Master SQL with Joins, CTEs and Window Functions

“Good queries return results. Great systems return them fast.” I used to celebrate when my SQL queries finally worked. As a Data Analyst, getting the right result felt like winning. The numbers matched, the joins made sense, and the dashboard updated. Job done. But the first time I watched a production system slow to a crawl because of a “working query,” everything changed. That’s when I started seeing the difference between writing queries… and building systems. A query that takes 12 seconds on a small dataset might take minutes (or crash entirely) at scale. And in a live environment, that’s not just slow — it’s expensive. What changed for me? I stopped asking: “Does it work?” And started asking: Is it indexed properly? How does the execution plan behave? What happens when this table grows 10x? Can this lock other transactions? Simple tweaks, adding the right index, avoiding SELECT *, and rewriting joins can turn seconds into milliseconds. AI can generate queries now. That’s impressive. But knowing why a query is slow, and how it impacts an entire system? That’s a different level of thinking. That’s where Database Administration begins. So here’s the real question: Are you optimising for results… or for performance? #DatabaseAdministration #SQLServer #MySQL #DataEngineering #QueryOptimization #TechCareers #AI #CareerGrowth #PerformanceTuning #DataProfessionals

Still Googling SQL syntax before every query? You’re not alone — and that’s the problem. Master these 23 SQL commands and stop looking things up forever 👇 No fluff. No theory. Just the SQL you’ll use daily in: • Data pipelines • Dashboards • Interviews Most people only use 5 of these. Top data engineers know all 23. If you're serious about data — you need these cold. 👉 Be honest — which SQL command do you STILL Google? Save this. You’ll come back to it. Follow Mohamed Khasim for more 🚀 Credit: José Siles 🙌 — give him a follow, great content! #SQL #DataEngineering #DataAnalytics #DataProfessionals #LearnSQL #StructuredQueryLanguage #DataSkills #AI

Everyone wants to learn ML and AI. I chose SQL Here's why SQL is quietly the most powerful skill in tech: 📊 Every business decision is backed by data 🔍 SQL lets you ask ANY question of ANY dataset ⚡ Optimized queries save hours of waiting and thousands in compute costs 🧩 It's the glue between raw data and business insight In 3 years, T-SQL and PostgreSQL have helped me: — Build insurance data models from scratch — Automate complex financial reporting pipelines — Support global OEM clients with clean, reliable data SQL isn't glamorous. But it's foundational. What was the skill you almost overlooked that turned out to be career-defining? #SQL #DataEngineering #TSQL #CareerAdvice #TechSkills

I significantly improved report performance – reducing runtime from minutes to seconds – by switching from GROUP BY for ranking to DENSE_RANK(). * Window Functions unlock performance gains many analysts miss, complementingGROUP BY for more efficient data analysis. * CTEs (Common Table Expressions) make complex SQL readable and maintainable. * Query Optimization isn’t about indexes alone – it’s about how you write the SQL. * I realized slow reports often stem from inefficient ranking logic. Window Functions can be the difference between a report that runs in 2s and one that times out in production. Senior engineers know when not to use them too – avoiding over-partitioning is key. Most people fail at understanding the subtle differences between ranking functions, leading to incorrect insights. ROW_NUMBER vs RANK vs DENSE_RANK — can you name a real scenario where picking the wrong one gives you the wrong business answer? #DataScience #SQL #DataAnalytics #DataEngineering #TechHiring #BuildInPublic This is an AI posting

