I'm an AWS-certified Lead Technologist with 10.8+ years of hands-on expertise designing and delivering enterprise-grade software across FinTech, Healthcare, Aerospace, and E-Commerce domains. My core craft is Java & Spring Boot microservices — building event-driven, cloud-native systems that handle millions of transactions reliably.
At IBM, I architected a platform processing 50M+ baggage events per day at 99.99% uptime across international airports. At Cygnet Infotech, I lead the technical strategy for a PCI/GDPR-compliant payments platform and mentor a team of 12+ engineers on distributed systems and cloud-native patterns.
I'm equally passionate about AI integration — actively exploring LangChain4j, Spring AI, RAG architectures, and MCP for building intelligent Java applications.
The Pattern: If the AI service fails repeatedly, the "Circuit" opens.
The Fallback: The system stops trying to reach the AI and instead routes the alert to a "Manual Review" queue for a human engineer. The telemetry ingestion continues unaffected—nothing is lost, only the AI "intelligence" is temporarily paused.
version: '3.8'
services:
# MQTT Broker (Edge Gateway)
mosquitto:
image: eclipse-mosquitto:latest
ports: [ "1883:1883" ]
volumes: [ "./mosquitto.conf:/mosquitto/config/mosquitto.conf" ]
# Apache Kafka (KRaft mode - Resilience & Buffering)
kafka:
image: bitnami/kafka:latest
ports: [ "9092:9092" ]
environment:
- KAFKA_CFG_NODE_ID=0
- KAFKA_CFG_PROCESS_ROLES=controller,broker
- KAFKA_CFG_LISTENERS=PLAINTEXT://:9092,CONTROLLER://:9093
- KAFKA_CFG_CONTROLLER_QUORUM_VOTERS=0@kafka:9093
# InfluxDB 2.0 (Time-Series Database for historical analysis)
influxdb:
image: influxdb:2.0
ports: [ "8086:8086" ]
environment:
- DOCKER_INFLUXDB_INIT_MODE=setup
- DOCKER_INFLUXDB_INIT_USERNAME=admin
- DOCKER_INFLUXDB_INIT_PASSWORD=adminpassword
- DOCKER_INFLUXDB_INIT_ORG=aerospace
- DOCKER_INFLUXDB_INIT_BUCKET=telemetry
@Configuration
public class MqttIngestionConfig {
private final KafkaTemplate<String, String> kafkaTemplate;
public MqttIngestionConfig(KafkaTemplate<String, String> kafkaTemplate) {
this.kafkaTemplate = kafkaTemplate;
}
@Bean
public MessageChannel mqttInputChannel() { return new DirectChannel(); }
@Bean
public MqttPahoMessageDrivenChannelAdapter inbound() {
MqttPahoMessageDrivenChannelAdapter adapter =
new MqttPahoMessageDrivenChannelAdapter("tcp://localhost:1883", "ingest-client", "aircraft/+/telemetry");
adapter.setConverter(new DefaultPahoMessageConverter());
adapter.setQos(1); // At Least Once delivery guarantee
adapter.setOutputChannel(mqttInputChannel());
return adapter;
}
@Bean
@ServiceActivator(inputChannel = "mqttInputChannel")
public MessageHandler handler() {
return message -> {
String topic = message.getHeaders().get("mqtt_receivedTopic").toString();
String payload = message.getPayload().toString();
// Extract aircraft ID (e.g., aircraft/Boeing777-123/telemetry)
String aircraftId = topic.split("/")[1];
// Critical Buffering: Send to Kafka
kafkaTemplate.send("flight-telemetry", aircraftId, payload);
System.out.println("Queued in Kafka: " + aircraftId + " -> " + payload);
};
}
}
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: telemetry-ingest
namespace: aviation-data
spec:
replicas: 2 # Resilience: Active-Active Deployment
template:
metadata:
annotations:
# Critical line: Automatically injects the Istio network proxy
sidecar.istio.io/inject: "true"
spec:
containers:
- name: telemetry-ingest
image: your-registry/telemetry-ingest:latest
ports:
- containerPort: 8080
# mtls-policy.yaml
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
name: default-strict-mtls
namespace: aviation-data
spec:
mtls:
mode: STRICT # Zero Trust: Plain-text is prohibited
// Establish the relationship tree: Aircraft has Engines, which have Sensors
CREATE (a:Aircraft {id: 'B777-123'})
CREATE (e:Engine {id: 'ENG-1', position: 'Left'})
CREATE (s:Sensor {id: 'TEMP-01', type: 'Temperature'})
CREATE (a)-[:HAS_PART]->(e)
CREATE (e)-[:HAS_SENSOR]->(s)
@AiService
public interface MaintenanceAssistant {
@SystemMessage({
"You are an expert aviation maintenance AI for commercial aircraft.",
"Analyze the sensor telemetry alert and cross-reference it with standard maintenance procedures.",
"Provide a concise, 3-step action plan for the ground crew."
