SQL Basics and Functions Reference

Here's an even more concise version: 💥 SQL Quick Reference 1. Basics * SELECT: Picks columns. * FROM: Specifies the table. * WHERE: Filters rows (LIKE, IN, BETWEEN, AND, OR). * ORDER BY: Sorts results (ASC, DESC). * AS (Alias): Renames columns or tables. 2. Summarizing * Functions: COUNT(), SUM(), AVG(), MIN(), MAX(). * GROUP BY: Groups rows for aggregate functions. * HAVING: Filters after grouping. 3. Combining * INNER JOIN: Matching rows from both tables. * LEFT JOIN: All from left, matches from right. * RIGHT JOIN: All from right, matches from left. * FULL JOIN: All rows from both tables. hashtag #SQL hashtag #Data hashtag #Dataanalyst hashtag #Learning hashtag #Joins

I don’t write SQL right away when I get a dataset. Most people do this: Open the table → start querying I don’t. I start with questions. What does each column actually mean? Where could the data be wrong? What decision am I trying to support? Then I look for: missing values duplicates weird distributions Only after that… I start writing SQL. Because good analysis doesn’t start with queries. It starts with understanding. Most mistakes happen before the first query.

🚀 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

✅ 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

✅ 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

Stop memorizing SQL. Start understanding it. 🚀 From WHERE → GROUP BY → HAVING → ORDER BY → LIMIT, this cheat sheet covers the queries you’ll actually use daily. 📊 🔖 Bookmark it. 🧠 Practice it. 🏆 Master it. Sanjay Gatti #SQL #DataEngineering #Analytics #LearnSQL #TechCareers

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

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

The TOP function (or LIMIT, depending on your flavor of SQL) is your best friend for performance and data exploration. The Logic Flow: 1️⃣ Filter First: Use WHERE to grab only what you need. 2️⃣ Sort Always: Use ORDER BY so your "Top" rows actually mean something. 3️⃣ Slice Last: Use TOP / LIMIT to cut the noise. Pro-Tip: If you’re using SQL Server, don't forget WITH TIES. It ensures you don't accidentally exclude records that have the same value as your N^{th} row! Whether you’re optimizing a dashboard or just exploring a new schema, remember: Sort before you slice. 📊💻 #SQL #DataAnalytics #DataScience #Database #CodingTips #PerformanceOptimization

🧩 SQL Concepts… All in One Sheet Not too advanced, not too basic — just a clean and structured summary of important SQL concepts 👇 When you're learning SQL, the hardest part isn’t always understanding… It’s remembering what to use and when. That’s where something like this helps: ✔️ Basic queries & filtering ✔️ Joins (with visual understanding) ✔️ Aggregations & GROUP BY ✔️ Window functions ✔️ Subqueries, CTEs & more #sql #cheatsheet #data #analytics #datascience