If you work with 𝗦𝗤𝗟, 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗼𝗿 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, this is one of those concepts that makes your queries feel 𝟭𝟬𝘅 𝘀𝗺𝗮𝗿𝘁𝗲𝗿. Most people think SQL is just about 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗿𝗼𝘄𝘀 𝗮𝗻𝗱 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮. And honestly… that’s 𝗳𝗶𝗻𝗲. But once you learn 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, SQL stops being basic and starts 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹. 💡 Because now you’re not just summarizing data. 💡 You’re analyzing it in context. 💡 Across every row. 💡 Without losing detail. 💡 Without collapsing the story. That’s 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘂𝗽𝗴𝗿𝗮𝗱𝗲. Instead of asking: 📊 “𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝘁𝗼𝘁𝗮𝗹?” You start asking: 💭 Who ranks highest? 💭 What’s changing over time? 💭 How does this row compare to others? 💭 What pattern is hidden inside the data? 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘄𝗶𝗻𝗱𝗼𝘄 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲. They let you: ✨ Rank records without losing granularity ✨ Build running totals over time ✨ Compare each row to its peers ✨ Detect patterns as they evolve In simple terms: 👉 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 👉 𝗪𝗶𝗻𝗱𝗼𝘄 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝘁𝗲𝗹𝗹 𝘆𝗼𝘂 𝗵𝗼𝘄 𝗶𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 And that difference changes everything. Because now SQL is no longer just 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮. It’s explaining 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮. Next week in 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗔𝗹𝗰𝗵𝗲𝗺𝗶𝘀𝘁 𝘀𝗲𝗿𝗶𝗲𝘀, I’ll break down: ✔️ How window functions actually work (in simple terms) ✔️ Real-world use cases in analytics & data engineering ✔️ The most common mistakes beginners make Because the best SQL queries don’t just return data. 𝗧𝗵𝗲𝘆 𝗿𝗲𝘃𝗲𝗮𝗹 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆 𝗵𝗶𝗱𝗱𝗲𝗻 𝗶𝗻𝘀𝗶𝗱𝗲 𝗶𝘁. 💭 Have you ever solved something with window functions that GROUP BY couldn’t handle? #AI #DataEngineering #LearningInPublic #TechCareer #SQL

🔥 Improving just 1% every day I used to learn everything at once. only to find out it lead me to nowhere. so i stopped doing that. Instead, I focused on one small improvement daily. 💡 Today’s 1% Improvement: I solved a simple SQL problem from Danny Ma 8 weeks sql challenge: 👉 “What is the total amount each customer spent?” 🧠 What I learned: Real-world data is split across tables You can’t calculate revenue without joining datasets The key idea: 👉 Transactions (sales) + Pricing (menu) = Revenue 🔍 The mindset shift: Earlier, I used to think: ❌ “Just write query and get answer” Now I think: ✅ “What business problem am I solving?” ✅ “Where does each piece of data come from?” ✅ “How do tables connect in real life?” 📈 Why this matters: SQL is not about syntax. It’s about thinking like a data problem solver. And that comes from… 👉 Daily 1% improvements. You don’t need 10 hours a day. You don’t need to be perfect. Just: 👉 Show up 👉 Solve one problem 👉 Understand one concept deeply That’s how consistency compounds. I amm documenting my journey of becoming an AI & Data Engineer by learning, building, and sharing every day. If you're on a similar path, let’s grow together 🤝 website link in comments #AIEngineer #SQL #BUILDINGINPUBLIC #CONSISTANCY

A mini heart attack story of a Data Engineer 😅 Code is in production. Backfilled 3 years of data… took 2 full days. Everything looks clean. Numbers match. Life is good. Then comes the message from stakeholders: “Hey, there’s a huge data volume difference after a certain date.” That moment… your soul leaves your body. You start thinking about everything: sources, queries, joins, partitions, timestamps, late-arriving data, 2 days more you have to spend to correct data, why did I choose computer science what not!!! After digging deep, you realize the data isn't wrong It’s just… more than what the current dashboards actually need, and actually opens up opportunities to extend the dataset for future use cases. Fix? Not rewriting everything. Not panicking. Just filtering the dataset to what the dashboards actually require for now. Lesson: Sometimes the solution to a problem is to showcase the problem is a better solution. famously said "that's not a bug, it's a feature' And sometimes… your pipeline is already ahead of the business. #DataEngineering #Debugging #Analytics #RealStory

