • Handling edge cases in analytical queries What stood out — Raw numbers show what happened. Comparisons show what changed. A revenue of 10K means nothing alone… but +25% or -10% tells the real story. That shift — from totals to trends — is what turns data into insight. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

Stop trying to filter aggregated data with a WHERE clause! 🛑 If you’ve ever tried to filter a GROUP BY result and watched your SQL query throw an error, you probably needed the HAVING clause. While WHERE filters individual rows before they are grouped, HAVING filters the groups after the aggregations are calculated. In my latest SQL tutorial, I break down: The fundamental difference between WHERE and HAVING. How to use HAVING with functions like SUM(), COUNT(), and AVG(). Real-world scenarios where this clause is a lifesaver. To Check out the full video click the link in the comments below #SQL #DataAnalytics #DataEngineering #LearningSQL #Database

📊 SQL Revision Day – Strengthening the Foundations Today, I focused on revising core SQL concepts by working on a dummy cellular company dataset. 🔍 Concepts I practiced: ✔️ SELECT & data filtering using WHERE (AND / OR, LIKE) ✔️ Sorting data using ORDER BY ✔️ Aggregating insights using GROUP BY ✔️ Combining multiple tables using INNER JOIN 💡 What stood out: Understanding how data connects across tables using JOINs really helps in seeing the bigger picture — especially when analyzing customers, plans, and subscriptions together. 📈 This hands-on revision is helping me move beyond theory and build practical confidence in SQL. #SQL #DataAnalytics #LearningJourney #DataSkills #BusinessAnalysis

Day 26/90 — SQL Series | Week 4 "My WHERE clause is not filtering dates correctly." "My SUM is returning NULL instead of a number." 9 times out of 10 — it's a data type mismatch. CAST and CONVERT fix it. CAST('250' AS INT) → turns text into number so you can do math CAST('2024-01-15' AS DATE) → turns text into date so filters work CAST(order_id AS VARCHAR) → turns number into text for concatenation Rule: use CAST (works everywhere). Use CONVERT only when you need date formatting in SQL Server. #SQL #CastConvert #DataAnalytics #LearnSQL #DataAnalyst

🚀 Day 33 – Top 50 SQL Questions Today’s problem: Consecutive Numbers 🔍 Objective: Find numbers that appear at least three times consecutively in a dataset. 💡 Approach: Used self joins on the Logs table Matched rows based on consecutive id values Compared num values across three rows Applied DISTINCT to ensure unique results 🛠️ SQL Query: SELECT DISTINCT l1.num AS ConsecutiveNums FROM Logs l1 JOIN Logs l2 ON l1.id = l2.id - 1 JOIN Logs l3 ON l1.id = l3.id - 2 WHERE l1.num = l2.num AND l2.num = l3.num; 📈 Key Insight: Self joins are highly effective for identifying sequential patterns in relational data. #SQL #DataAnalytics #LeetCode #ProblemSolving #Day33

Stop scrolling and Save This. Because no one has time for a 4-hour tutorial when you just need the data now. Here’s the 80/20 of SQL: 👁️ SELECT & WHERE → See only what you need 📊 GROUP BY → Summarize the mess 🔗 JOINs → Stop copy-pasting between Excel sheets 🧮 Aggregates → Count, Average, Sum (Done) 🛑 Golden Rule: Avoid SELECT * in production. Your DBA will thank you. What’s the one SQL command you use the most? #SQL #DataAnalytics #TechTips #CareerGrowth