SQL INTERVIEW PREPARATION PART-36

Explain the differences between DELETE, TRUNCATE, and DROP commands in SQL.

Answer:

These three SQL commands are used to remove data from a database, but they operate in different ways and serve different purposes.

DELETE:
- Purpose: Removes specific rows from a table based on a condition.
- Usage: Can delete all rows or a subset of rows from a table.
- Syntax:

  DELETE FROM table_name WHERE condition;
  

- Example:

  DELETE FROM employees WHERE department_id = 10;
  

- Characteristics:
- Can use WHERE clause to filter which rows to delete.
- Generates row-level locks.
- Deletes one row at a time, which can be slower for large tables.
- Can be rolled back if used within a transaction.
- Triggers, if defined, will be fired.

TRUNCATE:
- Purpose: Removes all rows from a table, resetting it to its empty state.
- Usage: Used when you need to quickly remove all data from a table.
- Syntax:

  TRUNCATE TABLE table_name;
  

- Example:

  TRUNCATE TABLE employees;
  

- Characteristics:
- Cannot use WHERE clause.
- Faster than DELETE as it deallocates the data pages instead of row-by-row deletion.
- Resets any AUTO_INCREMENT counters.
- Cannot be rolled back in some database systems as it is a DDL operation.
- Does not fire triggers.

DROP:
- Purpose: Removes an entire table or database from the database.
- Usage: Used when you need to completely remove a table or database structure.
- Syntax:

  DROP TABLE table_name;
  DROP DATABASE database_name;
  

- Example:

  DROP TABLE employees;
  

- Characteristics:
- Permanently deletes the table or database and all its data.
- Cannot be rolled back; once dropped, the table or database is gone.
- All indexes and triggers associated with the table are also deleted.
- Removes table definition and data.

Tip: Use DELETE when you need to remove specific rows and want the option to roll back the transaction. Use TRUNCATE when you need to quickly clear all data from a table without deleting the table structure itself. Use DROP when you need to completely remove a table or database structure and all associated data permanently. Always ensure you have backups and understand the impact of these operations before executing them.

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POWER BI INTERVIEW PREPARATION PART-13

What is row-level security (RLS) in Power BI?

Answer:
- Row-level security (RLS) is a feature in Power BI that restricts data access for certain users based on their role.
- It ensures that users only see data relevant to them, enhancing data security and privacy.

Example:

By creating roles in Power BI Desktop, you can define filters that limit data exposure. For instance, a sales manager might only view data for their specific region.

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SQL INTERVIEW PREPARATION PART-37

What is normalization in SQL, and what are the different normal forms? Explain each normal form with an example.

Answer:

Normalization is the process of organizing the columns and tables of a relational database to minimize data redundancy and improve data integrity. It involves decomposing a large table into smaller tables and defining relationships between them. The goal is to ensure that each piece of data is stored only once.

Normal Forms:

1. First Normal Form (1NF):
- Definition: Ensures that the table has a primary key and that all column values are atomic (indivisible).
- Example:

     CREATE TABLE students (
         student_id INT PRIMARY KEY,
         student_name VARCHAR(100),
         phone_number VARCHAR(15)
     );
     

Here, each cell contains only one value, and each record is unique.

2. Second Normal Form (2NF):
- Definition: Achieves 1NF and ensures that all non-key attributes are fully functionally dependent on the primary key.
- Example:

     CREATE TABLE student_courses (
         student_id INT,
         course_id INT,
         PRIMARY KEY (student_id, course_id)
     );
     CREATE TABLE students (
         student_id INT PRIMARY KEY,
         student_name VARCHAR(100)
     );
     CREATE TABLE courses (
         course_id INT PRIMARY KEY,
         course_name VARCHAR(100)
     );
     

Here, each non-key attribute is dependent on the whole primary key.

3. Third Normal Form (3NF):
- Definition: Achieves 2NF and ensures that all non-key attributes are not only fully functionally dependent on the primary key but also non-transitively dependent (i.e., no transitive dependency).
- Example:

     CREATE TABLE student_courses (
         student_id INT,
         course_id INT,
         PRIMARY KEY (student_id, course_id)
     );
     CREATE TABLE students (
         student_id INT PRIMARY KEY,
         student_name VARCHAR(100),
         department_id INT
     );
     CREATE TABLE departments (
         department_id INT PRIMARY KEY,
         department_name VARCHAR(100)
     );
     

Here, student_name depends only on student_id, and department_name depends only on department_id.

4. Boyce-Codd Normal Form (BCNF):
- Definition: A stricter version of 3NF where every determinant is a candidate key.
- Example:

     CREATE TABLE student_courses (
         student_id INT,
         course_id INT,
         course_instructor VARCHAR(100),
         PRIMARY KEY (student_id, course_id)
     );
     CREATE TABLE courses (
         course_id INT PRIMARY KEY,
         course_name VARCHAR(100),
         course_instructor VARCHAR(100)
     );
     

Here, the table is decomposed to ensure no non-trivial functional dependency other than a super key.

