I’m designing a pipeline where the same dataset needs to flow through different environments: Dev, UAT, and Prod. The challenge is that the production dataset is huge, but in Dev and UAT, I only need a subset of the data to test transformations and run analytics efficiently.

My idea is to use a date parameter (e.g., start_date/end_date) to limit the data volume in non-prod environments, so Dev and UAT only process a smaller, manageable slice of the dataset.

I’m wondering:

• Is using a date parameter a common or recommended practice for this?
• Are there risks in this approach that I should be aware of, such as skewed test results or missed edge cases?
• Are there better strategies for controlling data volume across environments while maintaining meaningful test coverage?

I’d love to hear how others handle large datasets across multiple environments in a practical, maintainable way.

As AI automates coding, querying, reporting, and even parts of analysis, the expectations from data professionals are starting to shift. Many companies are now evaluating candidates beyond technical execution alone, focusing more on problem-solving, business understanding, system thinking, and adaptability in AI-assisted environments.

How do you think data interviews are evolving in today’s AI-driven industry?

Here’s a clean version you can use:

Short Question:
How do you design a data structure to efficiently track and retrieve the first non-repeating character in a stream?

Description:
In many data stream problems, characters arrive one by one, and we need to continuously determine the first non-repeating character at any given point.

The challenge is to design a data structure that:

• Supports real-time updates as new characters arrive

• Efficiently tracks frequencies of characters

• Quickly returns the first character that has appeared only once so far

A brute-force approach would re-scan the entire stream after each insertion, which is inefficient. Instead, we need an optimized approach using a combination of data structures to maintain order and frequency.

Approach:
Use:

• A hash map to store frequency of each character

• A queue to maintain insertion order

Python Code:

from collections import deque

class FirstNonRepeating:
    def __init__(self):
        self.freq = {}
        self.queue = deque()

    def add(self, char):
        # Update frequency
        self.freq[char] = self.freq.get(char, 0) + 1
        
        # Add to queue
        self.queue.append(char)
        
        # Remove repeating characters from front
        while self.queue and self.freq[self.queue[0]] > 1:
            self.queue.popleft()

    def get_first_non_repeating(self):
        return self.queue[0] if self.queue else None


# Example usage
stream = FirstNonRepeating()

for ch in "aabcbd":
    stream.add(ch)
    print(stream.get_first_non_repeating())