Day 64 - Data Analysis Using AI Journey 🚀 𝗧𝗼𝗽𝗶𝗰: Having VS Where 𝗛𝗔𝗩𝗜𝗡𝗚: HAVING is used to filter data after grouping, mainly with aggregation functions. 𝗘𝗫: SELECT dept_id, COUNT(*) FROM employees GROUP BY dept_id HAVING COUNT(*) > 2; Shows only departments with more than 2 employees Why to use: 🔹To filter grouped/aggregated results 🔹Works with GROUP BY 𝗪𝗛𝗘𝗥𝗘 vs 𝗛𝗔𝗩𝗜𝗡𝗚 𝗪𝗛𝗘𝗥𝗘 → WHERE is used to filter rows before grouping or aggregation. 𝗛𝗔𝗩𝗜𝗡𝗚 → HAVING is used to filter grouped data after applying GROUP BY and aggregation functions. 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝘆 𝗦𝘆𝗻𝘁𝗮𝘅 𝗢𝗿𝗱𝗲𝗿: SQL Query Syntax Order is the standard sequence in which we write SQL clauses to form a valid query. SELECT → specifies the columns or data to be retrieved FROM → indicates the table or source of the data WHERE → filters rows based on specified conditions GROUP BY → groups rows based on one or more columns HAVING → filters grouped data based on conditions ORDER BY → sorts the result set in a specified order LIMIT → restricts the number of rows returned in the output #Frontlinesedutech #flm #frontlinesmedia #DataAnalytics #flmdataanlaytics #flmaipowereddataanlytics #dataanalyst #machinelearning #sql #Where #Having #WhereVSHaving Ranjith Kalivarapu Rakesh Viswanath Frontlines EduTech (FLM) Krishna Mantravadi Upendra Gulipilli

What people think I do as a Data Analyst vs what I actually do as a Data Analyst at Optum- 1)Write beautiful SQL code (sometimes) but mostly I Stare at complex SQL code and legacy stored procedures generated by Microstrategy and clean and rewrite it so that it can be used to make Dashboards. 2)Make really creative Dashboards (sometimes) but mostly client demands a fixed template. 3) Use advanced excel (sometimes) but mostly Stare at 2 monitors comparing 4 excels and matching data and doing QC. 4) Think that AI is gonna replace me (sometimes) but mostly I try to keep up with all the advancements in the world of AI and use it to boost my productivity. #dataanalysis #humour #AI

🚀 **Mastering SQL in 2026: From Queries to Intelligence** In today’s data-driven world, SQL is no longer just a skill — it’s a **strategic advantage**. This SQL Mindmap is not just a visual… it’s a **complete roadmap from beginner to advanced data professional**. 💡 Whether you're building dashboards, optimizing queries, or designing data systems — everything starts here. 🔍 **What this covers:** 🔹 Core Foundations → SELECT, WHERE, JOINs 🔹 Advanced Querying → Subqueries, Window Functions, CTEs 🔹 Data Transformation → CASE, CAST, STRING & DATE functions 🔹 Performance Optimization → Indexing, Execution Plans, Query Tuning 🔹 Analytics Layer → Aggregations, Percentiles, Statistical Functions 🔹 Real-world Applications → BI Tools, ML integrations ⚡ The difference between an average analyst and a top-tier data professional? 👉 **Deep understanding + optimized execution** 📊 SQL is evolving beyond databases — it's now powering: ✔️ Real-time analytics ✔️ AI/ML pipelines ✔️ Data warehousing (Snowflake, BigQuery) ✔️ Business Intelligence ecosystems 🔥 If you're serious about Data Analytics, Data Engineering, or AI — this is your **blueprint to mastery**. 💬 Which SQL concept do you find most challenging — Window Functions or Query Optimization? Let’s discuss! --- #SQL #DataAnalytics #DataEngineering #BusinessIntelligence #AI #MachineLearning #DataScience #Lear