5. Fourth Normal Form (4NF):
- Definition: Achieves BCNF and ensures that multi-valued dependencies are removed.
- Example:

     CREATE TABLE student_languages (
         student_id INT,
         language VARCHAR(50),
         PRIMARY KEY (student_id, language)
     );
     CREATE TABLE student_courses (
         student_id INT,
         course_id INT,
         PRIMARY KEY (student_id, course_id)
     );
     

Here, the two independent multi-valued facts (languages known by a student and courses taken by a student) are stored in separate tables.

6. Fifth Normal Form (5NF):
- Definition: Ensures that every join dependency is implied by the candidate keys.
- Example:
Rarely used in practical scenarios, but the concept is to decompose tables to avoid redundancy and ensure data integrity further.

Tip: Normalization is crucial for efficient database design and maintenance. However, over-normalization can lead to complex queries and performance issues. It's important to balance normalization with practical performance considerations.

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SQL Learning plan in 2024

|-- 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
|   |   |-- Research Papers
|   |   |-- New Developments in SQL
|
|-- Resources and Community
|   |-- Online Courses (Coursera, Udacity)
|   |-- Books (SQL for Data Analysis, Learning SQL)
|   |-- SQL Blogs and Resources
|   |-- GitHub Repositories

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POWER BI INTERVIEW PREPARATION PART-14

What is the difference between Import and DirectQuery modes in Power BI?

Answer:
- Import Mode:
- Data is imported into Power BI and stored in the data model.
- Allows for faster performance and complex data transformations.
- Data can be refreshed on a schedule.

- DirectQuery Mode:
- Data stays in the source system and is queried in real-time.
- Enables access to large datasets without importing them.
- May have performance limitations due to reliance on the source system.

Example:

Using Import mode for a small dataset allows for quicker analysis, while DirectQuery is suitable for dynamic data needs, like live sales data from a transactional database.

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SQL INTERVIEW PREPARATION PART-38

What are stored procedures in SQL, and what are their advantages? Provide an example to illustrate their usage.

Answer:

Stored procedures are precompiled collections of SQL statements and optional control-of-flow statements, stored under a name and processed as a unit. They can accept input parameters, return output parameters, and can be executed to perform repetitive or complex database operations.

Advantages of Stored Procedures:
1. Performance: Stored procedures are precompiled and stored in the database, which can result in faster execution compared to dynamic SQL queries.
2. Reusability: Once created, stored procedures can be reused multiple times across different applications or parts of an application.
3. Security: Stored procedures can help enforce security by controlling access to data and limiting direct access to tables.
4. Maintainability: Stored procedures provide a centralized location for logic, making it easier to manage and update complex operations.
5. Reduced Network Traffic: Executing a stored procedure requires less communication between the application and the database server compared to sending multiple individual SQL statements.

Example:

Suppose you want to create a stored procedure to insert a new employee record into the employees table.

1. Create the Stored Procedure:

   CREATE PROCEDURE AddEmployee
       @FirstName VARCHAR(50),
       @LastName VARCHAR(50),
       @DepartmentId INT,
       @Salary DECIMAL(10, 2)
   AS
   BEGIN
       INSERT INTO employees (first_name, last_name, department_id, salary)
       VALUES (@FirstName, @LastName, @DepartmentId, @Salary);
   END;
   

2. Execute the Stored Procedure:

   EXEC AddEmployee 'John', 'Doe', 10, 55000.00;
   

Explanation:
- The stored procedure AddEmployee accepts four parameters: @FirstName, @LastName, @DepartmentId, and @Salary.
- Inside the procedure, an INSERT statement is executed to add a new record to the employees table using the provided parameters.
- The procedure is executed with the EXEC command, passing the required values for the parameters.

Tip: Stored procedures are powerful tools for encapsulating business logic and database operations. Use them to simplify and secure your database interactions, especially when dealing with repetitive tasks or complex logic. Always consider parameterizing your stored procedures to prevent SQL injection attacks.

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POWER BI INTERVIEW PREPARATION PART-15

What are bookmarks in Power BI?

Answer:
- Bookmarks capture the current state of a report page, including filters and slicers, allowing users to return to that view easily.
- They are useful for storytelling and presenting insights by highlighting specific data points or views.

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POWER BI INTERVIEW PREPARATION PART-16

What are the different types of visualizations available in Power BI?

Answer:
- Power BI offers a variety of visualizations, including:
- Bar and Column Charts: Used for comparing quantities.
- Line and Area Charts: Ideal for showing trends over time.
- Pie and Donut Charts: Useful for displaying parts of a whole.
- Tables and Matrices: For detailed data presentation.
- Maps: For geographical data visualization.
- Cards: To display single values or metrics.

