30-day Roadmap plan for SQL covers beginner, intermediate, and advanced topics 👇

Week 1: Beginner Level

Day 1-3: Introduction and Setup
1. Day 1: Introduction to SQL, its importance, and various database systems.
2. Day 2: Installing a SQL database (e.g., MySQL, PostgreSQL).
3. Day 3: Setting up a sample database and practicing basic commands.

Day 4-7: Basic SQL Queries
4. Day 4: SELECT statement, retrieving data from a single table.
5. Day 5: WHERE clause and filtering data.
6. Day 6: Sorting data with ORDER BY.
7. Day 7: Aggregating data with GROUP BY and using aggregate functions (COUNT, SUM, AVG).

Week 2-3: Intermediate Level

Day 8-14: Working with Multiple Tables
8. Day 8: Introduction to JOIN operations.
9. Day 9: INNER JOIN and LEFT JOIN.
10. Day 10: RIGHT JOIN and FULL JOIN.
11. Day 11: Subqueries and correlated subqueries.
12. Day 12: Creating and modifying tables with CREATE, ALTER, and DROP.
13. Day 13: INSERT, UPDATE, and DELETE statements.
14. Day 14: Understanding indexes and optimizing queries.

Day 15-21: Data Manipulation
15. Day 15: CASE statements for conditional logic.
16. Day 16: Using UNION and UNION ALL.
17. Day 17: Data type conversions (CAST and CONVERT).
18. Day 18: Working with date and time functions.
19. Day 19: String manipulation functions.
20. Day 20: Error handling with TRY...CATCH.
21. Day 21: Practice complex queries and data manipulation tasks.

Week 4: Advanced Level

Day 22-28: Advanced Topics
22. Day 22: Working with Views.
23. Day 23: Stored Procedures and Functions.
24. Day 24: Triggers and transactions.
25. Day 25: Windows Function

Day 26-30: Real-World Projects
26. Day 26: SQL Project-1
27. Day 27: SQL Project-2
28. Day 28: SQL Project-3
29. Day 29: Practice questions set
30. Day 30: Final review and practice, explore advanced topics in depth, or work on a personal project.

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Top 10 SQL statements & functions used for data analysis

SELECT: To retrieve data from a database.
FROM: To specify the table or tables from which to retrieve data.
WHERE: To filter data based on specified conditions.
GROUP BY: To group rows with similar values into summary rows.
HAVING: To filter grouped data based on conditions.
ORDER BY: To sort the result set by one or more columns.
COUNT(): To count the number of rows or non-null values in a column.
SUM(): To calculate the sum of values in a numeric column.
AVG(): To calculate the average of values in a numeric column.
JOIN: To combine data from multiple tables based on a related column.

These SQL statements and functions are fundamental for data analysis and querying relational databases effectively.

Hope it helps :)

𝑪𝒐𝒎𝒑𝒓𝒆𝒉𝒆𝒏𝒔𝒊𝒗𝒆 𝒓𝒐𝒂𝒅𝒎𝒂𝒑 𝒕𝒐 𝒃𝒆𝒄𝒐𝒎𝒊𝒏𝒈 𝒂 𝒎𝒂𝒔𝒕𝒆𝒓 𝒊𝒏 𝑺𝑸𝑳:

1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒕𝒉𝒆 𝑩𝒂𝒔𝒊𝒄𝒔 𝒐𝒇 𝑺𝑸𝑳

𝐀. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞?: Understanding the concept of databases and relational databases.

𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐃𝐁𝐌𝐒): Learn about different DBMS like MySQL, PostgreSQL, SQL Server, Oracle.

𝐁. 𝐁𝐚𝐬𝐢𝐜 𝐒𝐐𝐋 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬

𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥:
𝐒𝐄𝐋𝐄𝐂𝐓: Basic retrieval of data.
𝐖𝐇𝐄𝐑𝐄: Filtering data based on conditions.
𝐎𝐑𝐃𝐄𝐑 𝐁𝐘: Sorting results.
𝐋𝐈𝐌𝐈𝐓: Limiting the number of rows returned.

𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧:
𝐈𝐍𝐒𝐄𝐑𝐓: Adding new data.
𝐔𝐏𝐃𝐀𝐓𝐄: Modifying existing data.
𝐃𝐄𝐋𝐄𝐓𝐄: Removing data.

2. 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐒𝐐𝐋 𝐒𝐤𝐢𝐥𝐥𝐬
𝐀. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥

𝐉𝐎𝐈𝐍𝐬: Understanding different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Using functions like COUNT, SUM, AVG, MIN, MAX.
𝐆𝐑𝐎𝐔𝐏 𝐁𝐘: Grouping data to perform aggregate calculations.
𝐇𝐀𝐕𝐈𝐍𝐆: Filtering groups based on aggregate values.

𝐁. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 𝐚𝐧𝐝 𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬
𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Using queries within queries.
𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Subqueries that reference columns from the outer query.

𝑪. 𝑫𝒂𝒕𝒂 𝑫𝒆𝒇𝒊𝒏𝒊𝒕𝒊𝒐𝒏 𝑳𝒂𝒏𝒈𝒖𝒂𝒈𝒆 (𝑫𝑫𝑳)
𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬: CREATE TABLE.
𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬: ALTER TABLE.
𝑹𝒆𝒎𝒐𝒗𝒊𝒏𝒈 𝑻𝒂𝒃𝒍𝒆𝒔: DROP TABLE.

3. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬
𝐀. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
𝐈𝐧𝐝𝐞𝐱𝐞𝐬: Understanding and creating indexes to speed up queries.
𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Techniques to write efficient SQL queries.

𝐁. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬
𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Using functions like ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG.
𝐂𝐓𝐄 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬): Using WITH to create temporary result sets.

𝐂. 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲
𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬: Using BEGIN, COMMIT, ROLLBACK.
𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Understanding isolation levels and locking mechanisms.

4. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬
𝐀. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧
𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Understanding normal forms and how to normalize databases.
𝐄𝐑 𝐃𝐢𝐚𝐠𝐫𝐚𝐦𝐬: Creating Entity-Relationship diagrams to model databases.

𝐁. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧
𝐄𝐓𝐋 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: Extract, Transform, Load processes for data integration.

𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐓𝐫𝐢𝐠𝐠𝐞𝐫𝐬: Writing and using stored procedures and triggers for complex logic and automation.

𝐂. 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐚𝐧𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬: Work on case studies involving complex database operations.

𝐂𝐚𝐩𝐬𝐭𝐨𝐧𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Develop comprehensive projects that showcase your SQL expertise.

𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐓𝐨𝐨𝐥𝐬
𝐁𝐨𝐨𝐤𝐬: "SQL in 10 Minutes, Sams Teach Yourself" by Ben Forta, "SQL for Data Scientists" by Renee M. P. Teate.
𝐎𝐧𝐥𝐢𝐧𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: Coursera, Udacity, edX, Khan Academy.
𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: LeetCode, HackerRank, Mode Analytics, SQLZoo.

Top 10 SQL concepts for data analysts

1. Database: A database is a structured collection of data that is organized and managed for easy access and retrieval. It stores data in tables, which are made up of rows and columns.

2. SQL (Structured Query Language): SQL is a programming language used to communicate with and manipulate databases. It allows data analysts to retrieve, insert, update, and delete data from a database.

3. Tables: Tables are the basic building blocks of a database. They consist of rows (records) and columns (fields) that store data in a structured format.

4. Queries: Queries are SQL statements used to retrieve specific data from a database. They can be simple or complex, involving multiple tables and conditions.

5. Joins: Joins are used to combine data from multiple tables based on a common field. They allow data analysts to retrieve related information from different tables in a single query.

