Prexa Patel’s Post

📢 Day 31 — ROLLUP: Hierarchical Aggregation ROLLUP creates multiple levels of totals. It is useful for hierarchical summaries. 📌 Syntax SELECT column1, column2, SUM(value) FROM table GROUP BY ROLLUP(column1, column2); 📌 Example SELECT department, job_title, SUM(salary) FROM employees GROUP BY ROLLUP(department, job_title); 🛠 Practical Uses ✔️ Department totals ✔️ Subtotals in reports #SQL #DataAnalytics #DataEngineering #Database #Programming #Tech #Developers #Learning #DataScience #DataAnalyst #MachineLearning #BigData #BusinessIntelligence #ETL #DataVisualization #DataWarehouse #CareerGrowth #SQLDeveloper #DatabaseDeveloper #DatabaseAdministrator #DataEngineer #BIDeveloper #SQLServer #PostgreSQL #MySQL #Oracle #Snowflake #BigQuery #SparkSQL #TechCommunity #ITProfessionals #ProfessionalGrowth #Networking #LinkedInLearningData

📢 Day 28 — Semi Join: Checking Data Existence Semi Join returns rows from the first table where matching records exist in another table. Implemented using EXISTS. 📌 Syntax SELECT columns FROM table1 WHERE EXISTS (subquery); 📌 Example SELECT customer_name FROM customers c WHERE EXISTS ( SELECT 1 FROM orders o WHERE c.customer_id = o.customer_id ); 🛠 Practical Uses ✔️ Customers who placed orders ✔️ Product availability checks #SQL #DataAnalytics #DataEngineering #Database #Programming #Tech #Developers #Learning #DataScience #DataAnalyst #MachineLearning #BigData #BusinessIntelligence #ETL #DataVisualization #DataWarehouse #CareerGrowth #SQLDeveloper #DatabaseDeveloper #DatabaseAdministrator #DataEngineer #BIDeveloper #SQLServer #PostgreSQL #MySQL #Oracle #Snowflake #BigQuery #SparkSQL #TechCommunity #ITProfessionals #ProfessionalGrowth #Networking #LinkedInLearningData

📢 Day 30 — HAVING: Filtering Aggregated Results HAVING filters grouped data after aggregation. Unlike WHERE, it works with aggregate functions. 📌 Syntax SELECT column, aggregate_function FROM table GROUP BY column HAVING condition; 📌 Example SELECT department_id, COUNT(*) FROM employees GROUP BY department_id HAVING COUNT(*) > 5; 🛠 Practical Uses ✔️ Departments with many employees ✔️ High-sales regions #SQL #DataAnalytics #DataEngineering #Database #Programming #Tech #Developers #Learning #DataScience #DataAnalyst #MachineLearning #BigData #BusinessIntelligence #ETL #DataVisualization #DataWarehouse #CareerGrowth #SQLDeveloper #DatabaseDeveloper #DatabaseAdministrator #DataEngineer #BIDeveloper #SQLServer #PostgreSQL #MySQL #Oracle #Snowflake #BigQuery #SparkSQL #TechCommunity #ITProfessionals #ProfessionalGrowth #Networking #LinkedInLearningData

📢 Day 33 — Scalar Subqueries: One Value Inside Another Query A scalar subquery returns a single value. It is often used inside SELECT or WHERE clauses. 📌 Syntax SELECT column FROM table WHERE column = (subquery); 📌 Example SELECT employee_name FROM employees WHERE salary = ( SELECT MAX(salary) FROM employees ); #SQL #DataAnalytics #DataEngineering #Database #Programming #Tech #Developers #Learning #DataScience #DataAnalyst #MachineLearning #BigData #BusinessIntelligence #ETL #DataVisualization #DataWarehouse #CareerGrowth #SQLDeveloper #DatabaseDeveloper #DatabaseAdministrator #DataEngineer #BIDeveloper #SQLServer #PostgreSQL #MySQL #Oracle #Snowflake #BigQuery #SparkSQL #TechCommunity #ITProfessionals #ProfessionalGrowth #Networking #LinkedInLearningData

