A Strategic Approach to LeetCode 1907: A Guide for Aspiring Data Engineers

Strategy was derived by me and I put next a lot of effort to let copilot help me with generating the blog post below

Welcome to this comprehensive guide on tackling LeetCode 1907 - Count Salary Categories. This isn’t just about solving a problem; it’s about developing a strategic approach that will serve you well in job interviews and your future career as a Data Engineer.

The Importance of Strategy

When preparing for job interviews, it’s easy to fall into the trap of trying to memorize solutions to common problems. However, this approach is not only exhausting but also ineffective in the long run. Instead, the key to success lies in understanding the underlying principles and developing a strategy to dissect and solve any problem that comes your way.

Dissecting the Example Question

Touching user and customer segmentation

LeetCode 1907, “Count Salary Categories,” is a fascinating query that offers more than meets the eye. If we were to generalize this question, it essentially asks us to segment customers or users based on a predefined rule-based classifier.

Interestingly, a rule-based classifier can be seen as a rudimentary form of a machine learning classifier. While this might seem unusual at first glance, it’s a helpful perspective that can deepen our understanding of both rule-based systems and machine learning.

Here’s a generalized structure of the data we’re working with:

+-------------------------+------------+

| Column Name       |   Type     |

+-------------------------+------------+

| [usertype]_id         |       int      | 

| [value_1]               |  [type_1]  |

| [value_2]               |  [type_2]  |

|         …                  |      …       |

| [value_x]               | [type_x]   |

+---------------------------------------+

In the upcoming section of the exercise, we’ll introduce a basic classifier. This will illustrate how you can perform customer or user segmentation. Additionally, this exercise will guide you on how to track when one of the provided segments changes, further enriching your data analysis skills.

Remember, the goal here isn’t just to solve the problem—it’s to understand the underlying concepts and strategies that will not only help you ace your job interviews but also become a successful Data Engineer.

Dealing with missing data

In the forthcoming section of the exercise, we’ll delve into the workings of a basic classifier. This will elucidate how you can execute customer or user segmentation effectively. An intriguing aspect of this exercise is its focus on handling missing values in the provided segmentations. Specifically, when a segmentation lacks a value, the exercise instructs us to count it as zero. This introduces us to the important concept of handling missing data, a common occurrence in real-world data sets.

Understanding the Interviewer’s Perspective

This exercise unravels several layers of complexity, particularly the inclusion of segments that are not immediately apparent. It’s crucial to understand why we also consider categories with no customer segments.

The primary reason for this approach is to streamline executive reporting. Including all categories, even those without current customer segments, ensures a comprehensive view of the data. This holistic perspective aids in strategic decision-making and planning.

The second reason pertains to the detection and resolution of potential issues in your rule-based classifier or machine learning model. When a category returns a zero, it could indicate a bug in your system. By including these categories in your report, you’re more likely to spot and address these issues promptly. If a category isn’t mentioned, it might be overlooked, depriving it of the attention it might need for optimization or debugging.

By understanding these reasons, you’ll be better equipped to tackle similar problems in your interviews and your role as a Data Engineer. Remember, every question is an opportunity to demonstrate your problem-solving skills and your understanding of the underlying concepts.

Crafting the Solution Strategy

The journey to implementing the rule-based classifier correctly begins with refreshing your understanding of the CASE logic. This is an essential tool in the SQL toolkit for any Data Scientist, Data Engineer, or Software Engineer.

Next, we need to define these categories. While LeetCode doesn’t currently support Presto, I’ve found that the new implementation of PostgreSQL provides a straightforward way to structure these categories. During an interview, it’s perfectly acceptable to ask your interviewer for guidance on how to convert a list of strings in your SELECT statement. In our day-to-day work, we often encounter situations where we need to refresh our memory. We typically do this by looking at existing examples, referring to the internal wiki, or using Google as our guide.

Finally, avoid the temptation to use a CROSS JOIN. Instead, consider the different types of joins you can use. The goal of the previous part was to create a second table that you could use in your join, ensuring that the relevant categories are displayed.

Remember, the key to acing your job interviews and succeeding as a Data Engineer lies not in memorizing solutions, but in understanding the underlying concepts and developing a strategic approach to problem-solving. Keep learning and keep growing!

Next Steps

If this exercise has piqued your interest, I encourage you to take the next step in your journey. Sign up on LeetCode and start exploring the wealth of SQL exercises they offer. It’s a fantastic platform that can significantly enhance your interview preparation.

But don’t stop there. Reflect on your approach to SQL interview preparation. Are you merely memorizing solutions, or are you truly understanding the problems and developing strategies to solve them? Remember, the latter is what will set you apart in your interviews and your career as a Data Engineer.

So, take action today. Start using a tool like LeetCode and transform your approach to problem-solving. It’s not just about acing your interviews; it’s about becoming a better engineer and continuously growing in your career.

Good luck with your interview preparation, and remember, every problem is an opportunity to learn and grow. Happy coding!

Useful reference https://dba.stackexchange.com/questions/129844/cross-product-between-table-column-and-input-values