SQL for Data Analysis

Did you know how NULL values are treated in GROUP BY?

In SQL, when you use GROUP BY, all NULL values in a column are grouped together, as if they were one single value.

This can be surprising because, outside of GROUP BY, SQL treats NULL as an unknown, meaning that NULL = NULL is false!

Here's an example:

SELECT department, COUNT(*) FROM employees GROUPBY department;

If some department values are NULL, SQL groups all those NULL entries together. This is helpful for aggregation, but it can also lead to unexpected results if you're not aware of it.

This behavior is unique to GROUP BY and highlights SQL’s different handling of NULL depending on context. So next time you’re aggregating, don’t forget to consider those NULLs!

Here you can find essential SQL Interview Resources👇
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SQL Data Cleaning Methods You Should Know for Data Science:

1. Identifying Missing Data
2. Removing Duplicate Records
3. Handling Missing Data
4. Standardizing Data
5. Correcting Data Entry Errors

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14 Days Roadmap to learn SQL

𝗗𝗮𝘆 𝟭: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝗦𝗤𝗟
Topics to Cover:
- What is SQL?
- Different types of databases (Relational vs. Non-Relational)
- SQL vs. NoSQL
- Overview of SQL syntax
Practice:
- Install a SQL database (e.g., MySQL, PostgreSQL, SQLite)
- Explore an online SQL editor like SQLFiddle or DB Fiddle

𝗗𝗮𝘆 𝟮: 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝗶𝗲𝘀
Topics to Cover:
- SELECT statement
- Filtering with WHERE clause
- DISTINCT keyword
Practice:
- Write simple SELECT queries to retrieve data from single table
- Filter records using WHERE clauses

𝗗𝗮𝘆 𝟯: 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴
Topics to Cover:
- ORDER BY clause
- Using LIMIT/OFFSET for pagination
- Comparison and logical operators
Practice:
- Sort data with ORDER BY
- Apply filtering with multiple conditions use AND/OR

𝗗𝗮𝘆 𝟰: 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻𝘀
Topics to Cover:
- Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
- GROUP BY and HAVING clauses
Practice:
- Perform aggregation on dataset
- Group data and filter groups using HAVING

𝗗𝗮𝘆 𝟱: 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗧𝗮𝗯𝗹𝗲𝘀 - 𝗝𝗼𝗶𝗻𝘀
Topics to Cover:
- Introduction to Joins (INNER, LEFT, RIGHT, FULL)
- CROSS JOIN and self-joins
Practice:
- Write queries using different types of JOINs to combine data from multiple table

𝗗𝗮𝘆 𝟲: 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗡𝗲𝘀𝘁𝗲𝗱 𝗤𝘂𝗲𝗿𝗶𝗲𝘀
Topics to Cover:
- Subqueries in SELECT, WHERE, and FROM clauses
- Correlated subqueries
Practice:
- Write subqueries to filter, aggregate, an select data

𝗗𝗮𝘆 𝟳: 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻
Topics to Cover:
- Understanding ERD (Entity Relationship Diagram)
- Normalization (1NF, 2NF, 3NF)
- Primary and Foreign Key
Practice:
- Design a simple database schema and implement it in your database

𝗗𝗮𝘆 𝟴: 𝗠𝗼𝗱𝗶𝗳𝘆𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 - 𝗜𝗡𝗦𝗘𝗥𝗧, 𝗨𝗣𝗗𝗔𝗧𝗘, 𝗗𝗘𝗟𝗘𝗧𝗘
Topics to Cover:
- INSERT INTO statement
- UPDATE and DELETE statement
- Transactions and rollback
Practice:
- Insert, update, and delete records in a table
- Practice transactions with COMMIT and ROLLBACK

𝗗𝗮𝘆 𝟵: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
Topics to Cover:
- String functions (CONCAT, SUBSTR, etc.)
- Date functions (NOW, DATEADD, DATEDIFF)
- CASE statement
Practice:
- Use string and date function in queries
- Write conditional logic using CASE

𝗗𝗮𝘆 𝟭𝟬: 𝗩𝗶𝗲𝘄𝘀 𝗮𝗻𝗱 𝗜𝗻𝗱𝗲𝘅𝗲𝘀
Topics to Cover:
- Creating and using Views
- Indexes: What they are and how they work
- Pros and cons of using indexes
Practice:
- Create and query views
- Explore how indexes affect query performance

Here you can find essential SQL Interview Resources👇
https://topmate.io/analyst/864764

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Think you've mastered SQL just because you can use CTEs, views, or SQL commands?

Think again.

