SQL ROLLUP for Hierarchical Aggregation and Totals

📢 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 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

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

Posit PBC just released ggsql, a declarative SQL based syntax for data visualisation. I spent some time this week exploring how to use it with the Oracle Autonomous Database. Code: https://lnkd.in/eHMfpxRA Blog: https://lnkd.in/eCiZW54N #datascience #datavisualization #sql #oracle

Cut a PostgreSQL job from 10–12 hours down to ~2hrs 🚀 This was a data loading + cleaning workflow, so the focus was on reducing unnecessary overhead and optimizing execution paths. Key optimizations that worked: 🔹 Used UNLOGGED tables for staging Instead of creating a logged table with type casting on existing columns, I switched to an UNLOGGED table and created new columns with proper casting (e.g., dates). → Reduced WAL overhead significantly for non-critical data. 🔹 Tuned session-level settings Increased work_mem Set synchronous_commit = off (only for this session) → Improved intermediate operations and write performance. 🔹 Optimized indexing strategy Created indexes in the order of joins Focused only on columns used in joins and filters → Avoided index bloat and improved query planning. 🔹 Avoided unnecessary indexing Indexing everything is tempting—but selective indexing made a big difference in execution time. Takeaway: Small, context-aware changes in Postgres can lead to massive performance gains—especially in ETL or staging workloads. #PostgreSQL #SQL #DatabaseOptimization #QueryOptimization #DataEngineering #PerformanceTuning #DatabasePerformance #ETL #DataProcessing #SoftwareEngineering #TechTips #Programming #Engineering #Indexing #SQLPerformance #DataWorkflows #Scalability

🚀 Day 29 of My SQL Journey – Subqueries (Part 4) Today I explored how subqueries can be used based on their location in SQL queries 📊 Instead of just learning types, I focused on where exactly we can use subqueries: 🔹 Subquery in SELECT Used to display calculated values alongside each row 👉 Example: Showing each customer with overall average amount 🔹 Subquery in WHERE Used for filtering based on conditions 👉 Example: Finding customers with second highest salary or matching categories 🔹 Subquery in FROM (Derived Table) Used to create a temporary table for further querying 👉 Example: Calculating average sales per store and using it as a table 🔹 Subquery in HAVING Used to filter grouped results based on aggregate conditions 👉 Example: Finding stores with transaction count less than overall transactions 💡 Key Takeaway: Subqueries are powerful because they help break complex problems into smaller steps and allow placing logic exactly where it's needed. 📈 Slowly moving from basic queries to writing more optimized and structured SQL! #SQL #LearningJourney #Day29 #Subqueries #Database #Coding #Tech #StudentDeveloper

https://lnkd.in/dR2jcm2s Hi Connections, The SQL WHERE clause is used to filter records based on conditions, and it supports conditional operators (like =, <, >), logical operators (AND, OR, NOT), and wildcard characters (used with LIKE for pattern matching). These tools make queries highly flexible and precise. https://lnkd.in/dR2jcm2s #sasdeveloper #sasprogramming #sasdevelopers #sasdeveloper #sqldeveloper #sql #sqlserver #sqldatabase #dataanalytics #dataanalysis #dataanalyst #SAS #SQL #DataAnalytics #Programming #DataScience #LearningAndDevelopment #KnowledgeSharing #CareerGrowth Please Subscribe, watch, comment and share