Example:

Using a bar chart to visualize sales by region allows users to quickly identify which areas are performing best.

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SQL INTERVIEW PREPARATION PART-39

What is the difference between UNION and UNION ALL in SQL? Provide examples.

Answer:

UNION:
The UNION operator combines the result sets of two or more SELECT statements into a single result set and removes duplicate rows. Each SELECT statement within the UNION must have the same number of columns in the result sets with similar data types.

Example:
Suppose we have two tables, employees_2022 and employees_2023:

employees_2022:
| employee_id | name |
|-------------|-------|
| 1 | John |
| 2 | Jane |

employees_2023:
| employee_id | name |
|-------------|-------|
| 2 | Jane |
| 3 | Jim |

Using UNION:

SELECT name FROM employees_2022
UNION
SELECT name FROM employees_2023;

Result:
| name |
|-------|
| John |
| Jane |
| Jim |

UNION ALL:
The UNION ALL operator also combines the result sets of two or more SELECT statements but does not remove duplicates. It includes all rows, regardless of whether they are duplicates.

Using UNION ALL:

SELECT name FROM employees_2022
UNION ALL
SELECT name FROM employees_2023;

Result:
| name |
|-------|
| John |
| Jane |
| Jane |
| Jim |

Key Differences:
- Duplicates: UNION removes duplicates, while UNION ALL includes all rows.
- Performance: UNION ALL is generally faster because it does not require the additional step of removing duplicates.

Tip: Use UNION when you need distinct results and UNION ALL when you want to retain all data, especially in scenarios where performance is critical and duplicates are acceptable.

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SQL INTERVIEW PREPARATION PART-41

What is the difference between a LEFT JOIN and an INNER JOIN in SQL? Provide examples to illustrate the differences.

Answer:

INNER JOIN:
An INNER JOIN returns only the rows that have matching values in both tables. If there are rows in either table that do not have matches, they will not be included in the result set.

Example:
Suppose we have two tables, employees and departments:

employees table:
| employee_id | name | department_id |
|-------------|-------|---------------|
| 1 | John | 10 |
| 2 | Jane | 20 |
| 3 | Jim | 30 |

departments table:
| department_id | department_name |
|---------------|-----------------|
| 10 | HR |
| 20 | Finance |
| 40 | IT |

An INNER JOIN query to get the employees and their corresponding department names would be:

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

Result:
| name | department_name |
|-------|-----------------|
| John | HR |
| Jane | Finance |

LEFT JOIN:
A LEFT JOIN returns all the rows from the left table and the matched rows from the right table. If there is no match, the result is NULL on the side of the right table.

Example:
Using the same tables, a LEFT JOIN query to get all employees and their corresponding department names would be:

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

Result:
| name | department_name |
|-------|-----------------|
| John | HR |
| Jane | Finance |
| Jim | NULL |

In this result, Jim is included even though there is no corresponding department in the departments table, showing a NULL for department_name.

Tip: Use an INNER JOIN when you want to retrieve only the records that have matching values in both tables. Use a LEFT JOIN when you want to retrieve all records from the left table and the matching records from the right table, filling in NULLs for non-matching rows. Understanding these joins is crucial for effectively querying relational databases and retrieving the desired data.

