SQL Filtering Explained | WHERE, DISTINCT, IN, LIKE & CASE WHEN

Want to write better SQL queries as a Data Analyst? In this video, we'll learn important SQL filtering techniques and the CASE statement with practical examples using a sample Sales dataset. In our previous video, we learned how to write SQL queries using SELECT, WHERE, ORDER BY, GROUP BY, HAVING and Window Functions. Now let's take the next step and learn how to filter data more effectively, apply multiple conditions, and create meaningful business categories using SQL. 🎯 *WHAT YOU WILL LEARN* 1. DISTINCT – Remove duplicate values from query results. 2. TOP – Retrieve a limited number of rows in SQL Server. 3. IN – Filter records using multiple values. 4. BETWEEN – Filter records within a specified range. 5. LIKE – Search for matching text patterns. 6. AND, OR and NOT – Combine and reverse filtering conditions. 7. IS NULL and IS NOT NULL – Identify missing values. 8. CASE WHEN – Apply conditional business logic. 9. Practical SQL queries combining multiple concepts. 💡 *PRACTICAL EXAMPLES* We'll use business scenarios such as: Finding customers from selected cities. Filtering sales within a particular range. Searching customer names using LIKE. Identifying records with missing values. Categorizing customers into High, Medium and Low value segments. 📚 *SQL FOR DATA ANALYST | BEGINNER TO ADVANCED* This video is part of our SQL learning playlist. Follow the series to build your SQL skills step by step. ▶ Previous Video: SQL for Data Analysts – Write Real Queries | SELECT to Window Functions ▶ Next Video: SQL Joins – Combining Data from Multiple Tables 👨‍💻 *WHO SHOULD WATCH?* Beginners learning SQL Aspiring Data Analysts Power BI Developers Business Analysts Anyone preparing for SQL interviews 💬 *PRACTICE QUESTION* What is the difference between `WHERE` and `HAVING` in SQL? And can you use `CASE WHEN` inside a SELECT statement? Write your answers in the comments! If this tutorial helps you, LIKE, SHARE and SUBSCRIBE to DataJivi. *DataJivi • Learn • Build • Create* #SQL #SQLForDataAnalysts #SQLTutorial #datajivi