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Mobile and IoT devices at the Edge drive data creation and produce insights for enterprises at an accelerated pace. IDC predicts that the pattern is set to continue, as there will be 55.9 billion connected devices by 2025. It estimates that 79.4ZB of data will be generated just by connected IoT devices by that year.
Enterprises like yours need a mechanism to manage data more effectively across platforms, from the Edge to the cloud and back, to benefit from the expanding volumes of data fully. The good news is data fabric essentially serves as the plumbing and translation for data traveling onto and off various platforms.
However, that is not it. You also require tools to extract, clean, comprehend, process, and analyze data. The process is critical as data volumes at the network’s Edge continue to climb. It must be done before sending the data to both humans and automated downstream and upstream customers. And for that, you need technology.
The development of three crucial areas—data management, integration, and analytics at the Edge—will be a major factor in the solutions to these needs. But first, let us understand the basics of the concept:
What is Edge data fabric?
A data fabric is a framework that accesses distributed data openly and conveniently, in real-time, in a single data layer, and under the same management. Enterprise operators may move and access data across various deployment platforms, data processes, geographic locations, and structural methods using it.
A data fabric includes data centers, the public cloud, private clouds, and the various gateways and Edge devices that operate there. The architecture can fully integrate different data pipelines and cloud settings using automated technologies.
Cloud computing and Edge: What is the connection?
Edge computing involves placing computing resources, such as small computers or micro data centers, closer to the sources of information generation to reduce network latency and bandwidth usage, which are common issues in cloud computing.
This approach ensures that service and operation can continue even when cloud connections are intermittent. Many industries, including manufacturing and healthcare, are interested in creating real-time control systems that use ML and AI to improve efficiency and reduce costs.
What is the need for data fabric solutions?
The exponential increase of big data in a little over 12 years has been fueled by advancements in Edge computing, Artificial Intelligence (AI), hybrid cloud, and IoT, adding to the complexity faced by businesses in managing the data.
The increasing data volumes have also brought several challenges, including data silos, security threats, and general decision-making bottlenecks. The unification and regulation of data environments have become increasingly important.
Using data fabric solutions, you can tackle these problems head-on. Your business can leverage them to integrate governance, reinforce security and strengthen privacy controls.
The data integration initiatives using data fabrics enable more comprehensive, data-centric decision-making. In the past, an enterprise might have had to use various data platforms matched to particular business lines.
However, as of today, data fabric enables decision-makers to view data more coherently to comprehend the customer lifecycle better and create linkages between data not previously possible. In a nutshell, data fabrics drive digital transformation and automation across enterprises by bridging these understanding gaps of customers, goods, and processes.
Introducing Edge fabric data management systems
IoT systems are highly vulnerable to unauthorized exposure because they are automated and incorporated with default IP addresses and other configurations. Data fabrics can assist in filtering a large amount of data streaming in real time before getting discharged to the network.
The intelligent fabric solution only ensures accurate data when used with an Edge system, whether the data is at rest or in transit. As a result, the developers’ workload at the application development and integration level is further reduced.
So an Edge fabric data management system generates a durable, coordinated, and governable data stream for local and distributed consumption. Such a solution provides interoperability and real-time processing. While an Edge system faces an increasing amount of data requirements for various settings, a fabric should carry out the following tasks:
- Working with all relevant protocols and APIs, including the REST API
- Seamless operations across various POSIX-compliant environments
- Data streaming using a variety of standards, including Kafka, Spark, and others
- Establishing connectivity between databases, including those using JDBC and ODBC
- Access to numerous no-fail interfaces, including radio networks, MTTP, HTTP, and others
Essential layers of the edge data fabric framework
Different businesses have varying requirements, so there is not just one data architecture for a data fabric. There are numerous cloud service providers and data infrastructure deployments to which the data fabric adapts.
However, businesses utilizing this kind of data framework show characteristics common to a data fabric across their architectural designs. These six layers comprise the following:
- The edge data management layer is in charge of data security and governance.
- The data ingestion layer finds connections between organized and unstructured data, which combines cloud data.
- Data processing guarantees only pertinent data surfaces for data extraction; the data is refined in the data processing layer.
- Data orchestration makes the data usable for teams across the organization. It is a crucial layer that transforms, integrates, and cleans the data.
- The data discovery layer reveals fresh possibilities for combining various data sources - for instance - methods to link data from a customer relationship management system and a supply chain data mart.
- The data access layer permits data consumption and provides the proper permissions for specific teams to adhere to legal requirements. Through dashboards and other data visualization tools, the layer also aids in surfacing pertinent data.
Building smart IoT systems with the power of AI
IoT applications and deployments are becoming dependent on AI. You could see a rapid increase in venture capital funding for incorporating AI into enterprise products. Large IoT platform software suppliers are also fast providing integrated AI features, including Machine Learning-based analytics.
Due to its capacity to quickly extract insights from data, AI has taken center stage in IoT. It helps automatically spot patterns and abnormalities in the data that smart sensors and gadgets provide. Information on temperature, pressure, humidity, air quality, vibration, and sound can be deduced from it.
You will discover that ML can have substantial advantages over traditional Business Intelligence tools for evaluating IoT data. It can generate operational predictions up to 20 times early and more accurately than threshold-based monitoring systems.
Additionally, AI tools like speech recognition and computer vision can help analyze data that formerly needed human evaluation.
You can avoid unplanned downtime, improve operational efficiency, enable new products and services, and enhance risk management by utilizing the potent mix of AI and IoT technologies. The following are some advantages of the IoT:
1. Adjustments on the fly
Data can be produced and analyzed to find points of failure, allowing the system to be adjusted as necessary.