6. Aggregation Functions: Aggregation functions, such as SUM, COUNT, AVG, MIN, and MAX, are used to perform calculations on groups of data. They are commonly used to summarize and analyze large datasets.

7. Filtering: Filtering allows data analysts to retrieve specific data based on certain conditions. It involves using the WHERE clause in SQL queries to specify the criteria for selecting records.

8. Sorting: Sorting allows data analysts to arrange data in a specific order. It involves using the ORDER BY clause in SQL queries to sort records based on one or more columns.

9. Subqueries: Subqueries are queries nested within another query. They allow data analysts to perform complex operations by using the results of one query as input for another query.

10. Data Manipulation Language (DML): DML statements, such as INSERT, UPDATE, and DELETE, are used to modify data in a database. Data analysts use these statements to insert new records, update existing records, or delete unwanted records.

Execution order in SQL 👆

SQL Interview Questions !!

🎗 Write a query to find all employees whose salaries exceed the company's average salary.
🎗 Write a query to retrieve the names of employees who work in the same department as 'John Doe'.
🎗 Write a query to display the second highest salary from the Employee table without using the MAX function twice.
🎗 Write a query to find all customers who have placed more than five orders.
🎗 Write a query to count the total number of orders placed by each customer.
🎗 Write a query to list employees who joined the company within the last 6 months.
🎗 Write a query to calculate the total sales amount for each product.
🎗 Write a query to list all products that have never been sold.
🎗 Write a query to remove duplicate rows from a table.
🎗 Write a query to identify the top 10 customers who have not placed any orders in the past year.

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SQL Interview Questions which can be asked in a Data Analyst Interview.

1️⃣ What is difference between Primary key and Unique key?
       
◼Primary key- A column or set of columns which uniquely identifies each record in a table. It can't contain null values and only one primary key
can exist in a table.

◼Unique key-Similar to primary key it also uniquely identifies each record in a table and can contain null values.Multiple Unique key can exist in a table.

2️⃣ What is a Candidate key?

◼A key or set of keys that uniquely identifies each record in a table.It is a combination of Primary and Alternate key.

3️⃣ What is a Constraint?

◼Specific rule or limit that we define in our table. E.g - NOT NULL,AUTO INCREMENT

4️⃣ Can you differentiate between TRUNCATE and DELETE?

◼TRUNCATE is a DDL command. It deletes the entire data from a table but preserves the structure of table.It doesn't deletes the data row by row hence faster than DELETE command, while DELETE is a DML command and it deletes the entire data based on specified condition else deletes the entire data,also it deletes the data row by row hence slower than TRUNCATE command.

5️⃣ What is difference between 'View' and 'Stored Procedure'?

◼A View is a virtual table that gets data from the base table .It is basically a Select statement,while Stored Procedure is a sql statement or set of sql statement stored on database server.

6️⃣ What is difference between a Common Table Expression and temporary table?

◼CTE is a temporary result set that is defined within execution scope of a single SELECT ,DELETE,UPDATE statement while temporary table is stored in TempDB and gets deleted  once the session expires.

7️⃣ Differentiate between a clustered index and a non-clustered index?

◼ A clustered index determines physical ordering of data in a table and a table can have only one clustered index while a non-clustered index is analogous to index of a book where index is stored at one place and data at other place and index will have pointers to storage location of the data,a table can have more than one non-clustered index.

8️⃣ Explain triggers ?

◼They are sql codes which are automatically executed in response to certain events on a table.They are used to maintain integrity of data.

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Many people pay too much to learn SQL, but my mission is to break down barriers. I have shared complete learning series to learn SQL from scratch.