Understanding SQL Subqueries doesn’t have to be confusing. I’ve just uploaded a new video where I explain: • What a subquery is (in the simplest way) • Types of subqueries • Self-contained subqueries • Practical examples using SELECT, WHERE, and HAVING If you're preparing for SQL interviews or building your data analytics skills, this will really help you get clarity. 🎥 Watch here: https://lnkd.in/gUwt7quZ I’ve kept the explanation simple and practical so you can actually use it while solving queries. Would love your feedback and suggestions for upcoming topics! #SQL #SQLTutorial #SQLForBeginners #Subquery #DataAnalytics #DataAnalyst #LearnSQL #Coding #Programming #TechLearning #InterviewPreparation #Database #MySQL #SQLServer #Analytics #Developer #CodeQueryHub #Learning #CareerGrowth #Upskill #TechContent #YouTubeLearning

📢 Day 29 — GROUP BY: Summarizing Data GROUP BY groups rows that have the same values. It is used with aggregate functions like SUM, COUNT, AVG. 📌 Syntax SELECT column, aggregate_function FROM table GROUP BY column; 📌 Example SELECT department_id, COUNT(*) FROM employees GROUP BY department_id; 🛠 Practical Uses ✔️ Sales per region ✔️ Employees per department #SQL #DataAnalytics #DataEngineering #Database #Programming #Tech #Developers #Learning #DataScience #DataAnalyst #MachineLearning #BigData #BusinessIntelligence #ETL #DataVisualization #DataWarehouse #CareerGrowth #SQLDeveloper #DatabaseDeveloper #DatabaseAdministrator #DataEngineer #BIDeveloper #SQLServer #PostgreSQL #MySQL #Oracle #Snowflake #BigQuery #SparkSQL #TechCommunity #ITProfessionals #ProfessionalGrowth #Networking #LinkedInLearningData

Many people find correlated subqueries confusing… but they don’t have to be. I’ve just shared a new video where I break down: • Inline subqueries (how to use them like temporary tables) • Correlated subqueries in a simple, step-by-step way • Key differences between self-contained and correlated queries • Practical SQL examples you can actually use If you're preparing for SQL interviews or working on data analytics, this will help you build strong fundamentals. 🎥 Watch here: https://lnkd.in/g_6nGeMk The goal is to make SQL simple, practical, and easy to understand. Your feedback is always welcome! #SQL #SQLTutorial #SQLForBeginners #CorrelatedSubquery #InlineSubquery #Subquery #DataAnalytics #DataAnalyst #LearnSQL #Coding #Programming #TechLearning #InterviewPreparation #Database #MySQL #SQLServer #Analytics #Developer #CodeQueryHub #Learning #CareerGrowth #Upskill #TechContent #YouTubeLearning

Going deep on SQL fundamentals reveals something surprising. The most common mistakes aren't complex JOIN errors or subquery disasters. They're the basics — and they catch everyone. Here are 5 SQL mistakes seen over and over again 👇 1. Using = instead of LIKE for pattern matching ❌ WHERE name = 'R%' ✅ WHERE name LIKE 'R%' 2. Using = NULL instead of IS NULL ❌ WHERE commission = null ❌ WHERE commission = NULL ✅ WHERE commission IS NULL 3. Putting ORDER BY before WHERE ORDER BY must always be the last clause in a query. Always. 4. Forgetting DISTINCT when duplicates ruin the output Seeing repeated rows? SELECT DISTINCT is often the fix. 5. Using DELETE without a WHERE clause This deletes ALL records from the table. Not just the one intended. All of them. 😬 --- Beyond mistakes, here are concepts worth understanding deeply: → DDL vs DML — CREATE/ALTER/DROP vs INSERT/UPDATE/DELETE → COUNT() vs COUNT(*) — one ignores NULLs, one doesn't → CHAR vs VARCHAR — fixed vs variable storage → HAVING vs WHERE — filtering groups vs filtering rows → Equi-Join vs Natural Join — how common columns appear in output SQL isn't hard. But the details matter more than most people think. #SQL #MySQL #DatabaseDevelopment #TechEducation #ComputerScience #Coding #LearningSQL #DataEngineering

🚀 SQL Cheat Sheet – Quick Topics Master these core SQL concepts 👇 ✔ Introduction ✔ SQL Database ✔ Constraints ✔ Operators ✔ SQL Tables ✔ SQL Clauses ✔ SQL Conditions ✔ SQL Joins ✔ Aggregate Functions ✔ SQL Functions ✔ SQL Views ✔ SQL Indexes ✔ Stored Procedures ✔ Functions (User Defined) ✔ Triggers ✔ Miscellaneous Follow JACOB JEYAKUMAR S For more updates #SQL #DataEngineering #DataAnalytics #Database #LearnSQL #TechCareers #Developers

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