In real-world data engineering pipelines, one of the most common mistakes is postponing data cleansing until too late in the pipeline.

The cleaner your upstream data is, the simpler and more maintainable your downstream models will be.

Let’s break it down.

✅ The Principle

Whenever possible, cleanse your data as early as possible — ideally at the staging layer.

✅ The Why

1️⃣ Clear Separation of Responsibilities

  • Staging models are responsible for:

    • Cleaning
    • Normalizing
    • Type casting

  • If you push dirty data all the way down to your marts (such as dim_customer, fct_transaction), you will create:

    • Complex downstream models
    • Repeated filters
    • Fragile, hard-to-maintain code

2️⃣ The Marts Layer Should Only Handle Business Logic

  • Marts layer should focus purely on:

    • Joins
    • Aggregations
    • Dimension relationships

  • Cleaning, null handling, and standardization should already be done upstream.

  • This leads to much cleaner and more readable SQL.

3️⃣ This Is How Real Systems Are Designed

Pipeline flow:

source → staging (cleansing, standardization) → marts (modeling, aggregations)

✅ Summary Table

Layer Role Cleansing?
Staging Clean, normalize, type cast ✅ Mandatory
Marts Joins, aggregations, business logic ❌ Should receive clean data

✅ Practical Implementation Rules

At staging layer, apply:

  • Convert dirty strings ('NaN', empty strings, extreme values) → NULL
  • Drop rows with missing primary keys (e.g. cardholder_id IS NULL)
  • Allow non-key columns (e.g. description) to remain NULL — downstream can safely process them

🔥 How to Say This in a Real Interview or Portfolio:

“We standardized dirty strings at the staging layer, ensuring that marts always consume clean data. This simplifies downstream joins and aggregations, greatly improving stability and maintainability.”