This Tutorial is for revision purpose before your Interview, requires basic understanding for SQL.

Module 1: Introduction to Databases & SQL Fundamentals

SQL (Structured Query Language) is a standard language for managing and quering data stored in a relational database (RDBMS) Database means where all the data is stored ( like military - base) in table format consisting rows ( Records ) and colomns ( Feilds ) this tables are called as " Schema" , these schemas are connected with each other through a logical reationship ( will explore this ) thats why this type of database is called as Relational Database and this ecosystem is called as Relational Database Management System ( RDBMS ).

Must Read Before Attending Data Engineering Interview

Choosing the right database type depends on your use case. SQL databases excel at structured, transactional 
workloads while NoSQL systems handle scale and flexibility. Here is a direct comparison of both paradigms.

Real-World Scenario:  

Amazon uses SQL databases (like Aurora/PostgreSQL) for transactional operations 4 processing orders, payments, and inventory updates where data consistency is critical. Simultaneously, they use NoSQL (like DynamoDB) for their recommendation engine and session management where flexible schema and horizontal scaling are needed.


The four CRUD Operations.

Every database revolves around these four fundamental operations.

Module 2: Database Design, Keys,  Constraints & Relationships

A well designed database begins with understanding of keys and constarints. Key defines how rows are uniquiely identified and how tables are related to each other. Constraints enforce bussiness rules at the database level ( Validations ) before data even reach your application.

Diffrence between different keys ( Most Asked )

Constraints are rules enforced at the database level to maintain data integrity. They act as a safety net 4 preventing bad data from entering your tables regardless of how the application behaves. Understanding each constraint and when to apply it is a foundational database design skill.

NULL Handling Tip: 

SQL Server treats NULL = NULL as TRUE, allowing only ONE NULL in a 
UNIQUE column. PostgreSQL and MySQL treat NULL <> NULL, allowing MULTIPLE NULLs in a UNIQUE column. Know your database engine!

Module 3: Data Definition Language (DDL) 4  Creating Tables

DDL commands define the structure of your database. The CREATE TABLE statement specifies the table name, columns, data types, and constraints. Getting your table structure right from the start saves enormous effort in later migrations and refactoring.

Creating Related Tables with Foreign Keys

In a relational database, tables reference each other using foreign keys. This example shows how an Employees table relates to a Departments table, and how a Job_History table uses a composite primary key to track career changes over time.

The Job_History table uses a composite primary key of (employee_id, start_date) because an employee can have multiple job records 4 but onlyone starting on any given date. This combination is always unique.

Real-World Scenario:

An HR system needs tables for Employees, Departments, Jobs, and Job_History. Foreign keys ensure every job history record maps to a valid employee, department, and job 4 preventing orphaned records.

Module 4: Data Manipulation 4 SELECT,  WHERE, ORDER BY, LIMIT

The SELECT statement is the most frequently used SQL command. Mastering it 4 along with ORDER BY, and WHERE, LIMIT 4 gives you the ability to retrieve exactly the data you need from any table.

Module 5: Operators & Filtering : AND, OR,  NOT, LIKE, IN, BETWEEN

SQL provides a rich set of filtering operators that let you express complex conditions with clean, readable syntax. These operators are the building blocks of every WHERE clause you will ever write.

The IN operator in SQL is used within a WHERE clause to determine if a value matches any value in a specified list or subquery. It acts as a shorthand for multiple OR conditions, making your queries significantly shorter and easier to read.

Matching a List of Values: You can provide an explicit list of values (strings, numbers, or dates) to filter your results.

Using a Subquery: You can filter results based on the output of another query.

Excluding Values: When paired with NOT, it returns all records that are not in the specified list.

Real-World: 

CRM search uses LIKE for partial name search, IN for city filter, and BETWEEN for date range filtering 4 all in a single query.


Module 6: Aggregate Functions 4 COUNT,  SUM, AVG, MIN, MAX

Aggregate functions perform calculations on a set of rows and return a single result. They are the foundation of every analytical query 4 from monthly financial reports to performance dashboards. These functions always operate on a column's values across multiple rows.

Real-World Scenario: 

A CFO needs a monthly snapshot 4 total payroll (SUM), headcount (COUNT), average compensation (AVG), highest salary (MAX) for budget planning, and newest hire date (MAX on hire_date) to track recruitment velocity. These aggregates form the backbone of every financial dashboard.

Module 7: GROUP BY & HAVING ( Most Asked )

GROUP BY and HAVING are the pair that unlocks true analytical power in SQL. Together they let you summarize data by category and then filter on those summaries 4 a pattern used in virtually every business intelligence query ever written.

GROUP BY groups rows sharing a common value and applies aggregate functions to each group separately.

Real-World Scenario: 

A regional sales director wants to identify underperforming territories. 
They GROUP BY region, calculate SUM(revenue) and COUNT(deals), then use HAVING 
SUM(revenue) < 100000 to find regions falling below target. This two-step process is impossible with WHERE alone

Module 8: Joins - INNER, LEFT, RIGHT,  FULL OUTER, SELF & CROSS ( Most Asked )

Joins are the most powerful feature of relational databases. They allow you to combine data from multiple tables based on related columns 4 turning normalized, separated data back into meaningful, unified result sets. Understanding every join type is non-negotiable for any SQL practitioner.

Use Programiz Compiler for live practice of SQL.

IPL Fixtures: 

A SELF JOIN on teams with t1.team_id < t2.team_id generates unique match pairs without duplicates. With 4 teams, that's 6 unique matchups: n(n-1)/2.


Module 9: Subqueries & Nested Queries ( Most Asked )

A subquery is a query nested inside another query. The inner query executes first, and its result is used by the outer query. Subqueries are incredibly versatile 4 they can appear in the WHERE clause, the clause, or even the FROM SELECT clause itself, enabling multi-layered logic within a single SQL statement.

Real-World Scenario: 

An HR team identifies employees eligible for a performance bonus those earning above the department average. The subquery calculates each department's average salary, and the outer query finds employees whose salary exceeds that average.


Module 10: Common Table Expressions  (CTEs) ( Most Asked )

A CTE (Common Table Expression) is a temporary named result set defined using the WITH keyword. It exists only for the duration of the query execution. CTEs dramatically improve the readability and maintainability of complex queries by breaking them into named, logical steps 4 similar to how functions work in programming languages.

Key Benefits: Improved readability, reusability within the query, ability to reference the result multiple times, and cleaner debugging. Note: A CTE does NOT save data or the query itself 4 it only lasts during execution.

MUST Practice Problems related to CTE in Hacker Rank ( Recommended )

CTEs become especially powerful when combined with aggregations and window functions. They allow you to compute intermediate results and then filter or transform them in a second step 4 a pattern that is otherwise messy with subqueries. This mirrors the ETL (Extract, Transform, Load) logic used in data 
engineering pipelines.

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