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Excel Learning Plan in 2024

|-- Week 1: Introduction to Excel
| |-- Excel Basics
| | |-- What is Excel?
| | |-- Excel Interface Overview
| | |-- Basic Operations (Open, Save, Close)
| |-- Setting up Excel
| | |-- Workbook and Worksheet Management
| | |-- Entering and Editing Data
| | |-- Basic Formatting
| |-- First Excel Project
| | |-- Creating a Simple Spreadsheet
| | |-- Basic Formulas (SUM, AVERAGE)
|
|-- Week 2: Intermediate Excel
| |-- Advanced Formulas and Functions
| | |-- Logical Functions (IF, AND, OR)
| | |-- Text Functions (CONCATENATE, LEFT, RIGHT)
| | |-- Date and Time Functions
| |-- Data Management
| | |-- Sorting and Filtering Data
| | |-- Data Validation
| |-- Basic Charts and Graphs
| | |-- Creating Charts
| | |-- Customizing Charts
| | |-- Sparklines
|
|-- Week 3: Advanced Excel Techniques
| |-- Advanced Data Analysis
| | |-- Pivot Tables
| | |-- Pivot Charts
| | |-- Slicers and Timelines
| |-- Lookup and Reference Functions
| | |-- VLOOKUP, HLOOKUP
| | |-- INDEX and MATCH
| | |-- INDIRECT and ADDRESS
| |-- Advanced Formatting
| | |-- Conditional Formatting
| | |-- Custom Number Formats
| | |-- Themes and Styles
|
|-- Week 4: Excel for Data Analysis
| |-- Data Cleaning
| | |-- Removing Duplicates
| | |-- Text to Columns
| | |-- Data Cleaning Functions (TRIM, CLEAN)
| |-- Data Visualization
| | |-- Advanced Chart Types (Waterfall, Funnel)
| | |-- Creating Dashboards
| |-- Power Query
| | |-- Importing Data
| | |-- Transforming Data
| | |-- Merging and Appending Queries
|
|-- Week 5: Excel for Business and Finance
| |-- Financial Functions
| | |-- PMT, PV, FV
| | |-- NPV, IRR
| |-- Business Modeling
| | |-- Scenario Analysis
| | |-- Goal Seek
| | |-- Data Tables
| |-- Reporting and Presentations
| | |-- Creating Professional Reports
| | |-- Using Templates
| | |-- Printing and Sharing Workbooks
|
|-- Week 6-8: Advanced Excel Tools
| |-- Macros and VBA
| | |-- Recording Macros
| | |-- Editing Macros in VBA
| | |-- Automating Tasks
| |-- Power Pivot
| | |-- Introduction to Power Pivot
| | |-- Creating Data Models
| | |-- Using DAX in Power Pivot
| |-- Excel Add-ins
| | |-- Installing Add-ins
| | |-- Popular Add-ins (Solver, Analysis ToolPak)
| |-- Collaboration and Sharing
| | |-- Co-authoring
| | |-- Excel Online
| | |-- Sharing and Permissions
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Excel and Power BI
| | |-- Excel and SQL
| | |-- Excel and R/Python
|
|-- Week 12: Post-Project Learning
| |-- Excel Administration
| | |-- Workbook and Worksheet Protection
| | |-- Data Encryption
| |-- Advanced Excel Topics
| | |-- New Excel Features
| | |-- Excel for Mac
| |-- Continuing Education
| | |-- Advanced Excel Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Excel Bible, Excel for Dummies)
| |-- Excel Blogs and Podcasts
| |-- GitHub Repositories
| |-- Excel Communities (Microsoft Tech Community, Reddit)

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SQL INTERVIEW PREPARATION PART-40

What are the differences between DELETE, TRUNCATE, and DROP commands in SQL? Provide examples.

Answer:

DELETE:
The DELETE command is used to remove rows from a table based on a specified condition. It can delete all rows or specific rows that match the condition. DELETE operations can be rolled back if they are part of a transaction.

Example:

DELETE FROM employees WHERE department_id = 10;

Key Points:
- Deletes specified rows.
- Can use a WHERE clause to filter rows.
- Triggers are fired.
- Slower compared to TRUNCATE for large data sets due to row-by-row deletion.
- Transactional and can be rolled back.

TRUNCATE:
The TRUNCATE command is used to remove all rows from a table. It is faster than DELETE because it does not log individual row deletions. TRUNCATE operations cannot be rolled back if they are not part of a transaction.

Example:

TRUNCATE TABLE employees;

Key Points:
- Deletes all rows from a table.
- Cannot use a WHERE clause.
- Resets table's identity column (if any).
- Does not fire triggers.
- Faster than DELETE due to minimal logging.
- Transactional and can be rolled back only if part of a transaction.

DROP:
The DROP command is used to remove a table or database entirely from the database. This operation deletes the table schema and all its data, and it cannot be rolled back.

Example:

DROP TABLE employees;

Key Points:
- Deletes the entire table schema and data.
- Irreversible and cannot be rolled back.
- Removes all associated objects like constraints, triggers, and indexes.
- Not transactional.

Tip: Use DELETE when you need to remove specific rows and want the operation to be logged and potentially rolled back. Use TRUNCATE for quickly removing all rows in a table while retaining the table structure. Use DROP when you need to permanently remove a table and its schema from the database.

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SQL INTERVIEW PREPARATION PART-41

What is SQL and what are its main components?

Answer:

SQL (Structured Query Language):
SQL is a standard programming language specifically designed for managing and manipulating relational databases. It allows users to create, read, update, and delete (CRUD) data stored in a relational database.

Main Components of SQL:

1. DDL (Data Definition Language):
- Purpose: Defines and modifies the structure of database objects.
- Commands:
- CREATE: Creates a new table, view, or other database objects.

       CREATE TABLE employees (
           employee_id INT PRIMARY KEY,
           first_name VARCHAR(50),
           last_name VARCHAR(50),
           hire_date DATE
       );
       

- ALTER: Modifies the structure of an existing table.

       ALTER TABLE employees ADD COLUMN salary DECIMAL(10, 2);
       

- DROP: Deletes a table, view, or other database objects.

       DROP TABLE employees;
       

2. DML (Data Manipulation Language):
- Purpose: Manipulates the data stored in the database.
- Commands:
- SELECT: Retrieves data from one or more tables.

