Why SQL Still Rules the Data World?

SQL + Relational Databases = Structured Data Management

SQL + Joins = Seamless Data Integration

SQL + Aggregations = Powerful Data Summarization

SQL + Subqueries = Complex Data Retrieval

SQL + Indexing = Faster Query Performance

SQL + Transactions = Reliable Data Integrity

SQL + Views = Simplified Data Access

SQL + Stored Procedures = Efficient Data Operations

SQL + Triggers = Automated Actions Based on Data Changes

SQL + Constraints = Data Validation and Integrity

SQL + Normalization = Eliminate Redundancy

SQL + Data Warehousing = Scalable Data Storage Solutions

SQL + Data Lakes = Manage Vast Amounts of Raw Data

SQL + ETL Processes = Efficient Data Transformation

SQL + Backup and Recovery = Secure Data Management

SQL + Big Data Integration = Bridging SQL and NoSQL

SQL + Reporting Tools = Generating Insightful Reports

SQL + BI Tools = Business Intelligence Integration

SQL + Analytics = Deep Data Insights

SQL remains unbeatable with its ability to manage, query, and analyze data efficiently.

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Most Asked SQL Interview Questions at MAANG Companies??

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

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

SELECT * FROM table_name;

1. 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.

1. 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;

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

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

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

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

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

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

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

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

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

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

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

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SQL books won’t teach you this.

Natural Keys vs. Autoincrement IDs vs. Public IDs. (or maybe all together)

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Natural keys carry intrinsic meaning because they are part of the domain.

They are directly related to the data, making them intuitive and easy to understand. Examples include email addresses or employee IDs.

The problem is that they are usually not good for performance, but they can also be a security risk if you expose them.

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Autoincrement IDs automatically generate unique integers to identify rows within a table.

They are often used as primary keys.

Simple integers are fast for the database to index and query. They provide optimal performance.

However, they are vulnerable to enumeration attacks since predicting the next or previous record is easy.

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UUIDs (Universally Unique Identifiers) are 128-bit identifiers used to uniquely identify information without relying on a centralized authority.

They are difficult to guess, making them suitable for public exposure in APIs.

The problem is they are larger and more complex than integers. This can impact performance, particularly in indexing and storage.

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Combining different types of keys can offer a balanced solution:

• InternalID: Used for internal operations and relationships between tables.

• PublicID: Used in API responses and endpoints to securely reference user records.

• Email (Natural Key): Used to ensure unique identification of users within the business logic.

The mixed approach keeps your system fast, secure, and easy to understand.

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Programming languages in data science ??
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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);

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After learning SQL ??
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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.

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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.

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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.

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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.

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Once you've learned/mastered the fundamentals of SQL, try learning these:

- ?????: LEFT, RIGHT, INNER, OUTER joins.
- ????????? ?????????: Utilize SUM, COUNT, AVG, and others for efficient data summarization.
- ???? ???? ??????????: Use conditional logic to tailor query results.
- ???? ???? ?????????: Master manipulating dates and times for precise analysis.

Next, explore advanced methods to structure and reuse SQL code effectively:

- ?????? ????? ??????????? (????): Simplify complex queries into manageable parts to increase the readability.
- ??????????: Nest queries for more granular data retrieval.
- ????????? ??????: Create and manipulate temporary data sets for specific tasks.

Then, move on to advanced ones:

- ?????? ?????????: Perform advanced calculations over sets of rows with ease.
- ?????? ??????????: Create reusable SQL routines for streamlined operations.
- ????????: Automate database actions based on specific events.
- ????????? ????: Solve complex problems using recursive queries.
- ???????????? ?? ???????: Techniques to enhance performance and efficiency.

Here you can find essential SQL Interview Resources?
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