To be a true SQL MASTER, you need to:

🔥 OPTIMIZE YOUR QUERIES for maximum speed and performance.
🔥 Decode EXECUTION PLANS to fine-tune every detail.
🔥 Know when to use INDEXES and avoid slow TABLE SCANS.
🔥 Write queries that handle MASSIVE DATASETS without breaking a sweat.
🔥 Continuously enhance your DATABASE DESIGN for improved performance.

SQL MASTERY isn’t about knowing the basics ,it’s about making your queries work SMARTER, FASTER, and at SCALE.

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💾 SQL : Mastering Data Management 💾

1. 🗂️ Understand Database Structures
2. ✍️ Learn Basic SQL Queries (SELECT, INSERT)
3. 🔍 Master Filtering with WHERE & HAVING
4. 🔄 Utilize Joins for Complex Data Retrieval
5. 🧮 Aggregate Functions (SUM, COUNT, AVG)
6. 🔧 Optimize with Indexing
7. 🛡️ Implement Data Integrity Constraints
8. 🔑 Understand Primary & Foreign Keys
9. 💡 Use Subqueries & Nested Queries
10. 📊 Create Views for Simplified Access
11. 🕵️ Analyze with GROUP BY & ORDER BY
12. 🚀 Perform Data Import/Export
13. 🔀 Work with Stored Procedures & Functions
14. 💽 Database Backups & Recovery Planning
15. 🔄 Transactions & Error Handling with COMMIT/ROLLBACK

Here you can find essential SQL Interview Resources👇
https://topmate.io/analyst/864764

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14 Days Roadmap to learn SQL

𝗗𝗮𝘆 𝟭: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝗦𝗤𝗟
Topics to Cover:
- What is SQL?
- Different types of databases (Relational vs. Non-Relational)
- SQL vs. NoSQL
- Overview of SQL syntax
Practice:
- Install a SQL database (e.g., MySQL, PostgreSQL, SQLite)
- Explore an online SQL editor like SQLFiddle or DB Fiddle

𝗗𝗮𝘆 𝟮: 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝗶𝗲𝘀
Topics to Cover:
- SELECT statement
- Filtering with WHERE clause
- DISTINCT keyword
Practice:
- Write simple SELECT queries to retrieve data from single table
- Filter records using WHERE clauses

𝗗𝗮𝘆 𝟯: 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴
Topics to Cover:
- ORDER BY clause
- Using LIMIT/OFFSET for pagination
- Comparison and logical operators
Practice:
- Sort data with ORDER BY
- Apply filtering with multiple conditions use AND/OR

𝗗𝗮𝘆 𝟰: 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻𝘀
Topics to Cover:
- Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
- GROUP BY and HAVING clauses
Practice:
- Perform aggregation on dataset
- Group data and filter groups using HAVING

𝗗𝗮𝘆 𝟱: 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗧𝗮𝗯𝗹𝗲𝘀 - 𝗝𝗼𝗶𝗻𝘀
Topics to Cover:
- Introduction to Joins (INNER, LEFT, RIGHT, FULL)
- CROSS JOIN and self-joins
Practice:
- Write queries using different types of JOINs to combine data from multiple table

𝗗𝗮𝘆 𝟲: 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗡𝗲𝘀𝘁𝗲𝗱 𝗤𝘂𝗲𝗿𝗶𝗲𝘀
Topics to Cover:
- Subqueries in SELECT, WHERE, and FROM clauses
- Correlated subqueries
Practice:
- Write subqueries to filter, aggregate, an select data

𝗗𝗮𝘆 𝟳: 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻
Topics to Cover:
- Understanding ERD (Entity Relationship Diagram)
- Normalization (1NF, 2NF, 3NF)
- Primary and Foreign Key
Practice:
- Design a simple database schema and implement it in your database

𝗗𝗮𝘆 𝟴: 𝗠𝗼𝗱𝗶𝗳𝘆𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 - 𝗜𝗡𝗦𝗘𝗥𝗧, 𝗨𝗣𝗗𝗔𝗧𝗘, 𝗗𝗘𝗟𝗘𝗧𝗘
Topics to Cover:
- INSERT INTO statement
- UPDATE and DELETE statement
- Transactions and rollback
Practice:
- Insert, update, and delete records in a table
- Practice transactions with COMMIT and ROLLBACK

𝗗𝗮𝘆 𝟵: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
Topics to Cover:
- String functions (CONCAT, SUBSTR, etc.)
- Date functions (NOW, DATEADD, DATEDIFF)
- CASE statement
Practice:
- Use string and date function in queries
- Write conditional logic using CASE

𝗗𝗮𝘆 𝟭𝟬: 𝗩𝗶𝗲𝘄𝘀 𝗮𝗻𝗱 𝗜𝗻𝗱𝗲𝘅𝗲𝘀
Topics to Cover:
- Creating and using Views
- Indexes: What they are and how they work
- Pros and cons of using indexes
Practice:
- Create and query views
- Explore how indexes affect query performance

Here you can find essential SQL Interview Resources👇
https://topmate.io/analyst/864764

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SQL can be simple—if you learn it the smart way..



