Yes, this is a reasonable approach, and it’s actually quite common in ETL and data engineering workflows. Using a configurable date parameter allows you to control how much data is processed in different environments without maintaining separate codebases.

For example:

• Development: Process only the last 7–30 days of data for faster iterations.
• UAT: Process a few months of data to validate business logic and performance.
• Production: Process the full historical dataset or use incremental loading.

The key is to make the date range configurable rather than hardcoding it.

Here’s a simple example in SQL:

DECLARE @StartDate DATE = '2025-01-01';

SELECT *
FROM Sales
WHERE OrderDate >= @StartDate;

Or, if you’re using Python:

from datetime import datetime, timedelta

environment = "DEV"

if environment == "DEV":
    start_date = datetime.today() - timedelta(days=30)
elif environment == "UAT":
    start_date = datetime.today() - timedelta(days=180)
else:  # PROD
    start_date = None

if start_date:
    filtered_data = df[df["OrderDate"] >= start_date]
else:
    filtered_data = df

A few best practices:

• Store the date parameter in a configuration file or environment variable instead of embedding it in the code.
• Log the date range used for every execution so it’s easy to troubleshoot.
• Prefer incremental loading (using a watermark or last processed timestamp) for production pipelines rather than repeatedly processing all historical data.
• Validate that downstream reports behave correctly when only a subset of data is processed in non-production environments.

Overall, using a date parameter is a clean and maintainable solution. It improves development speed, reduces resource consumption in lower environments, and keeps the same processing logic across DEV, UAT, and PROD, minimizing the risk of environment-specific bugs.