2. Greater operational effectiveness
IoT devices with AI integration may analyze data to identify trends and insights and change system behavior to increase efficiency.
3. AI data analytics decisions
Businesses can save money by spending less time monitoring IoT devices.
4. Greater scalability
You can optimize current processes or add new capabilities by increasing the number of devices connected to an IoT system.
Features of cloud Edge fabric
By 2025, 200+ zettabytes of data will be in cloud storage across the globe. That is explosive growth, and the sky is literally the limit. Just five years ago, the Edge data growth was under 10% and now look at it! To deploy Edge data fabric, you must set up a data management platform that delivers on security, interoperability, and governance. Let us study key features of cloud Edge fabric:
- Scalable information management is achieved via cloud Edge fabric, and so is unstructured data, such as IoT.
- Both technical and non-technical customers use data fabrics. Compared to conventional warehousing methods, cloud Edge fabric demands less IT input.
- Cloud Edge fabric quickly produces insights.
- No matter where data is located within the company, consolidating data governance and security is the primary goal of adopting the cloud Edge fabric.
- To enhance data use, link the system with new data sources, analytical models, user interfaces, and automation scripts.
- Hybrid hosting environments can use cloud Edge fabric.
- Because of its architecture, businesses may conduct essential tasks, such as adding new features through extensions and security layers and performing other crucial tasks without restructuring the central database.
AWS IoT for Edge computing
AWS is the most reliable cloud provider, with an extensive global infrastructure footprint that includes managed hardware in locations outside of AWS data centers, such as metro areas, 5G networks, on-premises sites, and ruggedized devices.
Additionally, AWS offers a wide range of Edge capabilities specifically designed for use cases such as IoT, hybrid cloud, 5G, and industrial machine learning, with 200+ integrated device services to choose from. These capabilities can be quickly deployed and easily scaled to billions of devices with the help of AWS Partners.
AWS Edge services allow you to deploy data processing, analysis, and storage capabilities near your endpoints, enabling the creation of high-performance applications with ultra-low latency and real-time responsiveness.
AWS IoT for the Edge can be used to build applications deployed both in the cloud and at the Edge, providing consistency across the two environments. With AWS, you can also maintain security and compliance from the Edge to the cloud, using encryption and access control to protect data that must be stored and processed on-premises or at the Edge.
Microsoft Edge for cloud computing
Microsoft product groups coined the term “The Intelligent Edge” to refer to the capability that allows Microsoft customers to access seamless experiences and compute capabilities regardless of where their data is located, whether in the cloud or offline.
Microsoft has open-sourced the Azure IoT Edge Runtime to support the development of applications that use edge technology, allowing customers to customize and modify the runtime for their specific needs. This makes it easier for developers to build applications that leverage edge technology.
The new Microsoft Edge program helps you browse the web, search, shop online, and more. Like other modern browsers, it stores specific data on your device, such as cookies, and sends information to Microsoft, such as browsing history, to improve and personalize your experience.
Microsoft Edge is designed to protect your privacy and give you control over what data is collected and stored. Additionally, it collects a set of required diagnostic data to ensure security, update, and performance.
Microsoft follows the principle of “information collection minimization,” meaning it only collects the data necessary to provide a service or for analysis and only stores it for as long as needed.
You also have the option to control whether optional diagnostic data related to your device is shared with Microsoft for troubleshooting and to improve their products and services.
Drivers for adoption of Edge data fabric
Getting value from an Edge data fabric securely is one of the most important goals for businesses. Here are other motivators for Edge data fabric adoption:
- Edge data helps with data security while in storage and transit.
- To get the total value from the data, OT and IT must work together. They must support not just the actual data but its type and validity.
- The Edge devices cannot hold data for long, and network resources are limited, which means greater security.
- It enables data consumption well beyond OT or IT teams.
A data management platform capable of meeting such requirements can be employed across the layers of devices, gateways, complicated equipment, and Edge computing platforms.
Combining intelligent IoT with Edge fabric for minimal risk
The fact that data at the network's periphery is frequently isolated and has yet to be fully mined does not imply it is not connected to mission-critical systems.
IoT quickly ties together shipping systems, medical equipment, communications networks, POS systems, chemical processing facilities, and factory floors. Data is moving to the Edge to reduce risk and necessitate the implementation of a data fabric.
How Edge computing helps with the faster creation of IoT applications
IoT device data must be processed at the Edge rather than traveling back to a central site. Edge computing provides local processing and storage for the cloud computational requirements of IoT devices. The data must be located on the Edge device for local usage by an application. IoT and Edge, when combined, can be beneficial in the following ways:
- Boost in the network bandwidth.
- Enhanced operational efficiency and quicker response times.
- Using analytics algorithms and ML, local data processing, aggregation, and quick decision-making is possible.
- When a network connection is lost, the system keeps running offline.
- Less latency due to the connection between IoT devices and corporate IT networks
An IoT gateway can transfer information from the Edge back to the cloud, a centralized data center, or to local processing of the Edge systems. In some cases, applications may need to store data on the Edge device to keep it private and allow for local usage by the application.
In such situations, datasets related to products and services will need to be transferred from the cloud to the Edge device, reversing the typical flow of data from the Edge to the cloud. This will depend on the specific product and the customer’s desire to protect their data.
Building smart IoT solutions for the Edge since 2008
Intuz, an enterprise-class IoT development company, helps small and mid-size enterprises build, manage, and scale their operations by building IoT-connected systems for various sectors. We offer a range of services, including IoT consulting, proof of concept development, industrial IoT solutions, firmware development, IoT mobile, web apps. Whether it is edge fabric or cloud computing - we can consult you on all technical aspects possible. Just fill out this short contact form, and one of our team members will reach out to you asap!