Common SQL Functions Used in Data Engineering

I reviewed 200 SQL submissions from data engineering candidates last year. 90% had the same problem — and it wasn't wrong answers. They were writing SQL to get results. Senior engineers write SQL their teammates can debug at 3am during an incident. That's the gap nobody talks about. These are the 7 patterns that make the difference: 𝟬𝟭 — 𝗪𝗶𝗻𝗱𝗼𝘄 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 — stop writing subqueries that run once per row. SUM() OVER (PARTITION BY...) does it in one scan. 𝟬𝟮 — 𝗟𝗔𝗚 / 𝗟𝗘𝗔𝗗 — stop self-joining tables to compare rows. Two lines of window syntax replaces 12 lines of JOIN logic. 𝟬𝟯 — 𝗚𝗮𝗽𝘀 & 𝗜𝘀𝗹𝗮𝗻𝗱𝘀 — date minus ROW_NUMBER creates a constant for consecutive dates. This one pattern solves 80% of streak problems. 𝟬𝟰 — 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 — COUNT(DISTINCT CASE WHEN channel='paid' THEN user_id END) gives you a full pivot in one scan, zero PIVOT syntax. 𝟬𝟱 — 𝗦𝗺𝗮𝗿𝘁 𝗱𝗲𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 — never SELECT DISTINCT in production. ROW_NUMBER() OVER (PARTITION BY id ORDER BY updated_at DESC) encodes your business rule. 𝟬𝟲 — 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗧𝗘 — org trees, hierarchies, graph traversal. Always add WHERE depth < N. Without it, cyclic data crashes your job every time. 𝟬𝟳 — 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 — LAG detects the inactivity gap. Cumulative SUM assigns the session ID. Two window functions. One scan. No self-join. The real insight: Every one of these replaces a slow, hard-to-read subquery or self-join with a single readable window function. 𝗧𝗵𝗮𝘁 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘀𝗲𝗻𝗶𝗼𝗿𝘀 𝗿𝗲𝘃𝗶𝗲𝘄 𝗳𝗼𝗿. 𝗡𝗼𝘁 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗻𝗲𝘀𝘀. 𝗥𝗲𝗮𝗱𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. Save this image before your next SQL interview or code review. Which of these 7 do you still reach for last — and which one completely changed how you write SQL? Drop it in the comments 👇 #DataEngineering #SQL #DataEngineer #WindowFunctions #SQLInterview

🔷 SQL Cheat Sheet for Data Engineers & Analysts 🔷 Mastering SQL is a must-have skill for anyone in data — whether you're working in analytics, backend, or data engineering. I’ve created this simple SQL cheat sheet covering all the essential concepts in one place: ✔️ Basic Commands (SELECT, INSERT, UPDATE, DELETE) ✔️ Filtering & Sorting Data ✔️ Joins (INNER, LEFT, RIGHT, FULL, CROSS) ✔️ Aggregations & Grouping ✔️ Subqueries & Set Operations ✔️ Indexing & Transactions ✔️ Views, Triggers & CTEs ✔️ Window Functions (RANK, ROW_NUMBER, etc.) ✔️ Date & Time Functions ✔️ Conditional Logic 💡 Whether you're preparing for interviews or working on real-world data pipelines, this will help you revise quickly. Save it for later and share with someone who is learning SQL 🚀 #SQL #DataEngineering #MySQL #BigQuery #Database #Analytics #LearnSQL #TechLearning #DataAnalytics #DataEngineer #100DaysOfCode

Most people think SQL is just about writing queries. But real difference comes from 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗮𝘁 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗶𝗺𝗲. Over the years, I’ve seen one thing very clearly: The better your SQL patterns are, the better your thinking becomes as a Data Engineer. Whether you are building pipelines, debugging data issues, optimizing reports, or preparing for interviews, some SQL concepts come up again and again. That’s why I put together this quick visual on: Top 10 SQL Patterns Every Data Engineer Must Know It covers patterns like: **Joins, CTEs, Window Functions, Aggregations, Subqueries, CASE WHEN, Ranking Functions, Running Totals, Deduplication, and Date-based Analysis** These are practical patterns we use in real projects when working with messy data, business logic, reporting needs, and performance challenges. If your SQL foundation is strong, your data engineering work becomes much easier and much cleaner. A lot of people keep learning tools. But many times, better SQL itself can solve the problem faster. Which SQL pattern do you use the most in your day-to-day work? For me, CTEs and Window Functions are absolute game changers. Download Data Engineering 𝗦𝗤𝗟 𝗞𝗜𝗧 here: https://lnkd.in/g_V8gDg3? Join My Telegram Channel here: https://lnkd.in/g88ic2Ja #SQL #DataEngineering #DataEngineer #Analytics #ETL #BigData #Database #TechCareers #DataAnalytics #LearnSQL

