Simple explanation of APPEND & MERGE two tables

Let’s think of SQL as a way to talk to a big toy box full of different toys.

### Imagine This:

1. Toy Box = Database: This is where all your toys (data) are stored.

2. Toys = Tables: Each type of toy is in its own section, like cars in one area and dolls in another.

3. Picking Toys = Queries: When you want to play with certain toys, you ask the toy box (database) to give them to you.

### Basic Commands:

1. SELECT: This is like saying, "Show me my cars!"
- Example: SELECT * FROM Cars; means "Show me all the cars!"

2. WHERE: This is like saying, "Show me only the red cars!"
- Example: SELECT * FROM Cars WHERE color = 'red';

3. INSERT: This is like adding a new toy to the toy box.
- Example: INSERT INTO Cars (color) VALUES ('blue'); means "Add a blue car!"

4. UPDATE: This is like changing a toy's color.
- Example: UPDATE Cars SET color = 'green' WHERE color = 'red'; means "Change all red cars to green."

5. DELETE: This is like taking a toy out of the toy box and saying goodbye.
- Example: DELETE FROM Cars WHERE color = 'blue'; means "Remove the blue car."

### Summary:

- SELECT = Look at your toys.
- WHERE = Choose specific toys.
- INSERT = Add new toys.
- UPDATE = Change your toys.
- DELETE = Remove toys.

That’s it! SQL helps you play with and organize your toys (data) in the best way!

Here are examples of how SQL is used in a product company:

### 1. Product Inventory Management

Scenario: Managing product details and stock levels.

- SELECT: View all products in inventory.
- Query: SELECT * FROM Products;

- WHERE: Find products that are out of stock.
- Query: SELECT * FROM Products WHERE stock_quantity = 0;

- INSERT: Add a new product to the inventory.
- Query: INSERT INTO Products (name, price, category, stock_quantity) VALUES ('Wireless Headphones', 150.00, 'Electronics', 100);

- UPDATE: Update the stock quantity after a sale.
- Query: UPDATE Products SET stock_quantity = stock_quantity - 1 WHERE product_id = 101;

- DELETE: Remove a discontinued product.
- Query: DELETE FROM Products WHERE product_id = 101;

### 2. Order Processing

Scenario: Managing customer orders.

- SELECT: View all customer orders.
- Query: SELECT * FROM Orders;

- WHERE: Find orders for a specific customer.
- Query: SELECT * FROM Orders WHERE customer_id = 123;

- INSERT: Add a new order.
- Query: INSERT INTO Orders (customer_id, order_date, total_amount) VALUES (123, '2024-07-14', 299.99);

- UPDATE: Update the status of an order.
- Query: UPDATE Orders SET status = 'Shipped' WHERE order_id = 456;

- DELETE: Cancel an order.
- Query: DELETE FROM Orders WHERE order_id = 456 AND status = 'Pending';

### 3. Customer Management

Scenario: Keeping track of customer information.

- SELECT: View all customer details.
- Query: SELECT * FROM Customers;

- WHERE: Find customers from a specific city.
- Query: SELECT * FROM Customers WHERE city = 'New York';

- INSERT: Add a new customer.
- Query: INSERT INTO Customers (name, email, phone, city) VALUES ('Jane Doe', 'jane@example.com', '555-1234', 'Los Angeles');

- UPDATE: Update a customer's contact information.
- Query: UPDATE Customers SET phone = '555-5678' WHERE customer_id = 123;

- DELETE: Remove a customer record.
- Query: DELETE FROM Customers WHERE customer_id = 123;

### 4. Sales Analytics

Scenario: Analyzing sales data for insights.

- SELECT: View total sales for a specific period.
- Query: SELECT SUM(total_amount) FROM Orders WHERE order_date BETWEEN '2024-01-01' AND '2024-06-30';

- WHERE: Find top-selling products.
- Query: SELECT product_id, SUM(quantity) FROM OrderDetails GROUP BY product_id ORDER BY SUM(quantity) DESC LIMIT 5;

- INSERT: Log a new sales report.
- Query: INSERT INTO SalesReports (report_date, total_sales) VALUES ('2024-07-01', 15000);

- UPDATE: Adjust sales forecasts based on new data.
- Query: UPDATE SalesForecast SET forecast_amount = 20000 WHERE forecast_month = '2024-07';

- DELETE: Remove outdated sales reports.
- Query: DELETE FROM SalesReports WHERE report_date < '2023-01-01';

In a product company, SQL is crucial for managing inventory, processing orders, handling customer data, and analyzing sales. These commands help streamline operations and provide valuable insights for decision-making.