       SELECT first_name, last_name FROM employees;
       

- INSERT: Adds new rows of data into a table.

       INSERT INTO employees (employee_id, first_name, last_name, hire_date)
       VALUES (1, 'John', 'Doe', '2024-07-28');
       

- UPDATE: Modifies existing data within a table.

       UPDATE employees SET salary = 60000 WHERE employee_id = 1;
       

- DELETE: Removes rows of data from a table.

       DELETE FROM employees WHERE employee_id = 1;
       

3. DCL (Data Control Language):
- Purpose: Controls access to the data within the database.
- Commands:
- GRANT: Provides specific privileges to users.

       GRANT SELECT ON employees TO user_name;
       

- REVOKE: Removes specific privileges from users.

       REVOKE SELECT ON employees FROM user_name;
       

4. TCL (Transaction Control Language):
- Purpose: Manages transactions within a database to ensure data integrity.
- Commands:
- COMMIT: Saves the changes made by the current transaction.

       COMMIT;
       

- ROLLBACK: Undoes the changes made by the current transaction.

       ROLLBACK;
       

- SAVEPOINT: Sets a savepoint within a transaction to which a rollback can occur.

       SAVEPOINT savepoint_name;
       

Tip: Freshers should focus on mastering the syntax and use cases of these commands to effectively interact with relational databases.

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POWER BI INTERVIEW PREPARATION PART-17

What is the use of slicers in Power BI?

Answer:
- Slicers are visual filters that allow users to segment and filter data dynamically on a report page.
- They provide a user-friendly way to interact with data by selecting specific values, which then updates all connected visualizations.

SQL INTERVIEW PREPARATION PART-42

What is a JOIN in SQL and what are the different types of JOINs? Provide examples.

Answer:

JOIN:
A JOIN clause in SQL is used to combine rows from two or more tables based on a related column between them. JOINs allow querying data across multiple tables.

Types of JOINs:

1. INNER JOIN:
- Definition: Returns only the rows that have matching values in both tables.
- Example:

     SELECT employees.employee_id, employees.first_name, departments.department_name
     FROM employees
     INNER JOIN departments ON employees.department_id = departments.department_id;
     

2. LEFT JOIN (or LEFT OUTER JOIN):
- Definition: Returns all rows from the left table and the matched rows from the right table. If no match is found, NULL values are returned for columns from the right table.
- Example:

     SELECT employees.employee_id, employees.first_name, departments.department_name
     FROM employees
     LEFT JOIN departments ON employees.department_id = departments.department_id;
     

3. RIGHT JOIN (or RIGHT OUTER JOIN):
- Definition: Returns all rows from the right table and the matched rows from the left table. If no match is found, NULL values are returned for columns from the left table.
- Example:

     SELECT employees.employee_id, employees.first_name, departments.department_name
     FROM employees
     RIGHT JOIN departments ON employees.department_id = departments.department_id;
     

4. FULL JOIN (or FULL OUTER JOIN):
- Definition: Returns all rows when there is a match in either left or right table. If there is no match, the result is NULL from the side where there is no match.
- Example:

     SELECT employees.employee_id, employees.first_name, departments.department_name
     FROM employees
     FULL OUTER JOIN departments ON employees.department_id = departments.department_id;
     

5. CROSS JOIN:
- Definition: Returns the Cartesian product of both tables, i.e., it combines each row of the first table with all rows of the second table.
- Example:

     SELECT employees.first_name, departments.department_name
     FROM employees
     CROSS JOIN departments;
     

6. SELF JOIN:
- Definition: A join where a table is joined with itself.
- Example:

     SELECT e1.employee_id, e1.first_name, e2.first_name AS manager_name
     FROM employees e1
     INNER JOIN employees e2 ON e1.manager_id = e2.employee_id;

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POWER BI INTERVIEW PREPARATION PART-18

What are hierarchies in Power BI and how are they used?

Answer:
- Hierarchies in Power BI are used to represent data at different levels of granularity, such as Year > Quarter > Month > Day.
- They enable users to drill down into data to analyze it at various levels of detail.

SQL INTERVIEW PREPARATION PART-43

What is a primary key and a foreign key in SQL? Explain with examples.

Answer:

Primary Key:
- Definition: A primary key is a column (or a set of columns) in a table that uniquely identifies each row in that table. Primary keys must contain unique values and cannot contain NULL values.
- Purpose: To ensure each record in a table is unique and to serve as a reference point for other tables.
- Example:

  CREATE TABLE employees (
      employee_id INT PRIMARY KEY,
      first_name VARCHAR(50),
      last_name VARCHAR(50),
      hire_date DATE
  );
  

In this example, employee_id is the primary key of the employees table. It uniquely identifies each employee.