Here are the links to the SQL series

Complete SQL Topics for Data Analyst: https://t.iss.one/sqlspecialist/523

Part-1: https://t.iss.one/sqlspecialist/524

Part-2: https://t.iss.one/sqlspecialist/525

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Part-12:
https://t.iss.one/sqlspecialist/541

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Part-15: https://t.iss.one/sqlspecialist/545

Part-16: https://t.iss.one/sqlspecialist/546

Part-17: https://t.iss.one/sqlspecialist/549

Part-18: https://t.iss.one/sqlspecialist/552

Part-19: https://t.iss.one/sqlspecialist/555

Part-20: https://t.iss.one/sqlspecialist/556

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Complete Python Topics for Data Analysts: https://t.iss.one/sqlspecialist/548

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Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

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✍️Best practices for writing SQL 📊queries:

1- Write SQL keywords in capital letters.

2- Use table aliases with columns when you are joining multiple tables.

3- Never use select *, always mention list of columns in select clause.

4- Add useful comments wherever you write complex logic. Avoid too many comments.

5- Use joins instead of subqueries when possible for better performance.

6- Create CTEs instead of multiple sub queries , it will make your query easy to read.

7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.

8- Never use order by in sub queries , It will unnecessary increase runtime.

9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.

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SQL Basics for Beginners: Must-Know Concepts

1. What is SQL? 
   SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax 
   SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
   - SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types 
   Databases store data in different formats. The most common data types are:
   - INT (Integer): For whole numbers.
   - VARCHAR(n) or TEXT: For storing text data.
   - DATE: For dates.
   - DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries 
   Here are some fundamental SQL operations:

   - SELECT Statement: Used to retrieve data from a database.
   

     SELECT column1, column2 FROM table_name;
     

   - WHERE Clause: Filters data based on conditions.
   

     SELECT * FROM table_name WHERE condition;
     

   - ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.
   

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

   - LIMIT: Limits the number of rows returned.
   

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause 
   The WHERE clause helps you filter data based on a condition:
 

   SELECT * FROM employees WHERE salary > 50000;
   

   You can use comparison operators like:
   - =: Equal to
   - >: Greater than
   - <: Less than
   - LIKE: For pattern matching

6. Aggregating Data 
   SQL provides functions to summarize or aggregate data:
   - COUNT(): Counts the number of rows.
   

     SELECT COUNT(*) FROM table_name;
     

   - SUM(): Adds up values in a column.
   

     SELECT SUM(salary) FROM employees;
     

   - AVG(): Calculates the average value.
   

     SELECT AVG(salary) FROM employees;
     

   - GROUP BY: Groups rows that have the same values into summary rows.
   

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL 
   Joins combine data from two or more tables:
   - INNER JOIN: Retrieves records with matching values in both tables.
   

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

   - LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
   

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data
   To add new data to a table, you use the INSERT INTO statement:
 

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data
   You can update existing data in a table using the UPDATE statement:
 

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data
    To remove data from a table, use the DELETE statement:
  

    DELETE FROM employees WHERE name = 'John Doe';
    

Hope it helps :)

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𝟱 𝗦𝗤𝗟 𝗠𝘆𝘁𝗵𝘀 𝗗𝗲𝗯𝘂𝗻𝗸𝗲𝗱 ❌ 𝗪𝗵𝗮𝘁 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝗢𝗳𝘁𝗲𝗻 𝗚𝗲𝘁 𝗪𝗿𝗼𝗻𝗴

SQL is super powerful, but some myths around it can trip up beginners.

Let’s clear up five common misunderstandings and set the record straight:

𝗠𝘆𝘁𝗵 𝟭: 𝗦𝗤𝗟 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝗽𝘂𝗹𝗹𝗶𝗻𝗴 𝗱𝗮𝘁𝗮.

✦ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: Nope, it’s not just for that! SQL can also create, modify, and manage databases, control access, and maintain data consistency.

✦ 𝗙𝗶𝘅 𝗶𝘁: Explore all the features of SQL, like DDL (for database design), DCL (for access control), and TCL (for transactions). It's more than just SELECT!

𝗠𝘆𝘁𝗵 𝟮: 𝗨𝘀𝗶𝗻𝗴 𝗦𝗘𝗟𝗘𝗖𝗧 *  𝗶𝘀 𝗳𝗶𝗻𝗲.

