</>
pythonintermediate

attrs Classes as Immutable Pipeline Records

Use attrs to create fast, validated, immutable record types for data pipeline stage outputs.

python
import attr
from datetime import datetime
from typing import Optional

@attr.s(frozen=True, slots=True)
class SaleRecord:
    sale_id:    int         = attr.ib(validator=attr.validators.instance_of(int))
    customer:   str         = attr.ib(validator=attr.validators.min_len(1))
    amount:     float       = attr.ib(validator=attr.validators.gt(0))
    currency:   str         = attr.ib(default='USD')
    created_at: datetime    = attr.ib(factory=datetime.utcnow)
    notes:      Optional[str] = attr.ib(default=None)

record = SaleRecord(sale_id=1, customer='Acme Corp', amount=1250.0)
print(attr.asdict(record))

Use Cases

  • typed pipeline records
  • immutable domain models
  • data engineering

Tags

#attrs#data-modeling#immutable#pipeline

Related Snippets

Similar patterns you can reuse in the same workflow.

pythonbeginner

Dataclasses as Pipeline Data Models

Use Python dataclasses to define typed, immutable data models passed between pipeline stages.

Best for: typed pipeline stages

#dataclasses#typing
pythonadvanced

Python ETL Pipeline Example

Complete extract-transform-load pipeline with error handling, logging, and incremental processing.

Best for: Automating data ingestion from CSV to warehouse

#etl#pipeline
pythonintermediate

Retry Logic for Data Pipelines

Configurable retry decorator with exponential backoff and jitter for resilient data pipeline tasks.

Best for: Resilient API calls in data pipelines

#retry#resilience
pythonadvanced

Databricks Notebook Data Pipeline

Databricks notebook with Delta Lake reads, transformations, merge operations, and table optimization.

Best for: Medallion architecture data pipelines on Databricks

#databricks#delta-lake