Window Functions 🪟🔍

🔍 Section 1: Introduction to Window Functions
- Understand the concept of window functions as a way to perform calculations across a set of rows related to the current row.
- Learn how window functions differ from aggregate functions and standard SQL functions.

SELECT column1, column2, SUM(column3) OVER (PARTITION BY column1 ORDER BY column2) AS running_total
FROM table_name;

🔍 Section 2: Common Window Functions
- Explore commonly used window functions, including ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().
- Understand the syntax and usage of each window function for different analytical purposes.

SELECT column1, column2, ROW_NUMBER() OVER (ORDER BY column1) AS row_num
FROM table_name;

🔍 Section 3: Partitioning Data
- Learn how to partition data using window functions to perform calculations within specific groups.
- Understand the significance of the PARTITION BY clause in window function syntax.

SELECT column1, column2, AVG(column3) OVER (PARTITION BY column1) AS avg_column3
FROM table_name;

🔍 Section 4: Ordering Results
- Explore techniques for ordering results within window functions to control the calculation scope.
- Understand the impact of the ORDER BY clause on window function behavior.

SELECT column1, column2, MAX(column3) OVER (ORDER BY column1 ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS max_window
FROM table_name;

🔍 Section 5: Advanced Analytical Capabilities
- Discover advanced analytical capabilities enabled by window functions, such as cumulative sums, moving averages, and percentile rankings.
- Explore real-world scenarios where window functions can provide valuable insights into data trends and patterns.

SELECT column1, column2, AVG(column3) OVER (ORDER BY column1 ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg
FROM table_name;

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𝐒𝐨𝐦𝐞 𝐁𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐲𝐨𝐮𝐫 𝐒𝐐𝐋 𝐪𝐮𝐞𝐫𝐢𝐞𝐬:

1. Simplify Joins

• Decompose complex joins into simpler, more manageable queries when possible.
• Index columns that are used as foreign keys in joins to enhance join performance.

2. Query Structure Optimization

• Apply WHERE clauses as early as possible to filter out rows before they are processed further.
• Utilize LIMIT or TOP clauses to restrict the number of rows returned, which can significantly reduce processing time.

3. Partition Large Tables

• Divide large tables into smaller, more manageable partitions.
• Ensure that each partition is properly indexed to maintain query performance.

4. Optimize SELECT Statements

• Limit the columns in your SELECT clause to only those you need. Avoid using SELECT * to prevent unnecessary data retrieval.
• Prefer using EXISTS over IN for subqueries to improve query performance.

5. Use Temporary Tables Wisely

• Temporary Tables: Use temporary tables to save intermediate results when you have a complex query. This helps break down a complicated query into simpler steps, making it easier to manage and faster to run.

6. Optimize Table Design

• Normalize your database schema to eliminate redundant data and improve consistency.
• Consider denormalization for read-heavy systems to reduce the number of joins needed.

7. Avoid Correlated Subqueries

• Replace correlated subqueries with joins or use derived tables to improve performance.
• Correlated subqueries can be very inefficient as they are executed multiple times.

8. Use Stored Procedures:

• Put complicated database tasks into stored procedures. These are pre-written sets of instructions saved in the database. They make your queries run faster because the database doesn’t have to figure out how to execute them each time

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Preparing for a SQL interview?

Focus on mastering these essential topics:

1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!

2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.

3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.

4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.

5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.

6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.

7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.

8. Indexing: Understand how proper indexing can significantly boost query performance.

9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.

10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.

11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.

12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.

If we master/ Practice in these topics we can track any SQL interviews..

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Getting started with SQL comparison operators.

If you're new to SQL, understanding comparison operators is one of the first things you'll need to learn.

They’re really important for filtering and analyzing your data. Let’s break them down with some simple examples.

Comparison operators let you compare values in SQL queries. Here are the basics:
1. = (Equal To): Checks if two values are the same.
Example: SELECT * FROM Employees WHERE Age = 30; (This will find all employees who are exactly 30 years old).

2. <> or != (Not Equal To): Checks if two values are different.
Example: SELECT * FROM Employees WHERE Age <> 30; (This will find all employees who are not 30 years old).

3. > (Greater Than): Checks if a value is larger.
Example: SELECT * FROM Employees WHERE Salary > 50000; (This will list all employees earning more than 50,000).

4. < (Less Than): Checks if a value is smaller.
Example: SELECT * FROM Employees WHERE Salary < 50000; (This will show all employees earning less than 50,000).

5. >= (Greater Than or Equal To): Checks if a value is larger or equal.
Example: SELECT * FROM Employees WHERE Age >= 25; (This will find all employees who are 25 years old or older).

6. <= (Less Than or Equal To): Checks if a value is smaller or equal.
Example: SELECT * FROM Employees WHERE Age <= 30; (This will find all employees who are 30 years old or younger).

These simple operators can help you get more accurate results in your SQL queries.

Keep practicing and you’ll be great at SQL in no time.

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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

SQL Roadmap 👇👇
https://www.linkedin.com/posts/sql-analysts_sql-activity-7235150234706690048-ydnI

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To be honest, I also wasn't aware of this fullform until today 😂

5 Key SQL Aggregate Functions for data analyst

🍞SUM(): Adds up all the values in a numeric column.

🍞AVG(): Calculates the average of a numeric column.

🍞COUNT(): Counts the total number of rows or non-NULL values in a column.

🍞MAX(): Returns the highest value in a column.

🍞MIN(): Returns the lowest value in a column.

Examples ⬇️⬇️⬇️