feat: Implement Step 10-11 - Statistics and Notification Services

Step 10: Data Analytics and Statistics Service
- Created comprehensive statistics service with real-time metrics collection
- Implemented time-series data storage interface (InfluxDB compatible)
- Added data aggregation and analytics endpoints
- Integrated Redis caching for performance optimization
- Made Kafka connection optional for resilience

Step 11: Real-time Notification System
- Built multi-channel notification service (Email, SMS, Push, In-App)
- Implemented priority-based queue management with Redis
- Created template engine for dynamic notifications
- Added user preference management for personalized notifications
- Integrated WebSocket server for real-time updates
- Fixed pymongo/motor compatibility issues (motor 3.5.1)

Testing:
- Created comprehensive test suites for both services
- Added integration test script to verify cross-service communication
- All services passing health checks and functional tests

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

services/statistics/backend/main.py

@@ -53,14 +53,18 @@ async def lifespan(app: FastAPI):
         await cache_manager.connect()
         logger.info("Connected to Redis cache")
         
-        # Initialize Metrics Collector
-        metrics_collector = MetricsCollector(
-            kafka_bootstrap_servers=os.getenv("KAFKA_BOOTSTRAP_SERVERS", "kafka:9092"),
-            ts_db=ts_db,
-            cache=cache_manager
-        )
-        await metrics_collector.start()
-        logger.info("Metrics collector started")
+        # Initialize Metrics Collector (optional Kafka connection)
+        try:
+            metrics_collector = MetricsCollector(
+                kafka_bootstrap_servers=os.getenv("KAFKA_BOOTSTRAP_SERVERS", "kafka:9092"),
+                ts_db=ts_db,
+                cache=cache_manager
+            )
+            await metrics_collector.start()
+            logger.info("Metrics collector started")
+        except Exception as e:
+            logger.warning(f"Metrics collector failed to start (Kafka not available): {e}")
+            metrics_collector = None
         
         # Initialize Data Aggregator
         data_aggregator = DataAggregator(