Cloud Data Analytics – Our enterprise customers repeatedly question whether the cloud is really the right way to implement IoT and predictive analytics in industrial processes, production facilities and equipment. There are several important reasons for your choice and at the same time there are arguments:
The aim of the new plans is to make devices and sensors at the edge (enterprise systems) or in the fog (i.e. the enterprise network) smarter to enable communication not only to and from the cloud, but – where appropriate – between the devices themselves.
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So why not run apps like Predictive Maintenance directly on the device? Microcontrollers can be used, but they are limited in terms of memory and RAM. Such small processor sizes are used in Raspberry Pi, Onion Omega 3 or similar products that provide small and cost-effective IoT environments. These devices have important interfaces like WiFi, Bluetooth and USB, but their performance is quite low. For example, the Raspberry Pi Zero W has a 1 GHz CPU and 512 MB of memory at a price of $10 each.
Architecting A Successful Modern Data Analytics Platform In The Cloud
Algorithms need to be very “lightweight” to work on these systems, and often this is not possible.
So what is the largest unit of computer science? Today, SoC (system on chip) components can also be used. They have a very high energy density and require little space. They are inexpensive in terms of hardware costs, but must be designed specifically for use, which involves development costs.
Such systems are used for everything from mobile phones to cloud computing devices. Actions refer to 54 cores, 3 GHz, 512 GB RAM, 1 TB memory and 100 Gbps bandwidth, although with a price of around $ 100, they are still more expensive than microcontrollers and Raspberry Pi.
Of course, we can also install a server on a machine with up to 176 cores, 2 TB of RAM and 460 TB of memory. However, hardware costs will increase, which forces us to question the cost-benefit ratio of unit – device inspection. This of course depends on the price of the device itself, although in most cases it is not worth it.
Interesting Facts About Data Analytics & Cloud Data In 2022
Fog works with the cloud, while Edge by definition excludes the cloud. Fog works hierarchically, while Edge is limited to a small number of layers. In addition to computing, Fogi also deals in networking, storage, management and acceleration. An interesting and low-cost cloud middleware solution can be created with Fog Computing. With Fog, computing power is pushed to the edge of networks and works for a group of “things” between the edge and the cloud.
Fog is architecture first, software second and hardware component third. The approach is still quite new and only dates back to 2014. In 2015, the Open Fog Consortium was founded by some important exponents such as Cisco, Intel, Dell, Microsoft and Princeton University. The first concept graphic was published in February 2017 and can be seen here:
The post isn’t much done yet, and you have to work your way through 162 pages of paper. However, a review of the document is useful, and without going into more detail, the following Edge, Fog and Cloud collaborations are important to us:
A machine learning (ML) model is developed on a Data Science platform. This can be done offline or in the cloud. uses Dataiku as a platform for this purpose. The ML model is exported to a productive platform. Depending on the requirements, the ML model can run at the Edge, in the fog, or directly in the cloud. The task of training a best model is usually delivered in the cloud because this task is intensive and requires a large database. The model is periodically optimized in the cloud using new data, and this optimized model is periodically applied to Fog or Edge. In principle, the learning activity can also be done offline, depending on how often it needs to take place.
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Currently, the software landscape around Edge and Fog Computing is still very controlled and the range of services is sometimes very different. Cloud providers such as MS Azure IoT, Predix, AWS or IBM Watson released Edge / Fog applications in 2016/2017 (mostly as open source) to act as a link between Edge and Cloud. It provides functionality that allows the machine learning model to run on the Edge. Here are three examples:
When it comes to data preparation, data cleaning and Edge/Fog data processing, these solutions are also very complex for the system. The solutions cannot be used independently of the cloud solution. There are also software solutions that make Edge and Fog Computing possible independent of the cloud provider. Foghorn, a Silicon Valley startup, is a case in point.
Foghorn offers a very fast data preparation and execution engine, also known as the CEP Engine. CEP stands for Complex Event Processing and includes various methods of capturing real-time intelligence. CEP can run in Docker and directly on the Edge. In most cases, the software does not run on the endpoints of the systems, but on the physical gateways.
Physical doors have the advantage that the software still runs close to the plant, but at the same time a whole bunch of machines and plant sites can be managed. In terms of networking, the software still runs on an automated network, ie. e. separated from the cloud and data exchange takes place over the standard gateway connection. However, important data can easily come to the cloud from this point.
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Important field features and IoT device protocols: Profibus, Profinet, Modbus, MQTT, OPC-UA and more. Performance up to 2 GHz, 4 GB DDR3 RAM and 128 GB SSD. Physical separation of automation and cloud network for security reasons.
By comparison, this Dell Edge gateway has a slightly weaker 1.33GHz performance, 2GB DDR3 RAM, and 32GB SSD in the same price range around $1,000.
With 16 cores and 4 TB of disk space, which will allow you to run very good analysis solutions.
Future analytics solutions won’t just run on-device or in the cloud. Instead, the Fog architecture will become the appropriate solution.
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Here, the combined movements of physical gateways and cloud platforms are used, which is a cost-effective way to deploy analytics solutions close to the device or system while making the best use of resources.
And GradeSens performs predictive maintenance on Google’s new postcard, in Industry 4.0 Use Cases Integrating Physical Models in Machine Learning Half-Day Workshop Digital Retrofit 4.0 Edge computing is often the best option for predictive quality.
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Performance cookies are used to understand and analyze key website performance metrics, which helps provide a better user experience for visitors. This is part 2 of the data management blog series published in January This blog focuses on data management technology in . Along with a collaborative governance process and a dedicated team of people, implementing a successful data governance plan requires tools. From data archiving, storage audits and reporting, data discovery, trace generation to automation and alerts, multiple technologies are integrated to manage the data lifecycle. Google offers a collection of tools that enable organizations to manage their data securely, ensure governance and drive the democratization of data. These tools fall into the following categories: Data Protection Data Protection protects data from
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