If you’re aiming to become a data analyst, mastering SQL is non-negotiable.
Here’s a smart roadmap to ace it:

1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.

2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.

3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.

4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.

5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.

6. Optimization: Study indexing and query optimization for faster, more efficient queries.

7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.

The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! 💪

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15 essential sql interview questions

1️⃣ Explain Order of Execution of SQL query
2️⃣ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( 💡 majority struggle )
3️⃣ Write a query to find the cumulative sum/Running Total
4️⃣ Find the Most selling product by sales/ highest Salary of employees
5️⃣ Write a query to find the 2nd/nth highest Salary of employees
6️⃣ Difference between union vs union all
7️⃣ Identify if there any duplicates in a table
8️⃣ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9️⃣ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1️⃣ 0️⃣ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1️⃣ 1️⃣ Write a query to find the Running Difference (Ideal sol'n using windows function)
1️⃣ 2️⃣ Write a query to display year on year/month on month growth
1️⃣ 3️⃣ Write a query to find rolling average of daily sign-ups
1️⃣ 4️⃣ Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function)
1️⃣ 5️⃣ Write a query to find the cumulative sum using self join
(helps in understanding the logical approach, ideally this question is solved via windows function)

Must Know Differences for SQL :



👉 INNER JOIN vs OUTER JOIN:
INNER JOIN: Returns only matching rows from both tables.
OUTER JOIN: Returns matching rows plus non-matching rows from one or both tables (LEFT, RIGHT, or FULL).

👉 VARCHAR vs NVARCHAR:
VARCHAR: Stores non-Unicode characters, taking 1 byte per character.
NVARCHAR: Stores Unicode characters, taking 2 bytes per character.

👉 PRIMARY KEY vs UNIQUE KEY:
PRIMARY KEY: Ensures unique values and does not allow NULLs.
UNIQUE KEY: Ensures unique values but allows a single NULL.

👉 CLUSTERED INDEX vs NON-CLUSTERED INDEX:
CLUSTERED INDEX: Sorts and stores data rows in the table based on the indexed column.
NON-CLUSTERED INDEX: Creates a separate structure from the data rows, with pointers to the original data.

👉 TEMPORARY TABLE vs TABLE VARIABLE:
TEMPORARY TABLE: Created in the tempdb database, persists for the session or until dropped.
TABLE VARIABLE: Stored in memory, scoped to the batch or stored procedure, and typically faster for small datasets.

👉 VIEW vs MATERIALIZED VIEW:
VIEW: A virtual table that does not store data, dynamically retrieves data from the base tables.
MATERIALIZED VIEW: Stores the result of the query physically, providing faster access to large datasets.

👉 STORED PROCEDURE vs FUNCTION:
STORED PROCEDURE: Executes a set of SQL statements and can return multiple values, including result sets.
FUNCTION: Returns a single value or table and can be used in SQL expressions.

👉 SIMPLE RECOVERY MODEL vs FULL RECOVERY MODEL:
SIMPLE RECOVERY MODEL: Does not log transactions in detail, preventing point-in-time restores.
FULL RECOVERY MODEL: Logs all transactions, allowing for point-in-time restores.

👉 RAISERROR vs THROW:
RAISERROR: Used to generate a custom error message, providing more control over the error handling.
THROW: Simplified error handling, introduced in SQL Server 2012, and rethrows the error.

👉 DELETE vs TRUNCATE:
DELETE: Removes rows based on a condition and logs each row deletion.
TRUNCATE: Removes all rows from a table quickly without logging individual row deletions.

Here you can find essential SQL Interview Resources👇
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SQL queries that are commonly asked during interviews: 3.O .............