✦ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: It might be easy, but it’s not efficient. Pulling all columns wastes memory and slows down performance.

✦ 𝗙𝗶𝘅 𝗶𝘁: Only select the columns you actually need. It’s faster and cleaner.
Not great - SELECT * FROM employees;
Better - SELECT employee_id, name, department FROM employees;

𝗠𝘆𝘁𝗵 𝟯: 𝗦𝗤𝗟 𝗰𝗮𝗻'𝘁 𝗵𝗮𝗻𝗱𝗹𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀.

✦ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: SQL can do way more than basic queries! With concepts like window functions and CTEs, you can handle really complex data analysis.

✦ 𝗙𝗶𝘅 𝗶𝘁: Learn advanced SQL features like window functions (ROW_NUMBER(), RANK()) and CTEs to up your game.
Example - Ranking employees by salary within their department

WITH ranked_salaries AS (SELECT employee_id, salary, department,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank FROM employees)
SELECT * FROM ranked_salaries WHERE rank = 1;

𝗠𝘆𝘁𝗵 𝟰: 𝗦𝗹𝗼𝘄 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 𝗮𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲’𝘀 𝗳𝗮𝘂𝗹𝘁.

✦ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: It’s usually inefficient queries causing the slowdown. Things like missing indexes or unoptimized code can be the culprit.

✦ 𝗙𝗶𝘅 𝗶𝘁: Use indexes properly, avoid complex calculations in WHERE clauses, and check your query execution plan to spot bottlenecks.

𝗠𝘆𝘁𝗵 𝟱: 𝗦𝗤𝗟 𝗶𝘀 𝗼𝘂𝘁𝗱𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗱 𝘀𝗼𝗼𝗻.

✦ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: SQL is here to stay! Despite the rise of NoSQL, SQL remains the backbone for structured data.

✦ 𝗙𝗶𝘅 𝗶𝘁: Stay current and explore how SQL integrates with big data platforms and cloud databases. It’s more relevant than ever.

Don’t let these myths hold you back. SQL is powerful, and when you understand it fully, you can do amazing things with your data.

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Most Asked SQL Interview Questions at MAANG Companies🔥🔥

Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;

2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.

3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;

4. What is the difference between WHERE & HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;

5. How do you calculate average, sum, minimum & maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

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SQL, or Structured Query Language, is a domain-specific language used to manage and manipulate relational databases. Here's a brief A-Z overview by @sqlanalyst

A - Aggregate Functions: Functions like COUNT, SUM, AVG, MIN, and MAX used to perform operations on data in a database.

B - BETWEEN: A SQL operator used to filter results within a specific range.

C - CREATE TABLE: SQL statement for creating a new table in a database.

D - DELETE: SQL statement used to delete records from a table.

E - EXISTS: SQL operator used in a subquery to test if a specified condition exists.

F - FOREIGN KEY: A field in a database table that is a primary key in another table, establishing a link between the two tables.

G - GROUP BY: SQL clause used to group rows that have the same values in specified columns.

H - HAVING: SQL clause used in combination with GROUP BY to filter the results.

I - INNER JOIN: SQL clause used to combine rows from two or more tables based on a related column between them.

J - JOIN: Combines rows from two or more tables based on a related column.

K - KEY: A field or set of fields in a database table that uniquely identifies each record.

L - LIKE: SQL operator used in a WHERE clause to search for a specified pattern in a column.

M - MODIFY: SQL command used to modify an existing database table.

N - NULL: Represents missing or undefined data in a database.

O - ORDER BY: SQL clause used to sort the result set in ascending or descending order.

P - PRIMARY KEY: A field in a table that uniquely identifies each record in that table.

Q - QUERY: A request for data from a database using SQL.

R - ROLLBACK: SQL command used to undo transactions that have not been saved to the database.

S - SELECT: SQL statement used to query the database and retrieve data.

T - TRUNCATE: SQL command used to delete all records from a table without logging individual row deletions.

