Continuous Learning in SQL: A Data Engineer's Journey Through LeetCode 

As a seasoned professional in the field of data engineering, I've come to realize that the learning journey never truly ends. This truth became particularly evident during my preparation for a Data Engineer role at Meta back in 2019. At that time, thoroughly evaluating and understanding all the LeetCode SQL questions was key to my successful interview and subsequent journey at Meta. However, the landscape has evolved significantly since then.

Today, LeetCode hosts approximately 300 SQL questions, a number that continues to grow. This expansion reflects the ever-changing nature of our field and the increasing complexity of data-related challenges in the industry. The sheer volume of questions now available presents both an opportunity and a challenge for aspiring and experienced data professionals alike.

In my current review, I'm not just aiming to solve these problems; I'm diving deeper to uncover additional patterns, explore the limitations and possibilities of various interview platforms, and investigate the nuances of different SQL engines and their versions. This comprehensive approach goes beyond mere problem-solving – it's about gaining a holistic understanding of SQL in various real-world contexts.

One advantage of focusing on SQL questions is the relatively manageable number of problems compared to other programming languages, even with the recent growth. Currently, I'm tackling the medium and hard difficulty questions, with about 50 questions in each category left. I started by completing the easy questions first, although I found that the 'easy' label wasn't always an accurate reflection of the problem's complexity.

As I progress through these questions, I'm not just solving problems; I'm uncovering patterns, reinforcing fundamentals, and pushing the boundaries of my SQL knowledge. This process is helping me to:

1. Identify common patterns in SQL problem-solving

2. Understand the strengths and limitations of different SQL engines and their versions

3. Explore advanced SQL concepts that may not be used daily but are crucial for complex data manipulations

4. Improve my ability to optimize queries for better performance

5. Analyze the capabilities and constraints of various interview platforms

My strategy for tackling these remaining questions varies from day to day. Sometimes I focus on a specific difficulty level, other times I choose questions based on topics I want to reinforce or particular SQL engine features I want to explore. The key is to maintain steady progress and remain flexible in my approach.

Here's a snapshot of the remaining questions I'm working through:

This journey has reminded me of the importance of continuous learning in our field. Even as experienced professionals, we can always find new challenges that push us to grow and refine our skills. The expansion of SQL questions on LeetCode since my initial preparation in 2019 is a testament to the evolving nature of our field and the need to stay current.

Whether you're preparing for an interview, looking to advance in your current role, or simply passionate about mastering your craft, I encourage you to seek out these opportunities for growth. The landscape of SQL and data engineering is constantly changing, and staying ahead requires a commitment to lifelong learning.

In the coming weeks, I'll be sharing insights from specific questions, discussing interesting patterns I've discovered, and reflecting on how this process is shaping my approach to data engineering. I'll also delve into the differences between various SQL engines and interview platforms, providing a more comprehensive view of the SQL ecosystem. Stay tuned for more updates on this SQL learning journey!