𝗖𝗧𝗘𝘀 (𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀)

CTEs can make complex queries more readable and easier to maintain.

They are excellent for breaking down complex queries into simpler, more manageable parts.

CTEs are most efficient for organizing query logic but don't inherently improve performance.

Since CTEs do not store their results. They act as temporary views executed every time they are referenced.


𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗿𝘆 𝗧𝗮𝗯𝗹𝗲𝘀

But we also have Temporary tables, and they come with Performance through Persistence.

Temporary tables store data in the database's temporary storage (e.g., the tempdb in SQL Server).

On Temporary tables, you can also define indexes!!!

This physical storage and the indexes will make the reads or joins X times faster.


𝗧𝗟;𝗗𝗥:

CTEs are excellent for organizing and simplifying complex SQL queries without storing intermediate results, making them easy to read and maintain.

Temporary Tables are better suited for performance optimization of large datasets, indexing, and reducing the need for repeated computations.

CTEs are used because of their readability, but the performance on Temporary Tables is hard to ignore for large datasets.

To effectively learn SQL for a Data Analyst role, follow these steps:

1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.

2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.

3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."

4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.

5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.

By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.

SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.

Let’s dive into a comprehensive guide on SQL for Data Science!

I have broken it down into three key sections to help you:

𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.

𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.

𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.

How to Find Duplicates in a Table ?

Quick Answer: Use the GROUP BY and HAVING clauses to identify rows that appear more than once.

Example: Imagine you have an employees table with columns first_name, last_name, and email. To find duplicate entries based on first_name and last_name

SELECT first_name, last_name, COUNT(*)
FROM employees
GROUP BY first_name, last_name
HAVING COUNT(*) > 1;

This query groups the rows by first_name and last_name, then filters to show only those groups with more than one occurrence, identifying the duplicates.

💥📚These SQL interview questions typically asked in a Data Analyst interview?

1.What distinguishes a Primary key from a Unique key?

Primary key uniquely identifies each record in a table and cannot contain null values, whereas a Unique key also uniquely identifies records but can contain null values and multiple unique keys can exist in a table.

2. Define Candidate key.

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

3.Explain the concept of Constraint in SQL.

A Constraint is a specific rule or limit defined in a table to enforce data integrity. Examples include NOT NULL and AUTO INCREMENT.

4. Differentiate between TRUNCATE and DELETE commands.

TRUNCATE is a DDL command that removes all data from a table while preserving the table's structure, and it is faster than DELETE. DELETE is a DML command that removes specific rows based on conditions and operates slower than TRUNCATE as it deletes data row by row.

5.Compare and contrast a 'View' and a 'Stored Procedure'.

A View is a virtual table derived from one or more base tables, often used to simplify complex queries, while a Stored Procedure is a precompiled collection of SQL statements stored on the database server, used to perform specific tasks or operations.

6.What sets apart a Common Table Expression from a temporary table?

A Common Table Expression (CTE) is a temporary result set defined within the execution scope of a single SELECT, DELETE, or UPDATE statement, while a temporary table is stored in TempDB and persists until the session ends.

7.Contrast a clustered index with a non-clustered index.

A clustered index determines the physical ordering of data in a table and there can be only one clustered index per table. In contrast, a non-clustered index is similar to an index in a book where data is stored separately from the index, and multiple non-clustered indexes can exist for a table.

8.Define triggers in SQL and their purpose.

Triggers are SQL codes that automatically execute in response to certain events on a table, such as INSERT, UPDATE, or DELETE operations. They are used to maintain data integrity and perform actions based on specific conditions.

➡️ The SQL Circle

𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝘁 𝗠𝗔𝗔𝗡𝗚 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀🔥🔥

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 (or LEFT OUTER JOIN): Returns all records from the left table and 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 (or RIGHT OUTER JOIN): Returns all records from the right table and 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 (or FULL OUTER 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 and 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 count the number of records in a table?

