Defining the importance of the database in edge computing

The idea of edge computing is straightforward. In order to be near the hardware, software, and people who create and use the data, computation and storage capabilities must be moved to the edge. In the current era of hyperconnectivity, demand for edge computing will continue to rise quickly, mirroring the rapid expansion of 5G infrastructure.

The demand for low-latency experiences is always increasing, driven by technologies like IoT, AI/ML, and AR/VR/MR. While decreasing latency, bandwidth costs, and network robustness are essential drivers, adherence to data protection and governance standards, which forbid the transfer of sensitive data to central cloud servers for processing, is another underrated but equally significant cause.

By processing data at the edge rather than at faraway cloud data centres, edge computing architecture optimises bandwidth use and lowers round-trip delay costs. As a result, end users benefit from apps that are always quick and always accessible.

Estimates indicate that the $4 billion global edge computing business in 2020 would grow quickly to a $18 billion market in only four years. Innovation at the edge will captivate the attention and finances of businesses as a result of digital transformation activities and the growth of IoT devices (more than 15 billion of which will link to organisational infrastructure by 2029, according to Gartner).

Therefore, it is crucial for businesses to comprehend the current status of edge computing, where it is going, and how to develop a future-proof edge strategy.

Simplifying management of distributed architectures

Early edge computing deployments consisted of customised hybrid clouds with on-premises servers hosting databases and applications that were supported by a cloud back end. Data was often transferred between on-premises servers and the cloud via a crude batch file transfer technique.

The operational expenditures (OpEx) of administering these distributed on-prem server installations at scale, in addition to the capital costs (CapEx), can be onerous. The batch file transfer system has the potential to cause edge apps and services to use outdated data. Then there are circumstances in which it is not feasible to host a server rack locally (due to space, power, or cooling limitations in off-shore oil rigs, construction sites, or even airplanes).

The next wave of edge computing deployments should make use of managed infrastructure-at-the-edge services provided by cloud providers to allay OpEx and CapEx worries. To mention a few prominent examples, managing distributed servers may be done with a great deal less operational cost thanks to AWS Outposts, AWS Local Zones, Azure Private MEC, and Google Distributed Cloud. Many on-premises locations can host storage and compute at these cloud-edge locations, which lowers infrastructure costs while maintaining low-latency data access. Moreover, managed private 5G networks using products like AWS Wavelength can be used for edge computing installations to take advantage of the high bandwidth and ultra-low latency capabilities of 5G access networks.

Every edge computing strategy must take the data platform into account because edge computing is all about distributing data storage and processing. If and how your database can meet the requirements of your distributed architecture will need to be determined.

Future-proofing edge strategies with an edge-ready database

Data processing and storage can take place at the client/device tier, cloud-edge locations, and central cloud data centres in a distributed architecture. In the latter scenario, the gadget can be a smartphone, a desktop computer, or specially adapted hardware. Each tier, from the cloud to the client, offers more assurances of service availability and responsiveness than the tier before it. With no reliance on network access, co-locating the database and the application on the device would ensure the highest level of availability and responsiveness.

The capacity to maintain data consistency and synchronisation across these many layers, subject to network availability, is a crucial feature of distributed databases. Data sync does not involve mass data transfer or data duplication among these scattered islands. It is the capacity to send at scale and in a way that is resistant to network outages only the pertinent subset of data. For instance, only shop-specific data may need to be transmitted downstream to store locations in the retail industry. However, in the case of healthcare, hospital data centres may just need to send aggregated (and anonymised) patient data upstream.

In a decentralized system, data governance challenges are made worse and must be a major factor in an edge strategy. The data platform, for instance, should be able to make it easier to enforce data retention guidelines at the device level.

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