SQL Cheat Sheet: Master Core Concepts

🚀 Day 4/30 – SQL Challenge Continuing this journey to stay consistent and improve my practical SQL skills. 🔹 Problem: Find duplicate emails in a table. 🔹 Approach: Used GROUP BY to group similar emails and HAVING with COUNT to filter those appearing more than once. 🔹 Real-world: This can be used in systems to identify duplicate user accounts or clean inconsistent data. Learning step by step and getting more comfortable with SQL each day 💻 #SQL #LeetCode #MySQL #CodingJourney #SDE #DataCleaning #BackendDevelopment

I used to overuse subqueries in SQL. It worked… but it made my queries harder to read and sometimes slower. Then I started using CTEs (Common Table Expressions). And everything became much cleaner. Instead of this: SELECT * FROM ( SELECT CustomerID, COUNT(*) AS TotalOrders FROM Orders GROUP BY CustomerID ) t WHERE TotalOrders > 5 You can write: WITH OrderSummary AS ( SELECT CustomerID, COUNT(*) AS TotalOrders FROM Orders GROUP BY CustomerID ) SELECT * FROM OrderSummary WHERE TotalOrders > 5 Same result — but much easier to read and maintain. Lesson I learned: Readable queries are easier to debug, optimize, and scale. Do you prefer subqueries or CTEs in your work? #SQL #SQLServer #DataEngineering #DatabaseDeveloper #TechTips

When I started learning MySQL, terms like DDL, DML, DQL, DCL, and TCL felt overwhelming. Over time, I realized SQL isn’t just about commands—it’s about understanding how data is structured and managed. 🔹 CRUD Operations (foundation of any application) Create → Add data Read → Retrieve data Update → Modify data Delete → Remove data 🔹 SQL Command Categories • DDL – Defines structure (CREATE, ALTER, DROP, TRUNCATE) • DML – Manages data (INSERT, UPDATE, DELETE) • DQL – Retrieves data (SELECT) • DCL – Controls access (GRANT, REVOKE) • TCL – Manages transactions (COMMIT, ROLLBACK, SAVEPOINT) 💡 Once these basics click, working with databases becomes more structured and efficient. #MySQL #SQL #Database #LearningJourney #FullStackDeveloper

Stop memorizing SQL queries. Start recognizing patterns. Most real-world data engineering problems (about 80% of them) boil down to the same 25 reusable patterns. If you understand the pattern, the syntax becomes secondary. Whether it's an interview or a production bug, you'll know exactly which tool to grab: 🔹 Window Functions: For Top-N analysis and running totals. 🔹 Self-Joins: For hierarchical data and comparisons. 🔹 CTEs: For cleaning and de-duplication logic. 🔹 Cohorts/Funnels: For user retention tracking. The biggest mistake? Solving random questions without a system. Don't just "code"—think in patterns. - Follow Dhiraj Kumar for more practical data engineering & SQL content Document Credit qoes to respective owner.. #SQL #DataEngineering #BackendDevelopment Oracle MySQL #sde #swe #mysql #fullstackdeveloper #softwaredeveloper

🔍 Understanding the 4 Types of SQL Joins — Made Simple! If you're working with databases, mastering SQL joins is a must-have skill. Here’s a quick breakdown: ✅ Inner Join – Returns only matching records from both tables 👉 Use when you need common data between tables ✅ Left Join – Returns all records from the left table + matching from the right 👉 Non-matching right-side data will be NULL ✅ Right Join – Returns all records from the right table + matching from the left 👉 Non-matching left-side data will be NULL ✅ Full Outer Join – Returns all records from both tables 👉 Non-matching data from either side will be NULL 💡 Pro Tip: Choosing the right join can significantly impact your query results and performance. Whether you're a beginner or brushing up your skills, this is a fundamental concept every developer should know! Note: Diagrams are simplified. In actual queries, both table columns exist separately and should be handled carefully. #SQL #Database #DataEngineering #SoftwareDevelopment #BackendDevelopment #FullStackDeveloper #TechLearning #Programming #Developers #LearnSQL #CareerGrowth