1. Find employees who report to a specific manager:
SELECT employee_id, employee_name
FROM Employee
WHERE manager_id = 101; -- Replace 101 with the specific manager_id;

2. Get the top 3 highest paid employees:
SELECT employee_id, salary
FROM Employee
ORDER BY salary DESC
LIMIT 3;

3. Find products with sales above the average sales:
SELECT product_id, SUM(sales_amount) AS total_sales
FROM Sales
GROUP BY product_id
HAVING total_sales > (SELECT AVG(sales_amount) FROM Sales);

4. Retrieve customers who placed orders within a specific date range:
SELECT customer_id, order_id
FROM Orders
WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31'; -- Replace with your date range

5. Find departments with more than 5 employees:
SELECT department, COUNT(employee_id) AS total_employees
FROM Employee
GROUP BY department
HAVING COUNT(employee_id) > 5;

6. Calculate the average order value for each customer:
SELECT customer_id, AVG(total_amount) AS average_order_value
FROM Orders
GROUP BY customer_id;

7. List products that have been sold at least 5 times:
SELECT product_id, COUNT(order_id) AS times_sold
FROM Sales
GROUP BY product_id
HAVING COUNT(order_id) >= 5;

8. Get the total number of orders placed by each customer per year:
SELECT customer_id, YEAR(order_date) AS year, COUNT(order_id) AS total_orders
FROM Orders
GROUP BY customer_id, YEAR(order_date);

9. Retrieve employees who have worked for more than 5 years:
SELECT employee_id, employee_name
FROM Employee
WHERE DATEDIFF(YEAR, hire_date, GETDATE()) > 5;

10. Find the department with the highest total salary:
SELECT department, SUM(salary) AS total_salary
FROM Employee
GROUP BY department
ORDER BY total_salary DESC
LIMIT 1;

Here you can find essential SQL Interview Resources👇
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SQL queries that are commonly asked during interviews: 2.O



1. Retrieve the second-highest salary of employees:
SELECT MAX(salary) AS second_highest_salary
FROM Employee
WHERE salary < (SELECT MAX(salary) FROM Employee);

2. Get the total number of employees in each department:
SELECT department, COUNT(employee_id) AS total_employees
FROM Employee
GROUP BY department;

3. List customers who have not placed any orders:
SELECT c.customer_id, c.customer_name
FROM Customers c
LEFT JOIN Orders o ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;

4. Find the product with the highest revenue:
SELECT product_id, SUM(quantity * price) AS total_revenue
FROM Sales
GROUP BY product_id
ORDER BY total_revenue DESC
LIMIT 1;


5. Retrieve employees who earn more than the average salary:
SELECT *
FROM Employee
WHERE salary > (SELECT AVG(salary) FROM Employee);

6. Get the list of products sold in the last 7 days:
SELECT DISTINCT product_id
FROM Sales
WHERE order_date >= DATEADD(DAY, -7, GETDATE());

7. Find the number of orders placed each day:
SELECT order_date, COUNT(order_id) AS total_orders
FROM Orders
GROUP BY order_date;

8. Retrieve employees with the same salary:
SELECT employee_id, salary
FROM Employee
GROUP BY salary
HAVING COUNT(employee_id) > 1;

9. List the customers who have placed the most orders:
SELECT customer_id, COUNT(order_id) AS total_orders
FROM Orders
GROUP BY customer_id
ORDER BY total_orders DESC
LIMIT 1;

10. Get the total quantity of each product sold per month:
SELECT product_id, MONTH(order_date) AS month, SUM(quantity) AS total_quantity
FROM Sales
GROUP BY product_id, MONTH(order_date);

Here you can find essential SQL Interview Resources👇
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SQL topics that are important for a data analyst role:

✔ Basic SQL Queries
SELECT Statements: Retrieve data from databases.
WHERE Clause: Filter records based on specified conditions.
ORDER BY: Sort results.
LIMIT: Limit the number of returned rows.

✔ Data Aggregation
GROUP BY: Group rows that have the same values in specified columns.
HAVING Clause: Filter groups based on a specified condition.
Aggregate Functions: COUNT(), SUM(), AVG(), MIN(), MAX().

✔ Joins
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
SELF JOIN
CROSS JOIN

✔Advanced SQL Concepts
Subqueries (Nested Queries): Query within another query.
Common Table Expressions (CTEs): Temporary result set that can be referenced within another SELECT, INSERT, UPDATE, or DELETE statement.

✔ Window Functions: Perform calculations across a set of table rows related to the current row (e.g., ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()).

✔ UNION and UNION ALL: Combine the results of two or more SELECT statements.

✔Data Manipulation
INSERT INTO: Add new rows to a table.
UPDATE: Modify existing records.
DELETE: Remove existing records.

✔Data Definition
CREATE TABLE: Define a new table.
ALTER TABLE: Modify an existing table.
DROP TABLE: Delete a table.
Primary and Foreign Keys: Enforce data integrity and relationships between tables.
Indexes: Improve the speed of data retrieval.

✔Performance Tuning
Query Optimization: Techniques to improve query performance (e.g., indexing, avoiding unnecessary columns in SELECT, avoiding SELECT *).
Execution Plans: Analyze how SQL statements are executed to optimize performance.

