SQL Fundamentals: DQL, DML, DDL, and Relationships

SQL is not just about writing queries… it’s about thinking in data. Most people start SQL with simple SELECT statements. But the real power begins when you go beyond basics. 🔹 SQL helps you transform raw data into meaningful insights 🔹 Window functions unlock patterns you didn’t even know existed 🔹 It plays a key role in ETL pipelines and real-time systems 🔹 Almost every business decision today is backed by SQL queries In the world of Data Engineering, SQL is not optional — it’s fundamental. The better you get at SQL, the better you get at solving real-world problems. 👉 Master SQL, and you don’t just analyze data… you drive decisions. #SQL #DataEngineering #Analytics #LearnSQL #CareerGrowth

SQL window functions changed how I think about data. Before I learned them, I was writing subqueries for everything. Clunky. Repetitive. Hard to read. Then I discovered window functions, and the same logic became cleaner, faster, and easier for anyone to follow. The one I kept reaching for: ROW_NUMBER() It assigns a unique rank to each row within a group. Simple idea. Powerful in practice. Real example: find the most recent order per customer. Without window functions: → Write a subquery to get max date per customer → Join it back to the original table → Hope nothing breaks With ROW_NUMBER(): → Partition by customer → Order by date descending → Filter where row = 1 Same result. Half the code. Much easier to explain to a colleague. I used this constantly when building SQL pipelines, pulling the latest record per entity from multi-source business data. It saved time and made my queries reviewable. If you're writing SQL regularly and haven't touched window functions yet, ROW_NUMBER() is where I'd start. Small function. Big shift in how you think. Which SQL concept clicked everything into place for you? Drop it below 👇 #SQL #DataAnalytics #DataScience #LearningInPublic

Before I trust any SQL query, I run these 3 checks. Every. Single. Time. Because in real pipelines, SQL doesn’t fail. It lies. Here are the 3 checks: 🔹 1. Row count check Before JOIN After JOIN Did the number of rows increase? If yes — why? 👉 A simple JOIN can silently duplicate data. 🔹 2. Data grain check What is one row supposed to represent? 👉 One order? 👉 One customer? 👉 One transaction? After transformations — is that still true? If not, your metrics are already wrong. 🔹 3. Duplicate check Run this: GROUP BY key_columns HAVING COUNT(*) > 1 If this returns rows: 👉 You don’t have a clean dataset 👉 Your aggregations will lie Most engineers check if SQL runs. Few check if data is still correct. 🔹 The shift Good SQL is not about syntax. It’s about control. Control over: • data grain • duplication • unintended joins Side note: This is the gap I see often — engineers understand SQL, but don’t get to practice failure scenarios safely. Working on something around this. 👀 What’s one check you always run before trusting your query? #dataengineering #sql #analytics #etl #learning

Most people learn SQL. Few master it. Here's the full SQL Master Tree , 18 areas, basics to advanced: 🔷 Database Fundamentals 🔷 Data Types, DDL, DML, DQL 🔷 JOINs, Subqueries, Views 🔷 Indexing, Transactions, ACID 🔷 Normalization, Window Functions, CTEs 🔷 Performance Optimization & Real-World Usage If I had to pick 3 concepts that make the biggest difference in day-to-day data work: 1. Window Functions (ROW_NUMBER, RANK, PARTITION BY) 2. CTEs — readable, reusable logic 3. Indexing + partitioning — because slow queries are career-limiting 😅 Save this. Share it with someone learning SQL. What would you add to this tree? 👇 #SQL #DataEngineering #Analytics #DataScience #LearningInPublic

If you want a global data job, SQL is non-negotiable. SQL simply means: Structured Query Language, a tool you use to talk to databases. Think of a database like a big cupboard where companies store all their information. SQL is the language you use to: ✔ Ask questions ✔ Pull out the exact information you need ✔ Clean the data ✔ Organize it ✔ Help the business make smarter decisions Simple example: “Show me all customers who bought from us this month.” SQL can answer that in seconds. Do you want me to share a beginner SQL practice sheet? 👇 #LearnSQL #DataSkills #BeginnersInTech #DataAnalysis #TechSkills #AnalyticsJourney

A SQL query that takes 45 minutes to run against a large table is not just a technical problem — it is a business problem. Slow dashboards frustrate analysts. Slow APIs frustrate users. Slow ETL pipelines delay decisions. And all of it costs money in compute resources and lost productivity. The good news is that most slow queries have fixable root causes — missing indexes, non-sargable WHERE clauses, correlated subqueries, inefficient joins, and unnecessary data retrieval. Understanding how the query optimizer works, reading execution plans, and applying the right optimization technique for each situation is one of the most valuable skills any data engineer or analyst can develop. Query performance is not magic. It is method. Read the full post here: https://lnkd.in/ezeFxa2k #SQL #DataEngineering #QueryOptimization #Database #Analytics #DataScience

