The Internet of Things, or, as we like to call it, IoT, is a massive phenomenon which purposefully binds and gives shape to an environment of interconnected devices and networks. However, ever since the emergence of this cloud-like concept, an uninterrupted series of do’s and don’ts have appeared, and most of them are connected to data storage, data evaluation, data transfer and everything else connected to data itself.
All the virtual information gathered within the IoT needs to be analyzed in a time-effective manner, so as to be able to make timely decisions. Solve for Interesting rep Alistair Croll states that the most difficult challenge in terms of virtual information management on the IoT is to correlate the collected data with every possible user action and interaction.
Think about it. We are used to transferring all sorts of information from one device to another in a matter of seconds. In the light of Croll’s statement, the following question ensues: What if data from hundreds or thousands of sensors needs to be mixed with data from hours, days or even months ago to make the right decision at the right moment?
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Here are a couple of tips and tricks on creating the right data systems for the new world of IoT, as provided by Silicon Angle.
Keep Control of Everything
The IoT landscape incorporates a host of different end points. This means that, in some cases, it can comprise millions, or even billions of devices or apps, while in other cases, it can be a constant stream of information provided from a smaller group of devices. In either case, you will be needing a datastore that has the capacity to keep up with a constant stream of info, without discarding any of the data.
Systems that cannot keep up with data receiving usually lose discarded information. In order to prevent that, your data store needs to feature memory. One streaming option is Apache Spark, a memory-optimized distributed processing framework that can stream and structure data on the fly. There are other streaming alternatives that can, in addition, be coupled with an in-memory database for longer term persistence.
Save and Serve Data At The Same Time
An effective IoT datastore is generally defined by the ability to easily multitask between saving data and simultaneously serving data. Serving data mainly implies responding directly to end user requests or end device requirements, but it also serves the function of providing comprehensive analytics reports. These are assessed by teams of analysts, and larger teams can be difficult to support as a constant and unpredictable query assault can overload classic systems.
To ensure the integrity of the database, it is necessary to implement locking mechanisms that can make the database “take a break” while completing a prior request. An environment of interconnected devices needs to work and rely on flexible systems. A solution in this matter consists in using a memory-optimized database to collapse functionality into a single system.
Always Be Online
The application infrastructure which supports all IoT devices needs to remain online on a permanent basis. This includes high-availability and disaster recovery abilities, as well as the option to make changes to the data store without blocking incoming data.
Apart from that, it also includes the ability to expand the capacity of the datastore, add new features of update it without taking it offline. For this, you will be needing a solid team of architects that can develop a complex and viable solution. One option suggested by Silicon Angle is to use Apache Zookeeper together with other tools where Zookeeper provides distributed processes to coordinate availability. Select in-memory databases also provide online operations directly for automatic scaling and high availability, which also simplify deployment.
