Crack SQL Interview : 200+ Questions with Concept Building

It looks like you've outlined a comprehensive curriculum for learning SQL and its advanced applications. This curriculum covers the full spectrum of SQL knowledge, from basic syntax to complex queries and performance optimization, and even touches on how to apply SQL in data analysis contexts and interview scenarios. Here's a brief elaboration on each topic:

1. Introduction to SQL: Understanding the fundamentals of SQL (Structured Query Language), including its uses, syntax, and basic commands like SELECT, INSERT, UPDATE, and DELETE.

2. Data Manipulation Language (DML): Deep dive into DML operations to manipulate data within a database effectively.

3. Data Definition Language (DDL): Learning how to define and modify the structure of database objects such as tables, views, and indexes with DDL statements like CREATE, ALTER, and DROP.

4. Data Control Language (DCL): Understanding how to control access to database objects with commands like GRANT and REVOKE.

5. Self-joins and their applications: Mastering the use of self-joins to query related records within the same table.

6. CROSS JOINs and Cartesian products: Learning about the Cartesian product of two tables and how to use CROSS JOINs intentionally, as well as understanding when to avoid them due to their potential performance implications.

7. Handling NULL values in joins: Gaining proficiency in managing NULL values that can arise during join operations and understanding their impact on query results.

8. Subqueries and Nested Queries: Understanding how to write subqueries, including correlated and non-correlated subqueries, and knowing where to use them within different parts of an SQL statement for complex data retrieval tasks.

9. Aggregation and Grouping: Mastering the use of aggregate functions (SUM, AVG, COUNT, etc.) and learning how to group records using GROUP BY for summarizing data.

10. Advanced SQL Techniques: Exploring advanced topics like window functions for analytics, Common Table Expressions (CTEs) for complex query construction, recursive CTEs for hierarchical data analysis, pivoting and unpivoting for reporting purposes, etc.

11. Data Types and Functions: Understanding the various SQL data types available and how to use built-in functions for string manipulation, date and time operations, mathematical computations, and more.

12. Indexes and Performance Tuning: Learning about the role of indexes in improving query performance, understanding execution plans, and knowing when and how to create indexes to optimize data retrieval.

13. Transactions and Concurrency: Ensuring data integrity by mastering transactions with ACID properties, managing isolation levels, handling deadlocks, and understanding locking mechanisms.

14. Views and Stored Procedures: Creating and managing database views for simpler query execution, writing optimized stored procedures, and understanding the differences between functions and stored procedures.

15. Database Design Principles: Applying normalization to reduce data redundancy, creating Entity-Relationship Diagrams (ERDs) for logical database design, and ensuring data integrity through primary and foreign keys.

16. Security and Access Control: Implementing security measures to protect sensitive data, setting up user authentication and authorization, managing access roles, and understanding column-level security.

17. Handling Large Datasets: Learning strategies for working with large datasets such as partitioning, batch processing, and indexing techniques that are specific to big data environments.

18. SQL in Data Analysis: Applying SQL to perform complex analytical tasks, analyze cohorts, handle time series data, and conduct A/B testing.

19. Interview Strategies: Developing problem-solving skills for SQL, optimizing queries on the fly, articulating thought processes, and effectively communicating solutions in an interview setting.

This curriculum is designed to provide a deep understanding of SQL across various contexts, ensuring that learners are well-equipped to handle real-world database challenges, analyze complex datasets, and perform well in technical interviews.