Foreign Key:
- Definition: A foreign key is a column (or a set of columns) in one table that uniquely identifies a row in another table. The foreign key establishes a relationship between the two tables.
- Purpose: To maintain referential integrity between the related tables by ensuring that the value in the foreign key column(s) matches a value in the referenced primary key column(s).
- Example:

  CREATE TABLE departments (
      department_id INT PRIMARY KEY,
      department_name VARCHAR(50)
  );

  CREATE TABLE employees (
      employee_id INT PRIMARY KEY,
      first_name VARCHAR(50),
      last_name VARCHAR(50),
      hire_date DATE,
      department_id INT,
      FOREIGN KEY (department_id) REFERENCES departments(department_id)
  );
  

In this example, department_id in the employees table is a foreign key that references department_id in the departments table. This establishes a relationship between employees and their respective departments.

Key Differences:

| Feature | Primary Key | Foreign Key |
|---------------|---------------------------------------|----------------------------------------|
| Uniqueness | Must be unique | Can have duplicate values |
| NULL Values | Cannot be NULL | Can contain NULL values |
| Purpose | Uniquely identifies each row in a table| Establishes a relationship between tables |
| Table | Each table can have only one primary key | A table can have multiple foreign keys |
| Enforcement | Enforces entity integrity | Enforces referential integrity |

Example of Referential Integrity:

-- Insert into departments
INSERT INTO departments (department_id, department_name) VALUES (1, 'HR');
INSERT INTO departments (department_id, department_name) VALUES (2, 'Finance');

-- Insert into employees
INSERT INTO employees (employee_id, first_name, last_name, hire_date, department_id)
VALUES (101, 'Alice', 'Smith', '2024-01-15', 1); -- Valid: department_id 1 exists
INSERT INTO employees (employee_id, first_name, last_name, hire_date, department_id)
VALUES (102, 'Bob', 'Brown', '2024-02-20', 3); -- Invalid: department_id 3 does not exist, will cause an error

Tip: Understanding primary and foreign keys is essential for designing relational databases that maintain data integrity and support complex relationships between tables. Always ensure that primary keys are unique and non-null and that foreign keys correctly reference existing primary key values in related tables.

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SQL INTERVIEW PREPARATION PART-44

What are aggregate functions in SQL? Provide examples.

Answer:

Aggregate Functions:
Aggregate functions in SQL perform calculations on a set of values and return a single value. They are often used with the GROUP BY clause to group the result set by one or more columns.

Common Aggregate Functions:

1. COUNT():
- Purpose: Returns the number of rows that match a specified condition.
- Example:

     SELECT COUNT(*) AS total_employees FROM employees;
     

2. SUM():
- Purpose: Returns the total sum of a numeric column.
- Example:

     SELECT SUM(salary) AS total_salary FROM employees;
     

3. AVG():
- Purpose: Returns the average value of a numeric column.
- Example:

     SELECT AVG(salary) AS average_salary FROM employees;
     

4. MIN():
- Purpose: Returns the minimum value in a set of values.
- Example:

     SELECT MIN(salary) AS minimum_salary FROM employees;
     

5. MAX():
- Purpose: Returns the maximum value in a set of values.
- Example:

     SELECT MAX(salary) AS maximum_salary FROM employees;
     

Using Aggregate Functions with GROUP BY:

Aggregate functions are commonly used with the GROUP BY clause to group rows that have the same values in specified columns into summary rows.

Example:

SELECT department_id, COUNT(*) AS total_employees, AVG(salary) AS average_salary
FROM employees
GROUP BY department_id;

In this example, the query groups the employees by their department_id and calculates the total number of employees and the average salary for each department.

Example Scenario:
Consider the following employees table:

+-------------+------------+--------+-------------+
| employee_id | first_name | salary | department_id|
+-------------+------------+--------+-------------+
| 1           | Alice      | 60000  | 1           |
| 2           | Bob        | 55000  | 1           |
| 3           | Carol      | 75000  | 2           |
| 4           | David      | 80000  | 2           |
| 5           | Eve        | 72000  | 3           |
+-------------+------------+--------+-------------+

Using aggregate functions:

-- COUNT example
SELECT COUNT(*) AS total_employees FROM employees;

-- SUM example
SELECT SUM(salary) AS total_salary FROM employees;

-- AVG example
SELECT AVG(salary) AS average_salary FROM employees;

-- MIN example
SELECT MIN(salary) AS minimum_salary FROM employees;

-- MAX example
SELECT MAX(salary) AS maximum_salary FROM employees;

-- Using GROUP BY
SELECT department_id, COUNT(*) AS total_employees, AVG(salary) AS average_salary
FROM employees
GROUP BY department_id;

Tip: Aggregate functions are powerful tools for summarizing and analyzing data in SQL. They are essential for generating reports and insights from large datasets. Practice using aggregate functions with and without the GROUP BY clause to understand their capabilities fully.