})
String analyzeAnomaly(@UserMessage String alertDescription);
}
import org.axonframework.modelling.command.TargetAggregateIdentifier;
// The Intent: A request to register an anomaly (The Write Model)
public record RegisterAnomalyCommand(
@TargetAggregateIdentifier String anomalyId,
String aircraftId,
String sensorId,
String aiActionPlan
) {}
// The Fact: A factual, immutable record that history cannot erase (Audit Trail)
public record AnomalyRegisteredEvent(
String anomalyId,
String aircraftId,
String sensorId,
String aiActionPlan
) {}
import org.axonframework.commandhandling.CommandHandler;
import org.axonframework.modelling.command.AggregateLifecycle;
import org.axonframework.spring.stereotype.Aggregate;
@Aggregate
public class MaintenanceAggregate {
// Other fields (anomalyId, etc.)
@CommandHandler // Handles the incoming Write request
public MaintenanceAggregate(RegisterAnomalyCommand command) {
// Validation logic here (if necessary)
// If valid, apply (publish) the immutable event to the Axon Event Store
AggregateLifecycle.apply(new AnomalyRegisteredEvent(
command.anomalyId(), command.aircraftId(), command.sensorId(), command.aiActionPlan()
));
}
}
import org.axonframework.eventhandling.EventHandler;
import org.springframework.stereotype.Component;
@Component
public class MaintenanceProjection {
// Inject a standard JPA Repository here for dashboard queries
@EventHandler // Listens to the immutable event, updates the dashboard view
public void on(AnomalyRegisteredEvent event) {
System.out.println("Updating Dashboard View for Anomaly: " + event.anomalyId());
// repository.save(new MaintenanceDashboardRecord(event.anomalyId(), ...));
}
}
- Scalability: Growing with the Fleet
The system scales horizontally at every layer.
The "Shock Absorber" (Kafka): Kafka is the heart of the scalability. If the number of aircraft suddenly doubles, you don't need to scale your databases immediately. Kafka absorbs the "spike" in data, holding it in a high-speed buffer until your downstream services can process it.
Microservice Elasticity: On OpenShift, we can set "Horizontal Pod Autoscalers" (HPA). If the Ingestion Service hits 80% CPU because of a massive data dump, OpenShift automatically spins up 5 more instances of that service to handle the load.
Independent Scaling: Since we used CQRS, you can scale the Read Side (Dashboards) to handle thousands of users without adding any load to the Write Side (AI Analysis/Event Store). - Resilience: What if a service goes down?
In a traditional monolithic system, if the database is down, the whole app crashes. In this event-driven architecture, the system is fault-tolerant.
Scenario A: The Ingestion Service Crashes
The Impact: Data stops moving from MQTT to Kafka.
The Recovery: The Edge Gateway on the aircraft or the MQTT Broker (with persistence enabled) will keep the messages. Meanwhile, OpenShift detects the service is down via "Liveness Probes" and automatically restarts the pod. Once it's back up, it resumes pulling data exactly where it left off.
Scenario B: Kafka is Down
The Impact: This is the most critical failure.
The Recovery: In production, Kafka is never a single server; it's a High-Availability (HA) Cluster. If one "Broker" node dies, the other two take over immediately. Data is replicated across multiple disks, so no telemetry is lost.
Scenario C: The AI / Maintenance Service is Down
The Impact: Telemetry is stored in the database, but no "Action Plans" are being generated.
The Recovery: This is where the beauty of Kafka shines. The data waits in the Kafka Topic. When the AI service recovers (or is redeployed), it sees its "offset" (last read position) and processes the backlog. The ground crew might see a slight delay, but no anomaly is ever missed.
Scenario D: The Neo4j or Event Store is Down
The Impact: We cannot store new "Action Plans."
The Recovery: We implement Retries with Exponential Backoff and Dead Letter Queues (DLQ). If the service can't write to the database, it moves the message to a "Retry Topic" in Kafka. It keeps trying until the database is back online. - The "Black Box" Recovery (Event Sourcing)
The ultimate resilience feature is Event Sourcing. If your "Read Database" (the one powering the dashboard) gets corrupted or deleted:
1. You spin up a fresh database.
2. You "Replay" the events from the Axon Event Store.
3. The system reconstructs the entire state of every aircraft from day one.
This is effectively a "Time Machine" for your data.