✔SQL Functions
String Functions: CONCAT(), SUBSTRING(), REPLACE(), LENGTH().
Date and Time Functions: NOW(), CURDATE(), DATEADD(), DATEDIFF().
Numeric Functions: ROUND(), CEIL(), FLOOR().

✔Error Handling
TRY...CATCH: Handle errors in SQL code (available in some SQL dialects).
Transaction Control: BEGIN TRANSACTION, COMMIT, and ROLLBACK to ensure data integrity.

✔Data Analysis Specific
Pivoting and Unpivoting: Convert rows to columns and vice versa.
Creating Reports: Using SQL to generate detailed data reports.
Data Cleaning and Transformation: Techniques to prepare data for analysis.

✔ Database Management
User Permissions and Roles: Manage access control.
Backup and Restore: Ensure data safety and recovery.

✔ Practical Use Cases
Real-world scenarios: Understanding and solving business problems using SQL.
Case Studies: Applying SQL knowledge to actual data sets and business requirements.

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Essential SQL interview questions covering various topics:

🔺Basic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.

🔺Querying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.


🔺Joins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.

🔺Aggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.

🔺Grouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.

🔺Subqueries:
-Define a subquery and provide an example.

🔺Indexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.

🔺Normalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.

🔺Transactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.

🔺Views and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.

🔺Advanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.

✅👀These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.

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SQL Essential Concepts

𝟭. 𝗜𝗻𝘁𝗿𝗼 𝘁𝗼 𝗦𝗤𝗟: Definition, purpose, relational DBs, DBMS.

𝟮. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅: SELECT, FROM, WHERE, ORDER BY, GROUP BY.

𝟯. 𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲𝘀: Integer, floating-point, character, date, VARCHAR, TEXT, BLOB, BOOLEAN.

𝟰. 𝗦𝘂𝗯 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: DML, DDL, DQL, DCL, TCL.

𝟱. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻: INSERT, UPDATE, DELETE.

𝟲. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: CREATE, ALTER, DROP, Indexes.

𝟳. 𝗤𝘂𝗲𝗿𝘆 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗼𝗿𝘁𝗶𝗻𝗴: WHERE, AND, OR conditions, ascending, descending.

𝟴. 𝗗𝗮𝘁𝗮 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻: SUM, AVG, COUNT, MIN, MAX.

𝟵. 𝗝𝗼𝗶𝗻𝘀 𝗮𝗻𝗱 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀: INNER JOIN, LEFT JOIN, RIGHT JOIN, Self-Joins, Cross Joins, FULL OUTER JOIN.

𝟭𝟬. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Filtering data, aggregating data, joining tables, correlated subqueries.

𝟭𝟭. 𝗩𝗶𝗲𝘄𝘀: Creating, modifying, dropping views.

𝟭𝟮. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀: ACID properties, COMMIT, ROLLBACK, SAVEPOINT, ROLLBACK TO SAVEPOINT.

𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀: CREATE, ALTER, DROP, EXECUTE, User-Defined Functions (UDFs).

𝟭𝟰. 𝗧𝗿𝗶𝗴𝗴𝗲𝗿𝘀: Trigger events, trigger execution, and syntax.

𝟭𝟱. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀: CREATE USER, GRANT, REVOKE, ALTER USER, DROP USER.

𝟭𝟲. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀: Indexing strategies, query optimization.

𝟭𝟳. 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 1NF, 2NF, 3NF, BCNF.

𝟭𝟴. 𝗡𝗼𝗦𝗤𝗟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: MongoDB, Cassandra, and key differences.

𝟭𝟵. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆: Primary key, foreign key.

𝟮𝟬. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝗶𝗲𝘀: Window functions, Common Table Expressions (CTES).

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#sql

Top 5 SQL Functions

https://t.me/sqlanalyst

1. SELECT Statement:
- Function: Retrieving data from one or more tables.
- Example: SELECT column1, column2 FROM table WHERE condition;

2. COUNT Function:
- Function: Counts the number of rows that meet a specified condition.
- Example: SELECT COUNT(column) FROM table WHERE condition;

3. SUM Function:
- Function: Calculates the sum of values in a numeric column.
- Example: SELECT SUM(column) FROM table WHERE condition;

4. AVG Function:
- Function: Computes the average value of a numeric column.
- Example: SELECT AVG(column) FROM table WHERE condition;

5. GROUP BY Clause:
- Function: Groups rows that have the same values in specified columns into summary rows.
- Example: SELECT column, AVG(numeric_column) FROM table GROUP BY column;

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