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Essential SQL Functions 👇👇

### DATE AND TIME FUNCTIONS:
- NOW(): Returns the current date and time.
- CURDATE(): Returns the current date.
- CURTIME(): Returns the current time.
- DATE(): Extracts the date part of a date or datetime expression.
- DATEDIFF(): Returns the number of days between two date values.
- YEAR(): Extracts the year.
- MONTH(): Extracts the month.
- DAY(): Extracts the day of the month.
- HOUR(): Extracts the hour.
- MINUTE(): Extracts the minute.
- SECOND(): Extracts the second.

### AGGREGATE FUNCTIONS:
- SUM(): Returns the sum of a set of values.
- AVG(): Returns the average value of a numeric column.
- MIN(): Returns the minimum value in a set of values.
- MAX(): Returns the maximum value in a set of values.
- COUNT(): Returns the number of rows that matches a specified condition.
- COUNT(*): Returns the number of rows in a table.
- COUNT(DISTINCT column_name): Returns the number of distinct values in a column.

### STRING FUNCTIONS:
- CONCAT(): Concatenates two or more strings.
- LENGTH(): Returns the length of a string.
- UPPER(): Converts a string to upper-case.
- LOWER(): Converts a string to lower-case.
- LEFT(): Extracts a number of characters from a string (starting from left).
- RIGHT(): Extracts a number of characters from a string (starting from right).
- SUBSTRING(): Extracts a substring from a string.

### NUMERIC FUNCTIONS:
- ROUND(): Rounds a number to a specified number of decimal places.
- FLOOR(): Returns the largest integer value less than or equal to a number.
- CEIL(): Returns the smallest integer value greater than or equal to a number.
- ABS(): Returns the absolute value of a number.

### INFORMATION FUNCTIONS:
- ISNULL(): Returns a specified value if the expression is NULL.
- COALESCE(): Returns the first non-null value in a list.
- NULLIF(): Returns NULL if the two expressions are equal.

### LOGICAL FUNCTIONS:
- IF(): Returns one value if a condition is TRUE, and another value if it is FALSE.
- CASE: Evaluates a list of conditions and returns one of multiple possible result expressions.
- AND: Combines two or more conditions and returns TRUE if all conditions are TRUE.
- OR: Combines two or more conditions and returns TRUE if any condition is TRUE.
- NOT: Reverses the value of a boolean expression.

### JSON FUNCTIONS:
- JSON_EXTRACT(): Extracts data from a JSON document.
- JSON_OBJECT(): Creates a JSON object from a list of key-value pairs.

### WINDOW FUNCTIONS:
- ROW_NUMBER(): Assigns a unique sequential integer to rows within a partition.
- RANK(): Assigns a rank to each row within a partition.
- DENSE_RANK(): Similar to RANK(), but without gaps in the ranking sequence.
- NTILE(): Divides rows into a specified number of approximately equal groups.

### OTHER FUNCTIONS:
- CAST(): Converts a value of one data type to another.
- CONVERT(): Converts a value of one data type to another.
- COALESCE(): Returns the first non-null expression among its arguments.

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Commonly used Python functions and methods:

### STRING FUNCTIONS:
- len(): Returns the length of a string.
- str.upper(): Converts a string to upper-case.
- str.lower(): Converts a string to lower-case.
- str.capitalize(): Capitalizes the first character of a string.
- str.split(): Splits a string into a list.
- str.join(): Joins elements of a list into a string.
- str.replace(): Replaces a specified phrase with another specified phrase.
- str.strip(): Removes whitespace from the beginning and end of a string.

### LIST FUNCTIONS:
- len(): Returns the length of a list.
- list.append(): Adds an item to the end of the list.
- list.extend(): Adds the elements of a list (or any iterable) to the end of the current list.
- list.insert(): Adds an item at a specified position.
- list.remove(): Removes the first item with the specified value.
- list.pop(): Removes the item at the specified position.
- list.index(): Returns the index of the first element with the specified value.
- list.sort(): Sorts the list.
- list.reverse(): Reverses the order of the list.

### DICTIONARY FUNCTIONS:
- dict.keys(): Returns a list of all the keys in the dictionary.
- dict.values(): Returns a list of all the values in the dictionary.
- dict.items(): Returns a list of tuples, each tuple containing a key and a value.
- dict.get(): Returns the value of the specified key.
- dict.update(): Updates the dictionary with the specified key-value pairs.
- dict.pop(): Removes the element with the specified key.

### TUPLE FUNCTIONS:
- len(): Returns the length of a tuple.
- tuple.count(): Returns the number of times a specified value appears in a tuple.
- tuple.index(): Searches the tuple for a specified value and returns the position of where it was found.

### SET FUNCTIONS:
- len(): Returns the length of a set.
- set.add(): Adds an element to the set.
- set.remove(): Removes the specified element.
- set.union(): Returns a set containing the union of sets.
- set.intersection(): Returns a set containing the intersection of sets.
- set.difference(): Returns a set containing the difference of sets.
- set.symmetric_difference(): Returns a set with elements in either the set or the specified set, but not both.

