# Data Engineer — Code Examples

## Example 1

```python
# Data Pipeline Implementation
from airflow import DAG
from datetime import datetime, timedelta

# Pipeline configuration
pipeline_config = {
    "source": "raw_data",
    "destination": "processed_data",
    "processing_steps": [...]
}

# ETL Pipeline
class DataPipeline:
    def extract(self):
        """Extract data from source systems"""
        pass
    
    def transform(self):
        """Apply business logic transformations"""
        pass
    
    def load(self):
        """Load data to destination"""
        pass
    
    def validate(self):
        """Validate data quality"""
        pass

# Spark job example
def process_large_dataset(spark, input_path, output_path):
    df = spark.read.parquet(input_path)
    
    # Transformations
    processed_df = df.transform(clean_data) \
                    .transform(enrich_data) \
                    .transform(aggregate_metrics)
    
    # Write results
    processed_df.write.mode("overwrite").parquet(output_path)

# Data quality checks
quality_checks = {
    "completeness": check_null_values,
    "uniqueness": check_duplicates,
    "validity": check_data_ranges,
    "consistency": check_referential_integrity
}
```