Exploring ggsql with Oracle Autonomous Database

Strengthening My SQL & DBMS Fundamentals Today, I focused on building a strong foundation in databases by working on some core SQL concepts: • Creating databases and tables • Inserting and selecting data • Filtering records using WHERE • Updating records with UPDATE • Deleting specific data using DELETE with WHERE • Modifying structure with ALTER TABLE • Using DISTINCT, sorting, LIMIT, and OFFSET • Performing aggregations (COUNT, SUM, etc.) • Grouping data with GROUP BY I also practiced these concepts hands-on by solving SQL labs on labs.chaicode.com, which helped me understand how these queries work in real scenarios. Consistent practice is the key. Thanks to Hitesh Choudhary ,Piyush Garg and Chai Aur Code team for teaching these concepts easily and creating such amazing labs for hands on practices #DBMS #SQL #Backend #ChaiCode

📢 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

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

📢 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

Week 12, Class 23 Complete ✅ Covered SQL and DBMS fundamentals: > SQL vs NoSQL > CREATE TABLE & ALTER TABLE > INSERT, SELECT, UPDATE, DELETE > WHERE, ORDER BY, DISTINCT > LIMIT, OFFSET, GROUP BY, HAVING > COUNT, SUM, AVG > Constraints and table operations Strengthening backend and database concepts step by step 🚀 Hitesh Choudhary @piyushgarg Anirudh Jwala Suraj Kumar Jha Akash Kadlag Jay Kadlag Chai Aur Code Nikhil Rathore #DBMS #SQL #Backend #ChaiCode

📢 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 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

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

🔍 One SQL Query Taught Me an Important Lesson Early in my journey as an Analyst, I used to rely heavily on UI validation. If the screen looked correct, I assumed the logic was correct. But one day, while checking data in PostgreSQL, I noticed something unusual — the UI showed correct values, but the database calculations were slightly different. That moment changed my approach completely. Since then, I always: ✔️ Validate data at database level ✔️ Cross-check calculations ✔️ Verify edge cases using SQL Sometimes, the real story is hidden behind the UI — inside the data. What is one SQL query that helped you catch an important issue? #SQL #PostgreSQL #BusinessAnalyst #QualityAssurance #TechLearning #PersonalBranding

🚀 Day 7 | SQL Learning Journey — Mastering SQL Operators ⚡ Today I went deep into SQL Operators — the real building blocks behind writing precise and efficient queries. Operators may look simple, but they control how data is filtered, compared, and manipulated inside the database. Here’s what I explored today 👇 🔹 Arithmetic Operators +, -, *, /, % Used for performing calculations directly in queries 🔹 Comparison Operators =, !=, >, <, >=, <= Helped in comparing values and filtering specific records 🔹 Logical Operators AND, OR, NOT Combined multiple conditions to make queries smarter 🔹 Special Operators BETWEEN – filtering within a range IN – matching multiple values LIKE – pattern-based searching IS NULL – handling missing values 🔹 Bitwise & Compound Operators (basics) #sql #queries #mysql #structured #query #language