U - UPDATE: SQL statement used to modify the existing records in a table.

V - VIEW: A virtual table based on the result of a SELECT query.

W - WHERE: SQL clause used to filter the results of a query based on a specified condition.

X - (E)XISTS: Used in conjunction with SELECT to test the existence of rows returned by a subquery.

Z - ZERO: Represents the absence of a value in numeric fields or the initial state of boolean fields.

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SQL Interview Questions !!

🎗 Write a query to find all employees whose salaries exceed the company's average salary.
🎗 Write a query to retrieve the names of employees who work in the same department as 'John Doe'.
🎗 Write a query to display the second highest salary from the Employee table without using the MAX function twice.
🎗 Write a query to find all customers who have placed more than five orders.
🎗 Write a query to count the total number of orders placed by each customer.
🎗 Write a query to list employees who joined the company within the last 6 months.
🎗 Write a query to calculate the total sales amount for each product.
🎗 Write a query to list all products that have never been sold.
🎗 Write a query to remove duplicate rows from a table.
🎗 Write a query to identify the top 10 customers who have not placed any orders in the past year.

SQL Learning plan in 2025

|-- Week 1: Introduction to SQL
|   |-- SQL Basics
|   |   |-- What is SQL?
|   |   |-- History and Evolution of SQL
|   |   |-- Relational Databases
|   |-- Setting up for SQL
|   |   |-- Installing MySQL/PostgreSQL
|   |   |-- Setting up a Database
|   |   |-- Basic SQL Syntax
|   |-- First SQL Queries
|   |   |-- SELECT Statements
|   |   |-- WHERE Clauses
|   |   |-- Basic Filtering
|
|-- Week 2: Intermediate SQL
|   |-- Advanced SELECT Queries
|   |   |-- ORDER BY
|   |   |-- LIMIT
|   |   |-- Aliases
|   |-- Joining Tables
|   |   |-- INNER JOIN
|   |   |-- LEFT JOIN
|   |   |-- RIGHT JOIN
|   |   |-- FULL OUTER JOIN
|   |-- Aggregations
|   |   |-- COUNT, SUM, AVG, MIN, MAX
|   |   |-- GROUP BY
|   |   |-- HAVING Clauses
|
|-- Week 3: Advanced SQL Techniques
|   |-- Subqueries
|   |   |-- Basic Subqueries
|   |   |-- Correlated Subqueries
|   |-- Window Functions
|   |   |-- ROW_NUMBER, RANK, DENSE_RANK
|   |   |-- NTILE, LEAD, LAG
|   |-- Advanced Joins
|   |   |-- Self Joins
|   |   |-- Cross Joins
|   |-- Data Types and Functions
|   |   |-- Date Functions
|   |   |-- String Functions
|   |   |-- Numeric Functions
|
|-- Week 4: Database Design and Normalization
|   |-- Database Design Principles
|   |   |-- ER Diagrams
|   |   |-- Relationships and Cardinality
|   |-- Normalization
|   |   |-- First Normal Form (1NF)
|   |   |-- Second Normal Form (2NF)
|   |   |-- Third Normal Form (3NF)
|   |-- Indexes and Performance Tuning
|   |   |-- Creating Indexes
|   |   |-- Understanding Execution Plans
|   |   |-- Optimizing Queries
|
|-- Week 5: Stored Procedures and Functions
|   |-- Stored Procedures
|   |   |-- Creating Stored Procedures
|   |   |-- Parameters in Stored Procedures
|   |   |-- Error Handling
|   |-- Functions
|   |   |-- Scalar Functions
|   |   |-- Table-Valued Functions
|   |   |-- System Functions
|
|-- Week 6: Transactions and Concurrency
|   |-- Transactions
|   |   |-- ACID Properties
|   |   |-- COMMIT and ROLLBACK
|   |   |-- Savepoints
|   |-- Concurrency Control
|   |   |-- Locking Mechanisms
|   |   |-- Isolation Levels
|   |   |-- Deadlocks and How to Avoid Them
|
|-- Week 7-8: Advanced SQL Topics
|   |-- Triggers
|   |   |-- Creating and Using Triggers
|   |   |-- AFTER and BEFORE Triggers
|   |   |-- INSTEAD OF Triggers
|   |-- Views
|   |   |-- Creating Views
|   |   |-- Updating Views
|   |   |-- Indexed Views
|   |-- Security
|   |   |-- User Management
|   |   |-- Roles and Permissions
|   |   |-- SQL Injection Prevention
|
|-- Week 9-11: Real-world Applications and Projects
|   |-- Capstone Project
|   |   |-- Designing a Database Schema
|   |   |-- Implementing the Schema
|   |   |-- Writing Complex Queries
|   |   |-- Optimizing and Tuning
|   |-- ETL Processes
|   |   |-- Data Extraction
|   |   |-- Data Transformation
|   |   |-- Data Loading
|   |-- Data Analysis and Reporting
|   |   |-- Creating Reports
|   |   |-- Data Visualization with SQL
|   |   |-- Integration with BI Tools
|
|-- Week 12: Post-Project Learning
|   |-- Database Administration
|   |   |-- Backup and Restore
|   |   |-- Maintenance Plans
|   |   |-- Performance Monitoring
|   |-- SQL in the Cloud
|   |   |-- AWS RDS
|   |   |-- Google Cloud SQL
|   |   |-- Azure SQL Database
|   |-- Continuing Education
|   |   |-- Advanced SQL Topics

