I'm looking to transition our data validation rules from Python scripts into a YAML configuration format. We have various data sources such as CSV and Parquet files stored in S3, as well as Postgres and Snowflake databases. Currently, the validation logic is scattered across different Python scripts, making it tough for our analysts to comprehend the validation criteria without diving into the code.
My idea is to set up YAML-defined rules that non-engineers can manage. Here's a basic example of what I envision:
```yaml
sources:
orders:
type: csv
path: s3://bucket/orders.csv
rules:
- column: order_id
type: integer
unique: true
not_null: true
severity: critical
- column: status
type: string
allowed_values: [pending, shipped, delivered, cancelled]
severity: warning
- column: amount
type: float
min: 0
max: 100000
null_threshold: 0.02
severity: critical
- column: email
type: string
regex: "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
severity: warning
```
The engine would read these specs, then perform aggregate checks in SQL for nulls, ranges, and unique constraints, while handling row-level checks for regex and allowed values.
My main challenge is how to represent cross-column rules succinctly, such as "if status = shipped then tracking_id must not be null" without making it overly complicated or verbose. Has anyone tackled this issue using a YAML configuration? Or do most people resort to using a Python DSL for such situations?
3 Answers
For handling that cross-column logic (like "if status = shipped, then tracking_id must not be null"), I'd recommend exploring strictyaml with a custom validator. It allows you to define advanced validation logic cleanly within the YAML.
It sounds like you're on the brink of reinventing something like Pydantic! While YAML might seem more readable, don't be surprised if analysts struggle with it just like they do with Python. It’s a tricky trade-off between flexibility and simplicity, honestly.
I think using Pydantic for your needs could be a good start. If it doesn't work out, I've had my fair share of similar challenges. I found two strategies helpful for handling complexity in mappings:
1. If certain rules are simple and repetitive, consider using a special flag to streamline them.
2. For more complex logic, sometimes referencing a Python function is the way to go. You could define a structure in YAML but process it through a Python callable to keep things robust while minimizing the potential for mistakes.
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