Edge computing is a somewhat overloaded term to describe locality-sensitive distributed computing architecture. Wikipedia defines it as “a distributed computing paradigm that brings computation and data storage closer to the sources of data.” We can think of the “sources of data” being users or even sensors making requests to our system.
The main aim of edge computing or multi-access edge computing is to reduce location-related latency in applications to enable high-performance, real-time use cases in widespread geographies. Edge computing systems are faster when computation and data are closer to the devices.
Developers today are beginning to realize that to get computation data closer to devices, there are better choices than centralized databases and even distributed databases with limited distribution to a single region.
Operating a database in a single region leads to high latency for users or edge devices in areas outside of the database region. Even if you distribute your application across multiple regions, users or devices outside the database region may experience unacceptable response times. And unexpectedly high latency can translate into dissatisfied users.
Edge computing uses data that has life cycles or life spans. In the same application, you can have ephemeral data, in-memory data, short-term persistent data, and long-term persistent data. Typically, long-term persistent data is stored in databases. Unfortunately, when LTP data is far from the edge, it’s slower, and this slow data effect tends to give databases a lousy reputation.
Choosing the right database solution can improve your edge computing architecture and user experience.