Steps to Choose the Right Database for Your IoT Systems
If you want to deploy IoT technology for enterprise management and considering the right database solution, it may surely be not very clear. IoT solutions may ideally be distributed across various geographic regions. As opposed to the centralized cloud-based data management solutions, more sectors are adopting a combination of fog computing, edge computing, and cloud computing. For this, the database platforms should offer the needed flexibility to process data at the edge and synchronize it between cloud and edge services to render it effectively.
Another thing to notice is that based on the specific IoT use case you have, the capabilities you may want in an ideal database may range from data filtering to real-time data streaming and zero latency operations, high availability, optimum analytics, geo distributions, flexibility in the schema and so on. This article will walk through the different steps in choosing an apt database for IoT solutions.
Realize IOT electronics manufacturing to application services through AiPCBA’s PCB assembly service.
Identifying the data needs for IoTsolutions
IoT works efficiently by collecting and processing data from various sources of connected devices to enable actions. It may be like triggering a notification on computing analytics, extracting patterns from historical data,and more. On generic IoT solutions, you may consider sensors and actuators installed across the organization for the sake of this discussion. There can be thousands of such sensors and actuators connected through edge service rules, and IoT solutions will collect all the data from different sensors simultaneously, which is used for real-time decision making. That can be alerts and system monitors to identify any unusual activity and provide a historical view of the analytical data to the end uses for better decision making.
Before you decide on services under databases that go with IoT, it is necessary that you need to have clarity on what you will be doing with the data and where you will be deploying the same. Remote database services providers like RemoteDBA.com will be able to you in choosing the right databases for IoT systems. Some questions which will help you to understand and prioritize the database needs are us below:
- What type of data processing and decision making is deployed to IoT based services?
- Is there any cloud solution deployed at one region, or whether the same has been dispersed across the regions?
- What would be the volume of data transferred from the sensors and other devices to the ad server and from the ad server to the central server?
- What could be the peak volume of data transferred?-
- Whether the IoT solution is used to control activities and devices? If yes, is there any requirement for real-time responses?
- What business insights you want to derive from historical data?
Breakdown the IoTsolutions to independent services
This is also a crucial step in designing the components of IoT and software services where each component should be able to perform the specific task assigned to it independently. While breaking down the IoT solutions into independent services, you may find the IoT solution distributed geographically. Some components may be deployed on the edge network, and others may be centralized. Here are some specific real-time uses cases to consider while choosing databases for various IoT functionalities.
Data ingest– The purpose of data ingest is to collect data and store the logs and messages from different devices. For this, the database should support high speed write operations as these data may come in a burst, so it is essential to ensure that all such data is captured under any circumstances.
Edge analytics – Edge analytics aims to perform the data translation from aggregation, classification, filtering, and different other functions on the incoming data. Here, the database should support high-speed read and write operations with a latency of sub-milliseconds. There should be tools and commands to perform various analytical and complex computational needs on the given data.
Device manager – The purpose of the device manager is to communicate messages across the devices. In this case, the database needs to access the messages and deliver them to various devices with minimum latency and maximum speed.
IoT system-wide analytics –The objective here is to collect data from the edge service and do data transformation for analytical operations.The database should provide necessary commands to perform the analytical computations on the given data and store it as long as required by the analytics engine.
C&C dashboard– The purpose of the same is to deliver a visual representation of the current state of the IoT ecosystem. In this case, a database’s need is to maintain the data most accurately and up to date, and it should also be able to read data with minimal latency.
Business intelligence– The purpose is to run the reports, inferences, and queries from the historical data.The database should store the data for a longer time in a cost-effective manner by providing the tools to analyze and query the data.
IoT data stream outlet will help normalize the data to the simplest format and further post the same to actual subscribers. While considering the database needs, they should also perform the data transformation very efficiently and quickly by supporting publishing and subscribing features.
Grouping your services based on their data needs
The next important step in choosing the right database for IoT is to plot and categorize the services based on how long the data will be staying in the database and the speed of reading and writing operations by the given service. We can see that the data is constantly coming into IoT systems and also going out to the server, which ideally stays in the database for a very short period only. At the same time, they may also arrive in high volume and velocity sometimes.
So, we need to have a high-speed database with only very low latency to hold the data for the ingest services, business intelligence services, etc., which rely largely on historical data. To do this, you have to group the services that have similar data access and characteristics to limit the overall number of databases used for different services. This approach will also help reduce the operational overhead.
By figuring out all the needs mentioned above in light of your specific IoT database requirements, you will be able to shortlist your most ideal databases to be used for the same.