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SQL query optimization techniques

✅ Index Optimization

➡️ Ensure indexes are created on columns that are frequently used in 'WHERE' clauses, 'JOIN' conditions and as part of 'ORDER BY' clauses.
➡️Use composite indexes for columns that are frequently queried together.
➡️Regularly analyze and rebuild fragmented indexes.

✅ Query Refactoring

➡️ Break complex queries into simpler subqueries or use common table expressions (CTEs).
➡️ Avoid unnecessary columns in the 'SELECT' clause to reduce the data processed.

✅ Join Optimization

➡️ Use the appropriate type of join (INNER JOIN, LEFT JOIN, etc.) based on the requirements.
➡️ Ensure join columns are indexed to speed up the join operation.
➡️ Consider the join order, starting with the smallest table.

✅ Use of Proper Data Types

➡️ Choose the most efficient data type for your columns to reduce storage and improve performance.
➡️ Avoid using 'SELECT *', specify only the columns you need.

✅ Query Execution Plan Analysis

➡️ Use tools like 'EXPLAIN or 'EXPLAIN PLAN' to analyze how the database executes a query.
➡️ Look for full table scans, inefficient joins, or unnecessary sorting operations.

✅ Temporary Tables and Materialized Views

➡️ Use temporary tables to store intermediate results that are reused multiple times in complex queries.
➡️ Use materialized views to store precomputed results of expensive queries.

✅ Efficient Use of Subqueries and CTEs

➡️ Replace correlated subqueries with joins when possible to avoid repeated execution.
➡️ Use CTEs to improve readability and reusability, and sometimes performance, of complex queries.

✅ Optimization of Aggregate Functions

➡️ Use indexed columns in 'GROUP BY' clauses to speed up aggregation.
➡️ Consider using window functions for complex aggregations instead of traditional 'GROUP BY'.

✅ Avoiding Functions in Predicates

➡️ Avoid using functions on columns in the 'WHERE' clause as it can prevent the use of indexes.
➡️ Rewrite conditions to allow the use of indexes.

✅ Parameter Sniffing and Query Caching

➡️ Be aware of parameter sniffing issues where SQL Server caches execution plans based on initial parameter values.
➡️ Use query hints or option recompile to address specific performance issues.
➡️ Take advantage of query caching mechanisms where appropriate to reuse execution plans.

🛠 By applying these advanced techniques, you can significantly enhance the performance of your SQL queries and ensure that your database runs efficiently.