SELECT COUNT(*) FROM table_name;

This query counts all the records in the specified table.

6. How do you calculate average, sum, minimum, and 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;


7. What is a subquery, and how do you use it?

Subquery: A query nested inside another query

SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);




Till then keep learning and keep exploring 🙌

After learning SQL 😂👇
https://www.instagram.com/reel/C-RnCrTSrqn/?igsh=MWk4cnVqa2VxZXZpaw==

Complete 14-day roadmap to learn SQL learning:

Day 1: Introduction to Databases
- Understand the concept of databases and their importance.
- Learn about relational databases and SQL.
- Explore the basic structure of SQL queries.

Day 2: Basic SQL Syntax
- Learn SQL syntax: statements, clauses, and keywords.
- Understand the SELECT statement for retrieving data.
- Practice writing basic SELECT queries with conditions and filters.

Day 3: Retrieving Data from Multiple Tables
- Learn about joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Understand how to retrieve data from multiple tables using joins.
- Practice writing queries involving multiple tables.

Day 4: Aggregate Functions
- Learn about aggregate functions: COUNT, SUM, AVG, MIN, MAX.
- Understand how to use aggregate functions to perform calculations on data.
- Practice writing queries with aggregate functions.

Day 5: Subqueries
- Learn about subqueries and their role in SQL.
- Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
- Practice writing queries with subqueries.

Day 6: Data Manipulation Language (DML)
- Learn about DML commands: INSERT, UPDATE, DELETE.
- Understand how to add, modify, and delete data in a database.
- Practice writing DML statements.

Day 7: Data Definition Language (DDL)
- Learn about DDL commands: CREATE TABLE, ALTER TABLE, DROP TABLE.
- Understand constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL.
- Practice designing database schemas and creating tables.

Day 8: Data Control Language (DCL)
- Learn about DCL commands: GRANT, REVOKE for managing user permissions.
- Understand how to control access to database objects.
- Practice granting and revoking permissions.

Day 9: Transactions
- Understand the concept of transactions in SQL.
- Learn about transaction control commands: COMMIT, ROLLBACK.
- Practice managing transactions.

Day 10: Views
- Learn about views and their benefits.
- Understand how to create, modify, and drop views.
- Practice creating views.

Day 11: Indexes
- Learn about indexes and their role in database optimization.
- Understand different types of indexes (e.g., B-tree, hash).
- Practice creating and managing indexes.

Day 12: Optimization Techniques
- Explore optimization techniques such as query tuning and normalization.
- Understand the importance of database design for optimization.
- Practice optimizing SQL queries.

Day 13: Review and Practice
- Review all concepts covered in the previous days.
- Work on sample projects or exercises to reinforce learning.
- Take practice quizzes or tests.

Day 14: Final Review and Projects
- Review all concepts learned throughout the 14 days.
- Work on a final project to apply SQL knowledge.
- Seek out additional resources or tutorials if needed.


Here are some practical SQL syntax examples for each day of your learning journey:

Day 1: Introduction to Databases
- Syntax to select all columns from a table:

   SELECT * FROM table_name;
  

Day 2: Basic SQL Syntax
- Syntax to select specific columns from a table:

   SELECT column1, column2 FROM table_name;
  

Day 3: Retrieving Data from Multiple Tables
- Syntax for INNER JOIN to retrieve data from two tables:

   SELECT orders.order_id, customers.customer_name
  FROM orders
  INNER JOIN customers ON orders.customer_id = customers.customer_id;
  

Day 4: Aggregate Functions
- Syntax for COUNT to count the number of rows in a table:

   SELECT COUNT(*) FROM table_name;
  

Day 5: Subqueries
- Syntax for using a subquery in the WHERE clause:

   SELECT column1, column2 
  FROM table_name 
  WHERE column1 IN (SELECT column1 FROM another_table WHERE condition);
  

Day 6: Data Manipulation Language (DML)
- Syntax for INSERT to add data into a table:

   INSERT INTO table_name (column1, column2) VALUES (value1, value2);