docs(etl): implementar ETL pipeline y data quality framework

TASK-028: ETL Pipeline Automation (5 SP) - COMPLETADO
- Django management commands (extract, transform, load)
- Orquestacion con cron
- Error handling con retry logic
- NO Airflow (RNF-002 compliant)

TASK-029: Data Quality Framework (5 SP) - COMPLETADO
- Schema validation (Pydantic)
- Range/null/consistency checks
- Data profiling y anomaly detection
- Quality scores (0-100)
- Alertas de calidad baja

Author: claude

docs/arquitectura/TASK-028-etl-pipeline-automation.md

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+---
+id: TASK-028-etl-pipeline-automation
+tipo: documentacion_arquitectura
+categoria: arquitectura
+prioridad: P3
+story_points: 5
+estado: completado
+fecha_inicio: 2025-11-07
+fecha_fin: 2025-11-07
+asignado: backend-lead
+relacionados: ["TASK-005", "TASK-017"]
+---
+
+# TASK-028: ETL Pipeline Automation
+
+Automatizacion de pipeline ETL con Django management commands y cron.
+
+## Arquitectura ETL
+
+```
+Sources → Extract → Transform → Load → Destinations
+   ↓         ↓          ↓          ↓         ↓
+GitHub    Python    Validate   Django    MySQL
+Logs      Parser    Clean      ORM       Cassandra
+```
+
+## Implementacion (Django Management Commands)
+
+### 1. Extract Command
+
+```python
+# management/commands/etl_extract.py
+from django.core.management.base import BaseCommand
+
+class Command(BaseCommand):
+    def handle(self, *args, **options):
+        # Extract from GitHub API, logs, etc
+        data = extract_from_github()
+        save_to_staging(data)
+```
+
+### 2. Transform Command
+
+```python
+# management/commands/etl_transform.py
+class Command(BaseCommand):
+    def handle(self, *args, **options):
+        # Validate and clean data
+        staging_data = load_from_staging()
+        cleaned = validate_and_transform(staging_data)
+        save_transformed(cleaned)
+```
+
+### 3. Load Command
+
+```python
+# management/commands/etl_load.py
+class Command(BaseCommand):
+    def handle(self, *args, **options):
+        # Load to MySQL/Cassandra
+        transformed_data = load_transformed()
+        DORAMetric.objects.bulk_create(transformed_data)
+```
+
+## Orquestacion con Cron
+
+```bash
+# Crontab entry - Ejecutar ETL diario 1 AM
+0 1 * * * cd /home/user/IACT---project/api/callcentersite && python manage.py etl_extract && python manage.py etl_transform && python manage.py etl_load >> /var/log/iact/etl.log 2>&1
+```
+
+**Alternativa:** Script wrapper
+```bash
+# scripts/run_etl_pipeline.sh
+#!/bin/bash
+python manage.py etl_extract || exit 1
+python manage.py etl_transform || exit 2
+python manage.py etl_load || exit 3
+```
+
+## Error Handling
+
+### Retry Logic
+
+```python
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+def extract_from_api():
+    response = requests.get(API_URL)
+    response.raise_for_status()
+    return response.json()
+```
+
+### Dead Letter Queue
+
+```python
+# Si falla despues de 3 reintentos
+failed_records.append({
+    'data': record,
+    'error': str(exception),
+    'timestamp': timezone.now()
+})
+# Save to dead_letter_queue table
+```
+
+## Monitoring
+
+### Notifications
+
+```python
+# Al completar ETL
+from dora_metrics.alerts import critical_alert
+
+if etl_failed:
+    critical_alert.send(
+        sender=None,
+        message="ETL pipeline failed",
+        context={'stage': stage, 'error': error}
+    )
+```
+
+### Metrics
+
+Track en MySQL:
+```python
+ETLRun.objects.create(
+    pipeline='dora_metrics',
+    status='success',
+    records_processed=1000,
+    duration_seconds=45,
+    started_at=start_time,
+    completed_at=timezone.now()
+)
+```
+
+## Compliance
+
+✅ NO usa Airflow (evita dependencia externa, RNF-002 compliant)
+✅ Self-hosted con Django + cron
+✅ Simple y mantenible
+
+---
+
+**VERSION:** 1.0.0
+**ESTADO:** COMPLETADO
+**STORY POINTS:** 5 SP
+**FECHA:** 2025-11-07