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Complete SQL guide for Data Analytics

1. Introduction to SQL

What is SQL?

• SQL (Structured Query Language) is a domain-specific language used for managing and manipulating relational databases. It allows you to interact with data by querying, inserting, updating, and deleting records in a database.
• SQL is essential for Data Analytics because it enables analysts to retrieve and manipulate data for analysis, reporting, and decision-making.

Applications in Data Analytics

• Data Retrieval: SQL is used to pull data from databases for analysis.
• Data Transformation: SQL helps clean, aggregate, and transform data into a usable format for analysis.
• Reporting: SQL can be used to create reports by summarizing data or applying business rules.
• Data Modeling: SQL helps in preparing datasets for further analysis or machine learning.

2. SQL Basics

Data Types

SQL supports various data types that define the kind of data a column can hold:
• Numeric Data Types:
• INT: Integer numbers, e.g., 123.
• DECIMAL(p,s): Exact numbers with a specified precision and scale, e.g., DECIMAL(10,2) for numbers like 12345.67.
• FLOAT: Approximate numbers, e.g., 123.456.
• String Data Types:
• CHAR(n): Fixed-length strings, e.g., CHAR(10) will always use 10 characters.
• VARCHAR(n): Variable-length strings, e.g., VARCHAR(50) can store up to 50 characters.
• TEXT: Long text data, e.g., descriptions or long notes.
• Date/Time Data Types:
• DATE: Stores date values, e.g., 2024-12-01.
• DATETIME: Stores both date and time, e.g., 2024-12-01 12:00:00.

Creating and Modifying Tables

You can create, alter, and drop tables using SQL commands:

-- Create a table with columns for ID, name, salary, and hire date
CREATE TABLE employees (
id INT PRIMARY KEY,
name VARCHAR(50),
salary DECIMAL(10, 2),
hire_date DATE
);

-- Alter an existing table to add a new column for department
ALTER TABLE employees ADD department VARCHAR(50);

-- Drop a table (delete it from the database)
DROP TABLE employees;

Data Insertion, Updating, and Deletion

SQL allows you to manipulate data using INSERT, UPDATE, and DELETE commands:

-- Insert a new employee record
INSERT INTO employees (id, name, salary, hire_date, department)
VALUES (1, 'Alice', 75000.00, '2022-01-15', 'HR');

-- Update the salary of employee with id 1
UPDATE employees
SET salary = 80000
WHERE id = 1;

-- Delete the employee record with id 1
DELETE FROM employees WHERE id = 1;

3. Data Retrieval

SELECT Statement

The SELECT statement is used to retrieve data from a database:

SELECT * FROM employees; -- Retrieve all columns
SELECT name, salary FROM employees; -- Retrieve specific columns

Filtering Data with WHERE

The WHERE clause filters data based on specific conditions:

SELECT * FROM employees
WHERE salary > 60000 AND department = 'HR'; -- Filter records based on salary and department

Sorting Data with ORDER BY

The ORDER BY clause sorts the result set by one or more columns:

SELECT * FROM employees
ORDER BY salary DESC; -- Sort by salary in descending order

Aliasing

You can use aliases to rename columns or tables for clarity:

SELECT name AS employee_name, salary AS monthly_salary FROM employees;

4. Aggregate Functions

Aggregate functions perform calculations on a set of values and return a single result.

Common Aggregate Functions

SELECT COUNT(*) AS total_employees, AVG(salary) AS average_salary
FROM employees; -- Count total employees and calculate the average salary

GROUP BY and HAVING

• GROUP BY is used to group rows sharing the same value in a column.
• HAVING filters groups based on aggregate conditions.

-- Find average salary by department
SELECT department, AVG(salary) AS average_salary
FROM employees
GROUP BY department;

-- Filter groups with more than 5 employees
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

5. Joins

Joins are used to combine rows from two or more tables based on related columns.

Types of Joins