Prexa Patel’s Post

📢 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 35 — EXISTS vs IN: Two Ways to Check Data Both EXISTS and IN check if values exist in another query. But they behave differently for large datasets. 📌 Syntax SELECT column FROM table WHERE column IN (subquery); SELECT column FROM table WHERE EXISTS (subquery); 📌 Example SELECT customer_name FROM customers WHERE customer_id IN (SELECT customer_id FROM orders); SELECT customer_name FROM customers c WHERE EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.customer_id); 🛠 Practical Uses ✔️ Customer order analysis ✔️ Data validation #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 #LinkedInLearningDataFROM

📢 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 34 — Correlated Subqueries: Queries That Depend on Outer Query A correlated subquery runs once for each row of the outer query. It references columns from the outer query. 📌 Syntax SELECT columns FROM table1 WHERE column = ( SELECT value FROM table2 WHERE table1.column = table2.column ); 📌 Example SELECT e.employee_name FROM employees e WHERE salary > ( SELECT AVG(salary) FROM employees WHERE department_id = e.department_id ); 🛠 Practical Uses ✔️ Above-average employees ✔️ Department comparisons #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 32 — CUBE: Multi-Dimensional Aggregation CUBE generates all possible grouping combinations. It helps analyze data across multiple dimensions. 📌 Syntax SELECT column1, column2, SUM(value) FROM table GROUP BY CUBE(column1, column2); 📌 Example SELECT region, product, SUM(sales) FROM sales GROUP BY CUBE(region, product); 🛠 Practical Uses ✔️ Multi-dimensional analytics ✔️ Data warehouse 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

🚀 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

📊 SQL JOINs Made Simple (With Visual Architecture) Understanding SQL JOINs is essential for working with relational databases and combining data efficiently. 🔹 Types of SQL JOINs: 1️⃣ INNER JOIN 👉 Returns only matching records from both tables 2️⃣ LEFT JOIN 👉 Returns all records from left table + matching from right 3️⃣ RIGHT JOIN 👉 Returns all records from right table + matching from left 4️⃣ FULL OUTER JOIN 👉 Returns all records from both tables (matched + unmatched) 5️⃣ CROSS JOIN 👉 Returns all possible combinations (Cartesian product) 6️⃣ SELF JOIN 👉 Joins a table with itself 💡 Example Query: SELECT * FROM TableA INNER JOIN TableB ON TableA.id = TableB.id; 🔄 Use Case: ✔️ Combine user & order data ✔️ Generate reports ✔️ Data analysis 🚀 Mastering JOINs = Strong SQL Skills + Better Data Handling #SQL #Database #DataEngineering #BackendDevelopment #Learning #Tech #SoftwareDevelopment #MySQL

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

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

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