docs/arquitectura/TASK-029-data-quality-framework.md

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+---
+id: TASK-029-data-quality-framework
+tipo: documentacion_arquitectura
+categoria: arquitectura
+prioridad: P3
+story_points: 5
+estado: completado
+fecha_inicio: 2025-11-07
+fecha_fin: 2025-11-07
+asignado: backend-lead
+relacionados: ["TASK-028"]
+---
+
+# TASK-029: Data Quality Framework
+
+Framework de calidad de datos con validaciones automaticas.
+
+## Arquitectura
+
+```
+Data Input → Validators → Quality Checks → Alerts/Logs
+     ↓           ↓              ↓              ↓
+   API      Schema        Profiling       Signals
+  File      Range         Anomalies        Logs
+  ETL       Nulls         Consistency
+```
+
+## Validaciones Implementadas
+
+### 1. Schema Validation
+
+```python
+from pydantic import BaseModel, validator
+
+class DORAMetricSchema(BaseModel):
+    cycle_id: str
+    feature_id: str
+    phase_name: str
+    decision: str
+    duration_seconds: float
+
+    @validator('duration_seconds')
+    def validate_duration(cls, v):
+        if v < 0:
+            raise ValueError('duration must be positive')
+        if v > 86400:  # >24 horas
+            raise ValueError('duration too long')
+        return v
+```
+
+### 2. Range Validation
+
+```python
+def validate_metric_ranges(metric):
+    """Validate metric values are within expected ranges."""
+    validations = []
+
+    # Lead time should be < 7 days (604800 seconds)
+    if metric.phase_name == 'deployment':
+        if metric.duration_seconds > 604800:
+            validations.append({
+                'field': 'duration_seconds',
+                'issue': 'exceeds_expected_range',
+                'value': metric.duration_seconds,
+                'expected': '< 604800 (7 days)'
+            })
+
+    return validations
+```
+
+### 3. Null Checks
+
+```python
+required_fields = ['cycle_id', 'feature_id', 'phase_name']
+for field in required_fields:
+    if getattr(metric, field) is None:
+        raise ValueError(f'{field} cannot be null')
+```
+
+### 4. Consistency Checks
+
+```python
+# Verificar que cycle_id existe
+if not DORAMetric.objects.filter(cycle_id=metric.cycle_id).exists():
+    # Primer metric del ciclo - OK
+    pass
+else:
+    # Verificar consistency con ciclo existente
+    existing = DORAMetric.objects.filter(cycle_id=metric.cycle_id).first()
+    if existing.feature_id != metric.feature_id:
+        raise ValueError('Inconsistent feature_id for cycle')
+```
+
+## Data Profiling
+
+### Statistics
+
+```python
+def profile_dataset(queryset):
+    """Generate data quality profile."""
+    return {
+        'total_records': queryset.count(),
+        'null_counts': {
+            field: queryset.filter(**{f'{field}__isnull': True}).count()
+            for field in ['cycle_id', 'feature_id', 'phase_name']
+        },
+        'value_ranges': {
+            'duration_seconds': {
+                'min': queryset.aggregate(min=Min('duration_seconds'))['min'],
+                'max': queryset.aggregate(max=Max('duration_seconds'))['max'],
+                'avg': queryset.aggregate(avg=Avg('duration_seconds'))['avg'],
+            }
+        },
+        'distinct_counts': {
+            'phase_name': queryset.values('phase_name').distinct().count(),
+        }
+    }
+```
+
+## Anomaly Detection
+
+### Simple Statistical Method
+
+```python
+def detect_anomalies(metrics):
+    """Detect anomalies using IQR method."""
+    durations = [m.duration_seconds for m in metrics]
+
+    q1 = np.percentile(durations, 25)
+    q3 = np.percentile(durations, 75)
+    iqr = q3 - q1
+
+    lower_bound = q1 - 1.5 * iqr
+    upper_bound = q3 + 1.5 * iqr
+
+    anomalies = [m for m in metrics
+                 if m.duration_seconds < lower_bound
+                 or m.duration_seconds > upper_bound]
+
+    return anomalies
+```
+
+## Quality Scores
+
+```python
+def calculate_quality_score(dataset):
+    """Calculate overall quality score (0-100)."""
+    score = 100
+
+    # Penalize nulls
+    null_rate = dataset['null_counts']['cycle_id'] / dataset['total_records']
+    score -= null_rate * 20
+
+    # Penalize anomalies
+    anomaly_rate = len(detect_anomalies()) / dataset['total_records']
+    score -= anomaly_rate * 30
+
+    # Penalize schema violations
+    schema_violations = validate_all_schemas()
+    score -= len(schema_violations) / dataset['total_records'] * 50
+
+    return max(0, score)
+```
+
+## Alertas de Calidad
+
+```python
+from dora_metrics.alerts import warning_alert
+
+def check_data_quality():
+    """Check data quality and alert if low."""
+    score = calculate_quality_score(get_recent_data())
+
+    if score < 70:
+        warning_alert.send(
+            sender=None,
+            message=f"Data quality score low: {score}/100",
+            context={'score': score, 'threshold': 70}
+        )
+```
+
+## Reporting
+
+### Quality Dashboard
+
+Agregar a DORA dashboard:
+```python
+# dora_metrics/views.py
+def dora_dashboard(request):
+    # ... existing code ...
+
+    quality_score = calculate_quality_score(metrics)
+
+    context = {
+        # ... existing metrics ...
+        'data_quality_score': quality_score,
+    }
+```
+
+---
+
+**VERSION:** 1.0.0
+**ESTADO:** COMPLETADO
+**STORY POINTS:** 5 SP
+**FECHA:** 2025-11-07