Day 31 of mastering SQL 📘Stored Procedures in SQL 🔍 What is a Stored Procedure? A Stored Procedure is a pre-written SQL code saved in the database that you can execute anytime. 👉 Think of it like a function in SQL that performs a task. 🧠 Why Use Stored Procedures? ✔ Reusability (write once, use many times) ✔ Better performance (precompiled) ✔ Security (restrict direct table access) ✔ Cleaner & modular code 🧾 Basic Syntax CREATE PROCEDURE procedure_name AS BEGIN SELECT * FROM table_name; END; 💡 Example CREATE PROCEDURE GetEmployees AS BEGIN SELECT name, salary FROM employees; END; 👉 Execute it using: EXEC GetEmployees; 🔄 With Parameters CREATE PROCEDURE GetEmployeeBySalary @min_salary INT AS BEGIN SELECT * FROM employees WHERE salary > @min_salary; END; 👉 Run: EXEC GetEmployeeBySalary 50000; ⚠️ Important Points Stored in the database Can accept parameters Improves performance Reduces repetition of queries #SQL #SQLLearning #Database #TechSkills #LearningJourney

SQL Roadmap That Takes You From Beginner to Advanced If you’re learning SQL randomly… you’re wasting time. SQL is not just queries. It’s a step-by-step skill progression. Here’s the complete roadmap 👇 Step 1: SQL Foundations What is SQL Tables, Rows, Columns Primary & Foreign Keys Constraints Step 2: Core Operations SELECT, WHERE ORDER BY, LIMIT INSERT, UPDATE, DELETE Step 3: Joins (Game Changer) INNER JOIN LEFT / RIGHT JOIN FULL JOIN Self Join Step 4: Aggregations GROUP BY HAVING COUNT, SUM, AVG Step 5: SQL Functions String functions Date functions Number functions Step 6: Advanced Queries Subqueries CTEs EXISTS vs IN Step 7: Database Design Normalization ER Diagrams Schema design Step 8: Indexing & Optimization Indexes Query plans Performance tuning Step 9: Transactions ACID properties Deadlocks Isolation levels Step 10: Stored Procedures Functions Triggers Automation Step 11: SQL for Analytics Window functions RANK, ROW_NUMBER Partitioning Most developers stop at SELECT. Top developers master performance + design + scalability. Image Credit: @Abhishek If this feels like your journey, you’re not alone. If you want to grow on LinkedIn, follow ❤️me Narendra Kushwaha. and DM me. I’ll guide you on the right path for 2026, based on my journey of building a 7K+ LinkedIn family in 7–8 months. #SQL #Database #BackendDevelopment #DataEngineering #SoftwareEngineering #Developers #CareerGrowth

🚀 Understanding SQL Query Execution Order Many developers write SQL queries in the order they appear — but databases don’t execute them that way. Here’s the actual execution flow behind the scenes: 🔹 FROM – Identify the source tables 🔹 JOIN – Combine tables based on conditions 🔹 ON – Apply join conditions 🔹 WHERE – Filter rows before grouping 🔹 GROUP BY – Group rows for aggregation 🔹 HAVING – Filter grouped data 🔹 SELECT – Choose the required columns 🔹 ORDER BY – Sort the result 🔹 LIMIT – Restrict the number of rows returned 💡 Key Insight: Even though we write "SELECT" first, it is executed much later in the process! Understanding this order helps in: ✔ Writing optimized queries ✔ Debugging complex SQL ✔ Improving performance 📌 Master the execution flow, and SQL will start making much more sense. #SQL #Database #BackendDevelopment #DataAnalytics #Programming #Learning

Most data engineers focus on writing SQL queries while ignoring what lies under the hood. Below is the execution of the query broken down into simpler steps: 1. When a SQL query is issued, it reaches down to the database engine. 2. This engine is responsible for the compilation of SQL by parsing the code to check for proper semantics, syntax and permissions to access the database objects. 3. Once the engine understands your intent, it translates that human-readable SQL into bytecode. This is a machine-readable format that represents the logical steps required to fetch your data. 4. Next comes the most critical part of the process i.e. the Query Optimiser. It analyses the bytecode in order to decide the most efficient path to execute the query. It might: - push down the predicate by filtering rows as early as possible to reduce I/O. - choose between different types of joins. - decide if it's faster to scan an index or the full table. - determine how many CPU cores can work on the task simultaneously. 5. Finally, the execution engine follows the optimized plan, pulls the records from storage by interacting with the storage engine and serves the results back to your screen. So, now you know your 'SELECT * FROM users' query is not as innocent as it looks. #DataEngineering #SQL #MySQL #Queries #QueryOptimisation #SystemDesign

SQL Made Simple – From Basics to Core Concepts This visual provides a quick overview of how SQL powers data management in relational databases. It covers the process of storing data in tables and performing operations such as SELECT, INSERT, UPDATE, and DELETE. Additionally, it highlights key concepts like joins, functions, indexes, constraints, and data types—essential building blocks for writing efficient and optimized queries. #SQL #DataAnalytics #Database #DataScience #Learning #TechSkills #Developers #DataEngineering