About the first stage of the Industry 4.0, we must collect data from all devices.
So you will need to build up IoT solution. IoT applications can be described as Things (or devices), sending data or events that are used to generate Insights, which are used to generate Actions to help improve a business or process.
We can look Microsoft solution at the Industry 4.0
Another, you must the data flow characteristics on the IoT, The data is quickly and large. The data flow characteristics is…
- Streaming Data
- Real Time
At the core, an IoT application consists of the following subsystems
- Devices (and/or on-premise edge gateways) that can securely register with the cloud, and connectivity options for sending and receiving data with the cloud
- A cloud gateway service, or hub, to securely accept that data and provide device management capabilities
- Stream processors that consume that data integration with business processes and place the data into storage
- A user interface to visualize telemetry data and facilitate device management
So I use these PaaS service to build our IoT Solution at my company
- Azure Function
- Azure Blob Storage
- Azure IoT Hub
- Azure Stream Analytics
Another, if you can use PaaS to build up your solution, you should have priority to choose PaaS
Azure IoT Hub
IoT Hub is a managed service, hosted in the cloud, that acts as a central message hub for bi-directional communication between your IoT application and the devices it manages. Devices can be connected directly or indirectly via a field gateway (IoT edge device). Both devices and field gateways may implement edge intelligence including analytics capabilities. This enables aggregation and reduction of raw telemetry data before transport to the backend, and local decision-making capability on the edge
IoT Hub is a starting point for IoT solution. IoT hub also includes Twins function, and you can command your device from cloud to edge. At the same time, IoT hub is like a queue, and it can keep data in its service bus
Azure Stream Analytics
Azure Stream Analytics is a real-time analytics and complex event-processing engine that is designed to analyze and process high volumes of fast streaming data from multiple sources simultaneously. Patterns and relationships can be identified in information extracted from a number of input sources including devices, sensors, clickstreams, social media feeds, and applications.
Data streams are event data generated by sensors or other sources that can be analyzed by another technology. Analyzing a data stream is typically done to measure the state change of a component or to capture information on an area of interest
- Continuously analyze data to detect issues and understand or respond to them.
- Understand component or system behavior under various conditions to fuel further enhancements of said component or system.
- Trigger specific actions when certain thresholds are identified.
Microsoft Azure Stream Analytics is an event processing engine. It enables the consumption and analysis of high volumes of streaming data generated by sensors, devices, or applications. Stream Analytics processes the data in real time. A typical event processing pipeline built on top of Stream Analytics consists of the following four components:
- Event consumer
- Event Producer
- Event Ingestion System
- Stream Analytics Engine
Another, Azure Stream Analytics also is central in the Industry 4.0. Azure Stream Analytics is a PaaS service that integrates with your applications and Internet of Things (IoT) to gain insights with streaming data or static data held in a blob store. The process of consuming data streams, analyzing them, and deriving actionable insights out of them is called event processing. It requires an event producer, an event processor, and an event consumer. Azure Stream Analytics provides the event processing aspect to streaming that’s fully managed and highly reliable.
What is serverless compute?
Serverless compute can be thought of as a function as a service (FaaS), or a microservice that is hosted on a cloud platform. Our business logic runs as functions and you don’t have to manually provision or scale infrastructure. The cloud provider manages infrastructure. Our app is automatically scaled out or down depending on load. Azure has several ways to build this sort of architecture. The two most common approaches are Azure Logic Apps and Azure Functions, which we focus on in this module.
The same time , Azure function also is like joint. Functions is a great solution for processing data, integrating systems, working with the internet-of-things (IoT), and building simple APIs and microservices. For our industry 4.0 , we use azure function to connect every services and data can be circulated.
Azure Data Lake
Azure Data Lake Storage Gen1 is an enterprise-wide hyper-scale repository for big data analytics workloads. Data Lake Storage Gen1 can be accessed from Hadoop (available with HDInsight cluster) using the WebHDFS-compatible REST APIs. It’s designed to enable analytics on the stored data and is tuned for performance for data analytics scenarios.Data Lake is a big data pool. You can save any type of data. If you want to build the ML solution, you can put these data into ML service or using sparks to analysis data. When you collect data and the data format is not confirm.
Although the cloud is powerful, sometimes you don’t only use the cloud, but also you must use edge solution. In the cloud world, the Ideal is better than coding. Industry 4.0 core is integrated thinking