🚀Day 94 of My 100 Days Data Analysis Journey You don’t master SQL by learning everything… you master it by knowing what stage you’re in. This journey has made one thing clear, SQL is not random. It’s structured. Right now, the focus is on the core layers: Understanding how data is stored (tables, keys, relationships) Writing clean queries with SELECT, WHERE, ORDER BY Breaking down joins and how tables actually connect Using aggregations to turn raw data into insights This is the phase where things stop being confusing… and start making sense. But beyond this stage is where it gets deeper: Optimization & Indexing, writing queries that don’t just work, but scale Database Design, structuring data for real-world systems Advanced SQL, window functions, CTEs, complex querying Real-world application, building projects that reflect actual use cases The goal is no longer just to learn SQL… It’s to think in SQL, design with SQL, and solve problems with SQL. Still in the building phase. Next phase: execution and real-world application. #SQL #DataAnalysis #Growth #LearningInPublic #DataSkills

💣 𝗗𝗲𝗹𝗲𝘁𝗶𝗻𝗴 𝗠𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗥𝗼𝘄𝘀 𝗶𝗻 𝗦𝗤𝗟? 𝗧𝗵𝗶𝘀 𝗖𝗮𝗻 𝗖𝗿𝗮𝘀𝗵 𝗬𝗼𝘂𝗿 𝗔𝗽𝗽 😬 A very common mistake I see beginners make 👇 ```sql DELETE FROM big_table; ``` Or even worse… ```sql DELETE FROM big_table WHERE condition; ``` 👉 On tables with millions of rows 💥 🔴 𝗪𝗵𝗮𝘁 𝗚𝗼𝗲𝘀 𝗪𝗿𝗼𝗻𝗴? • 🚫 Table locks → entire app gets blocked • 🧱 Transaction log explosion • 🐢 Long-running queries • 💥 Possible downtime 👉 Your application starts timing out… users panic… 😅 🧠 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗛𝗮𝗽𝗽𝗲𝗻𝘀 SQL Server doesn’t delete instantly 👉 It logs every row deletion 👉 Keeps locks until transaction completes So deleting 10M rows = 👉 10M log entries + long locks 👶 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗠𝗶𝘀𝘁𝗮𝗸𝗲 “Just run one DELETE… it will be faster” ❌ 👉 Actually it’s the slowest + riskiest way ⚙️ 𝗖𝗼𝗿𝗿𝗲𝗰𝘁 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀 ✅ 𝗕𝗮𝘁𝗰𝗵 𝗗𝗲𝗹𝗲𝘁𝗶𝗼𝗻 ```sql WHILE 1=1 BEGIN DELETE TOP (10000) FROM big_table WHERE condition; IF @@ROWCOUNT = 0 BREAK; END ``` 👉 Deletes in chunks → less locking → safer ✅ 𝗨𝘀𝗲 𝗧𝗥𝗨𝗡𝗖𝗔𝗧𝗘 (𝗶𝗳 𝗮𝗹𝗹 𝗱𝗮𝘁𝗮 𝗿𝗲𝗺𝗼𝘃𝗲𝗱) ```sql TRUNCATE TABLE big_table; ``` 👉 Minimal logging 👉 Super fast 🚀 ⚠️ But no WHERE clause ✅ 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻 𝗗𝗿𝗼𝗽 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) 👉 Drop old partitions instead of deleting rows • Near instant • Best for large datasets ✅ 𝗢𝗳𝗳-𝗣𝗲𝗮𝗸 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 👉 Run during low traffic 👉 Avoid impacting users 💡 𝗣𝗿𝗼 𝗧𝗶𝗽 👉 Monitor: • Transaction log size • Locks (sp_who2 / DMVs) • Execution time 🎯 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 Deleting data is easy… 👉 Deleting it safely at scale = real skill 💡 Junior → Runs DELETE Senior → Designs deletion strategy I’m on a mission to make people stronger in tech and data 💪📊 👉 Explore more at https://lnkd.in/gqVua8Tu #SQLServer #DataEngineering #Database #PerformanceTuning #BigData #ETL #SQL #DataEngineer #TechLearning #BigDataYatra

Most engineers chase the “modern stack.” Few realize the most important layer never changed. There’s a famous story about five monkeys, a ladder, and a bunch of bananas. Every time a monkey tried to climb the ladder, all of them were sprayed with cold water. Eventually, they stopped trying. Then the monkeys were replaced — one by one. The twist? None of the new monkeys had ever been sprayed. Yet every time one tried to climb, the others pulled it down. If you asked why? “That’s just how things are done here.” This is how many teams treat SQL today. “SQL doesn’t scale” “Don’t write complex queries” “Use modern frameworks instead” But ask why… and you’ll mostly hear inherited opinions. Here’s the reality: SQL didn’t become outdated. We just layered tools on top of it and forgot its power. While the ecosystem keeps changing: - SQL still powers every data warehouse - SQL still defines transformations - SQL is still how data turns into decisions It never broke. We just stopped using it properly. I wrote about this in my latest blog — why SQL remains the most durable abstraction in data engineering, and what most teams get wrong about it. #DataEngineering #SQL #AnalyticsEngineering #BigData