Based on the changes in data production and data forms in the Internet of Things era, and the evolution trend from Cloud-Native to IoT-Oriented (IoT-Oriented) architecture , EMQ officially proposed "Data Infrastructure for Internet of Things Architecture" (Data Infrastructure for IoT ) architecture paradigm.
From application priority to data priority, data-centric fusion of IoT data and traditional enterprise business data to realize unified real-time data " connection, movement, storage, processing, and analysis " on the cloud, edge, and end, and realize a closed loop from data generation to data realization. Shorten the cycle of data realization, reduce the cost of data realization, and help enterprises build "future-oriented" key business applications of the Internet of Things.
Connection – elastic, reliable and multi-protocol, connecting massive IoT devices
Data producers in IoT scenarios mainly come from various devices. Establishing equipment-to-equipment, equipment-to-system connections is a prerequisite for realizing the value of IoT data. The "data infrastructure for the Internet of Things architecture" built by EMQ through a series of product portfolios provides a one-stop connection capability integrating cloud and edge , which will help break the data islands faced by traditional scenario-oriented application construction and realize data-oriented application construction , providing a basic guarantee for mining data value.
1. The cloud-native biological networking protocol has a large number of concurrent connections
EMQ provides an elastic scaling, safe and reliable device access layer solution based on cloud-native architecture . Through the concurrent connection capability of tens of millions of IoT devices in the cloud, it provides a safe and stable connection for massive IoT devices to the cloud.
2. One-stop access to cross-industry protocol cloud
With the application of Internet of Things technology to the industry, it has become the development trend of the Industrial Internet of Things to establish data connections between industry equipment and new Internet of Things devices , and to open up existing industry data and new business data channels. Through EMQ "data infrastructure for the Internet of Things architecture", users can obtain access to industry standard protocols such as GB/T32960 or enterprise private protocols, extend the connection capabilities to various industry devices, and realize new types of Internet of Things devices Unified access to data and industry equipment.
3. Deepen the integrated connection of cloud, edge and terminal at the end of the industry
With the development of edge computing and distributed cloud architecture, enterprises not only need to provide strong connection capabilities in the cloud, but also need to be able to penetrate into industry terminal equipment , because industries such as industry and electric power are limited by the network or connection methods and cannot directly access the cloud. The equipment provides an end-to-end connection solution. EMQ can provide users with the last-stop equipment access capability for equipment access, and solve the connection problems of traditional industrial equipment and industrial equipment .
Mobile – real-time messaging engine, two-way data movement and distribution
After the connection between the device and the system is established, the two-way movement capability, movement speed and data reliability of real-time data determine the system's ability to consume data, which in turn affects the value that data can generate for the enterprise.
1. Massive data throughput in the cloud
As the source of data production changes to equipment, both the number of equipment and the frequency of data production have increased unprecedentedly. Data throughput determines the basic capability level of an IoT system. Through the cloud distributed high-performance cluster architecture, it can provide millions of throughput per second routing capabilities and low-latency data delivery capabilities , and provide high-throughput data bridging solutions to connect various business systems and data persistence systems. IoT data is flexibly integrated into Kafka, SQL, NoSQL and time series databases to achieve rapid application integration and business innovation
2. Stable and reliable two-way mobility from device to cloud
Based on standard MQTT messages, it provides various QoS message quality guarantees . Regardless of whether it is converging from the device to the edge, uploading from the edge to the cloud, or sending control commands from the cloud to the device, it can ensure that the data arrives accurately.
3. Cloud-edge-end data connection and edge data mobility autonomy
After the Internet of Things system device connection is extended to the edge side of industry and other industries, it can not only realize the aggregation of industrial and other industry data on the cloud, but also realize the data flow between edge side devices and applications, forming edge data autonomy.
Storage – low-latency, dynamically scalable cloud-native streaming data storage
According to the changing trend of the first paradigm, the data form of the Internet of Things is mainly real-time data flow . After the data is connected to the cloud, high-throughput streaming data requires a highly available, high-performance, and flexible storage solution to provide data persistence.
1. Cloud-native elastic expansion storage
Cloud-Native architecture is adopted to support independent horizontal expansion, cluster online expansion and dynamic expansion ; through optimized storage engine design, data is copied to multiple storage nodes to provide low-latency, highly reliable streaming data persistent storage services.
2. Flexible data storage model
The Schema-free data storage method is adopted to facilitate the flexible definition and storage of IoT scene data , and to meet the persistence of data formats of various types of devices.
3. Separation of computing layer and storage layer
Hierarchical storage is supported, and historical data can be automatically transferred to low-cost storage services such as object storage and distributed file storage, and the capacity can be expanded infinitely . While ensuring the computing performance of streaming data, it also ensures the high availability of data storage.
Processing – one-stop real-time processing of multi-level data with cloud-side collaboration
In the IoT scenario where real-time data is transmitted, whether it is in the cloud or at the edge, fast data processing, fast filtering, and fast integration are required to ensure the low-latency characteristics of the overall system . At the same time, efficient management of complex data processing is also required.
1. Real-time processing and integration of massive data in the cloud
Through the rule engine and stream processing based on SQL statements, one-stop IoT data extraction, filtering, conversion, storage and processing can be realized without writing code , ensuring fast processing of real-time data. And can connect data to Kafka, SQL, NoSQL and time series databases to achieve rapid application integration and business innovation
2. Local low-latency and fast processing at the edge
On the edge side, EMQ provides a lightweight flow data processing and rule engine , which can locally process and offload real-time data in small edge computing nodes such as industrial gateways and vehicles to ensure system responsiveness in low-latency scenarios.
3. Unified management of cloud-side data processing
The cloud-side collaboration capability provided by EMQ can provide unified remote rule delivery, remote algorithm update and other capabilities for scattered data processing nodes at the edge in the cloud . It is convenient for managers to configure and manage in a unified manner, improve management efficiency, and reduce local operation and maintenance costs.
Analysis – real-time data analysis and business insight, immediate decision-making
The ever-increasing scale and continuous high-speed generation of streaming data pose serious challenges to existing data systems and applications, especially how to perform low-latency analysis on continuously changing data streams. Through the EMQ " Data Infrastructure for the Internet of Things Architecture " provides real-time analysis solutions based on materialized views through streaming databases, supports complex query and analysis operations on continuously generated Internet of Things data streams, senses data changes in real time, and leverages data value and make business decisions instantly.
1. Real-time data insights
The real-time analysis capability provided by EMQ enables real-time analysis of data when it enters the system, supports complex query and analysis operations on the continuously generated IoT data stream, enables users to obtain real-time data insights, quickly responds to market changes, and improves business agility and secure a competitive advantage.
2. Risk analysis and early warning
The prevention and control of risks and failures often requires powerful real-time data analysis capabilities, which can provide timely warnings before risks occur . Whether it is predictive maintenance on the production line, real-time fraud detection in financial transactions, or even the prevention and control of epidemic diseases, fast data analysis and processing capabilities can be achieved through EMQ "data infrastructure for IoT architecture".
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