Edge computing Why Edge Computing only works with the right database

A guest post by Gregor Bauer* 3 min Reading Time

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The decentralized processing of data using Edge Computing is the logical evolution of Cloud Computing. The distributed Edge architecture gives central importance to data management - putting the focus on the database.

Peripheral databases must be quickly and easily adaptable to constantly changing conditions and environments due to the often evolving requirements.(Image: public domain /  Pixabay)
Peripheral databases must be quickly and easily adaptable to constantly changing conditions and environments due to the often evolving requirements.
(Image: public domain / Pixabay)

While the cloud has indeed revolutionized the IT landscape, it exhibits inherent weaknesses in certain scenarios, mainly due to its centralized cloud architecture. All data must first be transferred to one or more, often distant, data centers and then relayed back. In an office or home office setting, this is usually not critical. The latencies inevitably caused by the data travel time do not play a significant role there. However, the situation is different for real-time applications, which are typical in the Internet of Things (IoT) and the Industrial Internet of Things (IIoT). Sensors in digitalized production chains, as well as applications in online traffic control, medical uses, or predictive maintenance, rely on real-time data processing. For an autonomous vehicle, any delay in receiving updated data from the data center, even if only brief, could render it leaderless and dangerous. This highlights the limitations of centralized cloud architectures in scenarios where immediate data processing and response are crucial.

More efficiency and security through Edge Computing

Edge Computing is indeed the fundamental solution to these problems. Besides the reduced latency times due to its distributed, decentralized structure, which makes such applications feasible in the first place, there are also cost and security advantages. Companies can save on expensive network connections, and sensitive, potentially business-critical data does not need to be transferred to an anonymous cloud. Moreover, the requirements for network bandwidth decrease, and the limiting dependency on constantly available online connections is eliminated. Additionally, the distributed data center structure increases fault tolerance and reliability.

This advancement in Edge Computing is not achieved at the cost of high additional effort; in fact, quite the opposite. On the hardware side, Edge Computing is relatively modest in its demands: Mini data centers and gateways at the edge handle the collection and processing of peripheral data and then connect to a private or public cloud. The most important capacities additionally required in Edge Computing are the sensory equipment of the components, the analytical capabilities to evaluate the peripheral data, and the control and regulation possibilities based on this data. This places data management at the center of importance. For this, a database is needed that reflects the decentralized architecture of Edge Computing and supports its unique features.

The central role of the database

The first question concerns the type of database: Pure cloud databases are already ruled out for the reasons mentioned earlier. This leaves relational and NoSQL databases. The database architecture needed for Edge Computing, broadly speaking, consists of a central full-format database and many local databases. Some of these function as server databases, while others act as functionally reduced embedded databases in a light version for peripheral small devices. These database offshoots do not need to be highly performant or highly scalable, nor do they need to have sophisticated functions such as full-text search.

The peripheral databases, however, need to be rapidly and easily adaptable to the often-changing requirements and constantly evolving conditions and environments. This brings us to the data models. Relational SQL databases work with fixed table structures. Changes to this row-column schema are, if at all, only possible with significant effort. On the other hand, NoSQL databases possess a much more flexible data model based on JSON documents. Changes can thus be implemented much faster and with less effort.

Replication and synchronization

For databases suitable for Edge Computing, replication and synchronization capabilities are paramount. The distributed structure of Edge architectures necessitates constant replication of data between the central database and the peripheral embedded databases to synchronize them. This does not need to happen in real-time, but data traffic should be minimized to keep network and database loads as low as possible. Continuously replicating entire database contents would be counterproductive. Therefore, the ability to replicate only selected data from independent database clusters is crucial.   Additionally, the embedded databases should have their own analytics functions for data processing. This offers two advantages. First, it minimizes latency times, and second, it reduces data traffic between the central and peripheral databases. This approach optimizes the efficiency of the Edge Computing system, ensuring that data processing is as fast and resource-efficient as possible.

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No Edge-capable database, no Edge Computing. This may sound simple at first glance, but it underscores the importance of the database as a central data management entity for the decentralized Edge architecture. Therefore, the functionality, availability, and security of Edge installations depend crucially on choosing the right database.