# DataOps

> Applies DevOps principles to data engineering for automated, tested, and monitored data pipelines.

- **Category**: Data Science
- **Subcategory**: Operations
- **Canonical URL**: https://designpattern.fyi/patterns/data-ops/

---

## Description
**Context**: Data pipelines need the same reliability and automation as software. DataOps brings CI/CD, testing, and monitoring to data engineering.


## Use Cases
Data teams requiring reliable, automated, and monitored data pipelines with rapid iteration capabilities.



## Implementation Example

```python
# DataOps Pattern class DataPipelineCI: def __init__(self): self.tests = []
def add_test(self, test): self.tests.append(test)
def run_ci(self, pipeline_code): # Run tests before deployment for test in self.tests: if not test.run(pipeline_code): raise Exception(f"Test failed: {test.name}")
# Deploy if tests pass self.deploy(pipeline_code)
def deploy(self, pipeline_code): # Deploy to production print("Deploying pipeline...")
```



## Trade-offs


### Advantages

- - Automated pipelines

- - Improved reliability

- - Faster iteration

- - Better testing




### Considerations & Drawbacks

- - Cultural change required

- - Tooling complexity

- - Learning curve

- - Initial setup overhead







---
**Reference**: [Original Source](https://www.dataopsmanifesto.org/)