The Role of the Database in Edge Computing
The goal of edge computing is to allocate data processing and storage. A next-generation, edge-ready database is required to keep data consistent and in sync across the cloud, edge, and client tiers.
The concept of edge computing is straightforward. It is advantageous to bring computing and storage capabilities to the edge, close to the devices, applications, and users who generate and consume the data. Edge computing demand will continue to rise in the current hyperconnected era, addressing the needs of the rapid growth of 5G infrastructure.
The demand for low-latency experiences is increasingly driven by IoT, AI/ML, and AR/VR/MR technologies. While decreasing latency, bandwidth costs, and network resiliency are important drivers, another understated, but equally important reason, is adherence to data privacy and governance policies, which prohibit the transfer of sensitive data to central cloud servers for processing.
Edge computing architecture optimizes bandwidth use and reduces round-trip latency costs by processing data at the edge rather than in distant cloud data centers. As a result, end users benefit from always-on, always-accessible applications.
The global edge computing market is predicted to grow to $18 billion in just four years, from $4 billion in 2020. So, it is critical for businesses to understand the current state of edge computing, where it is headed, and how to develop a future-proof edge strategy. A distributed database and data sync solution is often the cornerstone of an effective edge computing solution for businesses.
Streamlining Distributed Architecture Management
Early edge computing deployments included customized hybrid clouds with on-premises servers hosting databases and applications and a cloud back end. A crude batch file transfer system was typically used to transfer data between on-premises servers and the cloud.
In addition to the capital costs (CapEx), the operational costs (OpEx) of managing these distributed on-premises server installations at scale can be onerous. The batch file transfer system has the potential to use outdated data in edge apps and services. Then there are cases where hosting a server rack locally is not an option.
The next generation of edge computing deployments should make use of managed infrastructure-at-the-edge services provided by cloud providers to help alleviate OpEx and CapEx worries. To name a few prominent examples, managing distributed servers can be done with less operational overhead thanks to AWS Outposts, AWS Local Zones, Azure Private MEC, and Google Distributed Cloud. Several on-premises locations can host storage and computing at these cloud-edge locations, which lowers infrastructure costs while maintaining low-latency data access. Additionally, managed private 5G networks using products like AWS Wavelength can be used for edge computing deployments to take advantage of the high bandwidth and ultra-low latency capabilities of 5G access networks.
Because edge computing is all about distributing data storage and processing, every edge computing strategy must consider the data platform. It must be determined whether and how your database can meet the requirements of your distributed architecture.
Edge-ready Databases for Future-proofing Edge Strategies
Data storage and processing can take place across multiple tiers in a distributed architecture. This includes central cloud data centers, cloud-edge locations, and client/device tiers. The device in the latter case could be a mobile phone, a desktop system, or custom-embedded hardware. Each tier, from cloud to client, provides greater guarantees of service availability and responsiveness than the previous tier. Co-locating the database and the application on the device would ensure maximum availability and responsiveness without relying on network connectivity.
The ability to keep data consistent and in sync across these various tiers, subject to network availability, is a critical aspect of distributed databases. Data synchronization is not about bulk data transfer or duplication.
It is the ability to transfer only the relevant subset of data at scale while remaining resilient to network outages. For example, only store-specific data may need to be transferred downstream to store locations in the retail industry. In healthcare, for example, only aggregated (and anonymized) patient data may be required to be transmitted upstream from hospital data centers.
Data governance issues are increased in a distributed environment and must be a key consideration in an edge strategy. For example, the data platform should be capable of facilitating the implementation of data retention policies all the way down to the device level.
Reach out to Molnii to find out more about edge computing.