The key to the success of any IoT application is the efficacy of its data and data processing. The NoSQL database solutions available now might be in the best position to manage a massive volume of data. Among the data storage models, the key-value store model reigns supreme over the others in terms of IoT.
We are now in the middle of a data revolution. Many new technologies are pumping out a wide range of database solutions. However, in many cases, the joke is about the new names of the same old ideas (like the old ASP and the new SaaS). In other terms, you can say that true transformation brings new solutions to the market, which are very few even though the boom we see is vast.
A perfect example of a really innovative solution is the Internet of Things or IoT. A Gartner research shows the fact that IoT was a $546 billion market in 2016 in terms of end-point spending and about 6 billion connected devices. That was just the beginning; now, in 2020, the market is estimated to be 1.5 trillion dollars with about 20 billion-plus connected devices.
IoT use case
People may argue that IoT is not a new thing. However, if you explore the past, you may find many devices that were interconnected. Take a gas pump, for example. Automated gas pumps are there for a long and are counted as intelligent connected devices. With reliable hardware and robust software running inside those machines, you can fill your vehicle tank and pay it for quickly.
With much more complex hardware and software inside these machines, they can effectively control and coordinate various processes like dispending gasoline, billing it, and doing online marketing by offering codes and discount coupons. All these processes have evolved significantly. The self-service online payment system pumps started offering services back in the 90s. Since their first generation, these systems have evolved largely. The three V’s of big data as Variety, Volume, and Velocity go back to this first generation of online pumps.
Gas pumps remained as a single system that deals with well-structured and defined data. So, in real-time use, it lacked two out of the three V’s: volume and variety. Despite the inherent complexity of deploying many POS terminals on every street, this is something more refined over many years to find and tune these components and applications. All these challenges are effectively addressed by now. The major challenge left today with such systems is the volatile oil prices, which change more than once a day, making a low qualification of these systems for the V of velocity.
The IoT challenges are of a different category. It is about processing unstructured data in huge volumes. So, it has to deal with ‘variety ideally.’ It is pushed by geographically distributed devices and tends to generate a significant amount of data in every nanosecond. Monetizing this data is way beyond simple Electronic Transfer Funds (ETF) transactions, which require real-time analysis to support business decisions. Such research cannot be performed locally, and therefore, this generation of IoT strongly supports cloud services, which work as concentrators to capture and store these immense data streams.
How do Key-Value stores work?
Nowadays, all types of databases are used by information technology companies. As we can see, relational database models are so popular and can make it possible to connect information easily. There are other types of databases, which provide some add-on advantages and may remain better in different situations. There are also NoSQL, document-oriented, column-oriented, and graph-based databases available. Alon with these exists Key-Value databases too.
At the basic, Key-Value DBs are housed on a table with simple columns. The first column is the value, and the second contains the key. This key is a distinct mark of identification. The value can take various forms like simple strings of integers or numbers, but sometimes complex objects may also come up as database values. The position or function of value can also occupy a document. References of files may also be inserted into the databases.
Values in a given database need not have to be uniform. It is also possible to accommodate various objects in the same database column. It applies to the identification marks too. In many cases, the key may presumably follow a unique pattern; however, it is not necessary to be so in principle. The integers and strings can also be constructed based on arbitrary criteria. Usually, Key-Value stores may appear as in-memory databases, i.e., being in the working memory and on-disk solutions.
Advantages of Key-Value DBS
The notable advantages of Key-Value DBs are high performance and scalability. Both these benefits stem from the simple structure of the Key-Value model. As the Key-Value stores need not have stipulated any of the uniform patterns, changes to the given database can be made during the operation. You can introduce a new field while an action is being run on the other entries simultaneously.
Also, high speed makes it possible for this database to establish a quick and straightforward connection between the key and value. If you have to retrieve data, you may access the value through the specific key. The data is made available directly. However, this can sometimes be a disadvantage of a Key-Value Store as there is no other access method available for the same. Relational databases may permit the running of very complex queries, and the content of those databases can also be sifted through in different aspects. On the other hand, the Key-Value store will only let you access the information via the key.
To conclude, the applications of Key-Value Stores must be made by considering both their advantages and existing restrictions. It can be used effectively where quick access time is needed for a vast data set. For large websites, key-value databases may always create new entries by deleting the old ones, which they are well adapted to. You may also analyze your objectives, and the database needs to go for key-value stores on finding it to be apt.
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