40 Important SQL Commands and Clauses Every Data Professional Must Know 👇 SQL isn't just a query language. It's the foundation of every data-driven role in tech. Whether you're a data analyst, engineer, or developer - these 40 commands will cover every scenario you'll face at work. Save this and share it with someone learning SQL. Data Retrieval & Filtering SELECT · WHERE · DISTINCT · LIMIT · ORDER BY · GROUP BY · HAVING · BETWEEN · IN · LIKE · IS NULL Data Manipulation INSERT INTO · UPDATE · DELETE · TRUNCATE Table & Database Management CREATE TABLE · ALTER TABLE · DROP TABLE · RENAME · ALTER DATABASE · CREATE INDEX · DROP INDEX Joins & Relationships JOIN · LEFT JOIN · RIGHT JOIN · FULL OUTER JOIN · FOREIGN KEY · PRIMARY KEY Advanced Clauses UNION · AS · EXISTS · CASE · WITH · VIEW Constraints & Defaults CHECK · UNIQUE · DEFAULT Transactions COMMIT · ROLLBACK These 40 commands aren't just syntax to memorize. They're the tools that turn raw data into real decisions. Bookmark this for your next SQL session  Which command do you use most? Drop it below #SQL #DataEngineering #DataScience #DataAnalysis #DatabaseEngineering #TechSkills #LearnSQL #DataCareer #BigData #Python

DELETE, TRUNCATE, and DROP are three SQL commands that every developer and data engineer needs to understand deeply — not just for interviews, but for working safely with production databases. They all remove data. But they work at completely different levels, have very different performance characteristics, and carry very different risks. Using the wrong one especially in production can mean the difference between a quick fix and a hours-long recovery operation. Know the difference. Choose carefully. And always back up before you drop. Read the full post here: https://lnkd.in/ef5fH5ig #SQL #Database #DataEngineering #SQLInterview #DataScience #Analytics

Understanding the logical order of execution in SQL queries is a game-changer for every Data Analyst and Data Engineer 🚀 Many of us write queries starting with SELECT, but SQL actually processes them in a completely different order behind the scenes. Knowing this helps in writing optimized queries, debugging faster, and truly mastering data manipulation. 📌 SQL Logical Execution Order: FROM JOIN ON WHERE GROUP BY HAVING SELECT ORDER BY LIMIT 💡 Key Insight: Even though SELECT appears first in your query, it is executed almost at the end. This is why aliases created in SELECT often can’t be used in WHERE—because they don’t exist yet at that stage. Mastering this concept can significantly improve your query performance and problem-solving skills in real-world data scenarios. #SQL #DataAnalytics #DataEngineering #Learning #TechSkills #CareerGrowth

🚀 Are Your SQL Queries Slow? These Optimization Tricks Can 10x Your Performance! If you're aiming to become a Data Engineer / SQL Developer, writing queries is just the beginning… 👉 The real skill is writing fast and efficient queries. I recently explored some powerful SQL Optimization Techniques that can significantly boost query performance 🚀 🔍 Key Insights you shouldn’t ignore: ✅ Replace LIKE with REGEXP for better pattern matching ✅ Avoid large IN clauses → use temporary tables instead ✅ Order your JOINs from largest → smallest tables ✅ Avoid subqueries in the WHERE clause ✅ Don’t select unnecessary columns ❌ ✅ Never use functions on indexed columns ✅ OR conditions can break indexes ⚠️ ✅ Use approx_distinct() & approx_percentile() for large datasets 💡 Golden Rules of SQL Optimization: ✔ Avoid SELECT * ✔ Filter data as early as possible ✔ Use proper indexing ✔ Analyze queries using EXPLAIN ✔ Understand data distribution ✔ Reduce unnecessary data movement 🔥 Reality Check: Same query… different approach 👇 ⏳ 10 seconds → ⚡ 0.5 seconds 👉 That’s the power of optimization. 💬 What’s your go-to SQL optimization trick? Drop it in the comments 👇 📌 For more practical & job-ready content: 👉 Follow Amol Tathe 👉 Let’s connect #SQL #SQLOptimization #DataEngineering #DataAnalytics #Database #QueryOptimization #LearnSQL #TechCareers #DataEngineer #SoftwareEngineering #Analytics #CodingTips #ITJobs #CareerGrowth 🚀

SQL looks scary until you realize most real-world queries run on a handful of core concepts. Master these 20 SQL concepts and you’ll already be ahead of many aspiring data analysts/devs: ✅ SELECT ✅ WHERE ✅ JOIN ✅ GROUP BY ✅ ORDER BY ✅ Subqueries ✅ HAVING ✅ INSERT / UPDATE / DELETE and more. Don’t try to learn everything in one day — build queries, break them, debug them, repeat. That’s how SQL actually sticks. 🚀 Which SQL concept took you the longest to understand? For me, JOINs and Subqueries were the real boss fights 😅 Credits: Sumit Gupta Thanks for this 💯 #SQL #DataAnalytics #DataEngineering #Database #LearningSQL #SQLQueries #TechSkills #Programming #CareerGrowth #DataAnalyst #SoftwareEngineering #BeginnersGuide

Great visual summarizing core SQL concepts—from SELECT and JOIN to indexing and keys. In real-world data engineering, understanding when and how to use these effectively makes a huge difference in performance and data quality. Currently revisiting these fundamentals while working on dbt models—always good to strengthen the basics.

So true. I focus heavily on these core concepts in the SQL courses that I teach and offer. #SQL #AI #DataAnalytics #CareerSkills #statistics #research #dataanalytics #dataanalysis #career #careeradvice #sqlserver #jobsearch #programing #codingcommunity #datamanagement #tech #newproje