async processing - IoT Digital Twin PLM

PaaS Integration: Leverages cloud-based Platform as a Service (PaaS) for scalable, managed asynchronous processing.
Event-Driven Architecture: Utilizes services like Azure Event Hubs with Kafka endpoints for efficient event ingestion and stream processing.
Maintenance Efficiency: Takes advantage of the reduced maintenance overhead offered by cloud PaaS solutions.
Dynamic Scalability: Harnesses the cloud’s ability to dynamically scale asynchronous processes according to the workload.
Managed Messaging Services: Employs managed queueing and pub/sub services such as Azure Service Bus for reliable message handling.

MQTT Broker: Uses the Ignition MQTT broker for robust, lightweight messaging in on-premise setups.
Direct Ingestion: Supports direct data ingestion from Ignition into on-premise systems for seamless data integration.
RabbitMQ and Kafka: Offers the flexibility to integrate RabbitMQ for message queuing or Kafka for high-throughput data streaming, based on specific system requirements.

Ignition: Utilizes a built-in redundancy model to ensure system resilience, enabling automatic failover in case of server failure.
Delta Lake: Enhances data resilience with ACID transactions and schema validation, maintaining data integrity.
Redis Cache: Improves system resilience by ensuring frequently accessed data is stored in a fast, accessible manner, reducing the impact of data spikes or failures.

IoT Hub: Provides secure, always-on communication channels between devices and the cloud, which is essential for maintaining a high level of service availability.
Traffic Manager: Increases availability by directing client requests to the nearest healthy datacenter and automating failover.
Event Hub and Service Bus: Ensures messaging systems are constantly available, handling millions of events per second and maintaining secure communications.
SQL DB: Offers built-in features that ensure databases remain available, optimizing for performance and security.

IoT Edge Gateway: Makes certain that IoT applications can be stored and run on multiple edge devices, allowing the system to scale out and manage device failures effectively.
Kubernetes: Scales IoT applications in a containerized environment, managing workloads dynamically to meet demand.
Databricks: Supports data task scalability with its auto-scaling cluster capabilities, adapting to varying loads for tasks like ETL and analytics.
Synapse Analytics: Delivers scalable data query execution, meeting high-performance analytical requirements on-demand or as scheduled.