### NUMERIC FUNCTIONS:
- abs(): Returns the absolute value of a number.
- round(): Rounds a number to a specified number of digits.
- max(): Returns the largest item in an iterable.
- min(): Returns the smallest item in an iterable.
- sum(): Sums the items of an iterable.

### DATE AND TIME FUNCTIONS (datetime module):
- datetime.datetime.now(): Returns the current date and time.
- datetime.datetime.today(): Returns the current local date.
- datetime.datetime.strftime(): Formats a datetime object as a string.
- datetime.datetime.strptime(): Parses a string to a datetime object.

### FILE I/O FUNCTIONS:
- open(): Opens a file and returns a file object.
- file.read(): Reads the contents of a file.
- file.write(): Writes data to a file.
- file.readlines(): Reads all the lines of a file into a list.
- file.close(): Closes the file.

### GENERAL FUNCTIONS:
- print(): Prints to the console.
- input(): Reads a string from standard input.
- type(): Returns the type of an object.
- isinstance(): Checks if an object is an instance of a class or a tuple of classes.
- id(): Returns the identity of an object.

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SQL INTERVIEW PREPARATION PART-53

What is a subquery in SQL? Provide an example.

Answer:

Subquery:
A subquery, also known as an inner query or nested query, is a query within another SQL query and embedded within the WHERE, HAVING, FROM, or SELECT clauses. Subqueries can be used to perform operations that need to be completed in multiple steps.

Types of Subqueries:

1. Single-row Subquery:
- Returns a single row and single column.
- Example:

     SELECT first_name, last_name
     FROM employees
     WHERE salary > (SELECT AVG(salary) FROM employees);
     

2. Multi-row Subquery:
- Returns multiple rows and a single column.
- Example:

     SELECT first_name, last_name
     FROM employees
     WHERE department_id IN (SELECT department_id FROM departments WHERE location = 'New York');
     

3. Multi-column Subquery:
- Returns multiple columns and rows.
- Example:

     SELECT first_name, last_name
     FROM employees
     WHERE (department_id, salary) IN (SELECT department_id, MAX(salary) FROM employees GROUP BY department_id);
     

4. Correlated Subquery:
- Refers to columns in the outer query and executes once for each row selected by the outer query.
- Example:

     SELECT e1.first_name, e1.salary
     FROM employees e1
     WHERE e1.salary > (SELECT AVG(e2.salary) FROM employees e2 WHERE e2.department_id = e1.department_id);
     

Using Subqueries in Different Clauses:

1. In the SELECT Clause:
- Example:

     SELECT first_name, last_name, (SELECT department_name FROM departments WHERE departments.department_id = employees.department_id) AS department
     FROM employees;
     

2. In the FROM Clause:
- Example:

     SELECT AVG(salary)
     FROM (SELECT salary FROM employees WHERE department_id = 1) AS dept1_salaries;
     

3. In the WHERE Clause:
- Example:

     SELECT first_name, last_name
     FROM employees
     WHERE department_id = (SELECT department_id FROM departments WHERE department_name = 'HR');
     

4. In the HAVING Clause:
- Example:

     SELECT department_id, AVG(salary)
     FROM employees
     GROUP BY department_id
     HAVING AVG(salary) > (SELECT AVG(salary) FROM employees);
     

Example Scenario:
Consider the following employees and departments tables:

-- employees table
+-------------+------------+----------+-------------+
| employee_id | first_name | salary   | department_id|
+-------------+------------+----------+-------------+
| 1           | Alice      | 60000    | 1           |
| 2           | Bob        | 55000    | 1           |
| 3           | Carol      | 75000    | 2           |
| 4           | David      | 80000    | 2           |
| 5           | Eve        | 72000    | 3           |
+-------------+------------+----------+-------------+

-- departments table
+---------------+-----------------+---------+
| department_id | department_name | location|
+---------------+-----------------+---------+
| 1             | HR              | London  |
| 2             | Finance         | New York|
| 3             | IT              | San Francisco|
+---------------+-----------------+---------+

Using subqueries:

-- Single-row subquery example
SELECT first_name, last_name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

-- Multi-row subquery example
SELECT first_name, last_name
FROM employees
WHERE department_id IN (SELECT department_id FROM departments WHERE location = 'New York');

-- Multi-column subquery example
SELECT first_name, last_name
FROM employees
WHERE (department_id, salary) IN (SELECT department_id, MAX(salary) FROM employees GROUP BY department_id);

-- Correlated subquery example
SELECT e1.first_name, e1.salary
FROM employees e1
WHERE e1.salary > (SELECT AVG(e2.salary) FROM employees e2 WHERE e2.department_id = e1.department_id);

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