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How to Choose the Most Appropriate Databases for Your IoT systems?

The Internet of Things (IoT) is everywhere these days, and most of us depend on it to work every day. In fact, the Internet of Things is so embedded in our everyday lives that we often forget how many systems we depend on to work in order for our technology to do what we want. So how do you know which IoT database is best for your application?

Deciding which database to use for an IoT system isn’t always as simple as it may seem. It’s not just a matter of choosing the best-looking option on the shelf, but rather, it’s about selecting the best option for your specific application. While there are dozens of options to choose from, the most popular database systems for IoT apps fall into two categories: NoSQL and SQL.

Choosing the right database system for IoT applications is not an easy task. Large-scale IoT solutions are spread across multiple geographic locations. Compared to cloud-based systems, more and more solutions use a combination of fog edge and cloud computing to improve performance. Therefore, the database platforms you choose should have the flexibility to process data at the edge and synchronize efficiently between the cloud and servers at the edge.

In addition, depending on your IoT usage scenarios, the enterprise database may contain the type of functionality you need, such as

  • Real-time data flow
  • Filtering data
  • Merging of data
  • Virtually zero latency for read and write operations
  • Immediate analysis
  • High availability
  • Flexibility of the scheme, etc.

This article explains the different steps to follow to select the most appropriate databases for your IoT solutions.


Define your data needs

IoT solutions rely heavily on collecting data from various connected devices and processing it to make intelligent situational decisions, such as. For example, initiating specific actions, retrieving real-time analytics, using historical data to identify patterns, etc.

In a typical IoT solution, you may have actuators and sensors installed in different locations. There can be thousands of these devices connected through an edge server. Over time, IoT solutions will collect data from all these sensors, analyze it, and make real-time decisions to control actuators, alert system monitors to unusual activity, and provide analytics to end users based on historical data.

Before choosing IoT services and databases, you need to be clear about what you want to do with the data and where you want to store it. Here are some important questions you can ask yourself to better understand it.

  • What decision making and data processing methods are delegated to the edge servers?
  • Is your cloud solution deployed in one location or is it spread across multiple locations?
  • How much data is transferred from the devices to your edge server and from the edge server to the central server?
  • How much do you estimate the peak volume will be?
  • Does your IoT solution control connected devices or actuators?
  • What information do you need when analyzing historical data?

Step #2

Break down your IoT solution into separate software services

Secondly, software components and services must be developed that can perform different tasks independently. When you divide IoT solutions into independent services, you should also get design specifications for your IoT solutions. If the IoT solution is geographically distributed, with some components deployed at the border and the rest at a central location, you need to separate the services for each location. You can rely on services for reliable IoT systems database management consulting.

Step #3

We can subdivide the IoT architecture into different services to facilitate the analysis of their responsibilities and data needs. It is summarized below.

  • Data management to collect and store messages from connected devices and support high-speed write requests, as data may arrive in batches, and to ensure that data is not lost in unusual circumstances.
  • Edge analysis will perform data-related tasks such as classification, translation, aggregation, filtering and other functions on incoming data. He is also responsible for real-time decisions at the border. It will support high-speed write and read operations with negligible latency and provide the commands and tools needed to perform more complex data calculations.
  • Device Manager to send messages to devices. This allows you to easily and quickly access messages and deliver them to your devices with minimal delay.
  • System-level analysis also allows for data generation on edge servers and data transformation and analysis. It provides commands to perform analytical calculations on given data and store it for the long time that analytical systems require.
  • C&C dashboard for a visual representation of the current state of a given IoT ecosystem. This keeps the data accurate and current, and allows it to be read with a delay of only a submillisecond.
  • With Business Intelligence, you can create easy-to-understand reports and extract information from historical data. It provides cost-effective data storage for a longer period of time and offers more tools for data analysis and retrieval.
  • With IoT Data Stream Outlet, data can be normalized into a standard format and sent to subscribers. It can perform data conversions and supports subscription and publishing functions.

Step #4

Group microservices according to the data requirements of each microservice and select an appropriate database

The next step is to choose the right IoT database based on your data needs for each service. You will notice that the data comes in faster through the data ingestion server and stays in the database for a limited time. At the same time, more data will come in at higher speeds on a larger scale. Therefore, we need a fast database with low latency to store the data to be recorded. However, business intelligence services can rely heavily on historical data. To simplify the task, services with similar characteristics and data needs can be grouped together to reduce the number of databases to be considered. This reduces operating costs even further.

Final step

Cost and efficiency estimates

Once you have ranked the databases according to your needs, you can evaluate the cost and efficiency of the databases you are considering. Keep in mind that the increasing complexity of the IoT application stack further increases operational and overhead costs. The total cost of the database depends on many parameters. The cost of the database itself is only a small part of the actual cost. This may include database licensing costs, infrastructure costs, data loss costs, data recovery costs, etc. The need for automation and provisioning, failover, provisioning, segregation, scaling, backup and recovery can drive up the cost of ownership.

Once these steps are efficiently completed, you can get the right databases for your next generation IoT solutions.

This source has been very much helpful in doing our research. Read more about best database for telemetry and let us know what you think.

Frequently Asked Questions

Which database is best for IoT data?

The Internet of Things (IoT) is the network of physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these objects to connect and exchange data. Each thing is uniquely identifiable through its embedded computing system but is able to inter-operate within the existing Internet infrastructure. Experts estimate that the IoT will consist of about 30 billion objects by 2020. The Internet of Things is a promising technology that can be used to improve many business processes such as inventory management, supply chain management, and customer relationship management. This technology can also be used to support smart cities, and smart grids, and can improve efficiencies in energy usage. In this article, I have discussed about Databases are a key component of software applications. They allow us to store and retrieve all kinds of data and information, making our lives easier. Depending on the application we are trying to build, we will need one kind of database or another. For instance, if we are building a web-based application, we can use a database like MySQL or PostgreSQL. If on the other hand we are building a mobile application, we might need to use a different database, like SQLite.

What database does IoT use?

As the name suggests, the Internet of Things (IoT) is a network of devices that can interact with one another over the Internet. These devices can be anything, from our phones, to our washing machines, and even coffee makers. IoT is the next step in technological evolution, and it has the potential to change our lives, and the world, forever. The first step in understanding how IoT works is by understanding how it is different from the Internet we all use today, from a technological perspective. (Link to relevant article about how IoT works) The Internet of Things has exploded in recent years, with the technical advances made in this field now allowing for the development of a vast array of smart devices. However, one of the biggest issues facing this rapidly growing technology is the considerable volume of data being created, which cannot be stored on a central server. Thus, every single device requires its own unique database; a large scale IoT deployment will require hundreds of thousands of diverse databases.

What types of databases commonly used for IoT integration of machines?

In recent years, the integration of machines has increased with the rise of the Internet of Things. Industries as diverse as healthcare, agriculture, and retail have all been impacted by it. There are many different types of databases commonly used for the integration of machines. As a matter of fact, there are many databases that are often used in combination. I’m going to go over the top three databases for the Internet of Things. IoT(Internet of Things) is an amazing technology which is being used in many fields and applications. One of the most commonly used applications for IoT is integration of machines. There are many types of databases which are used for integrating machines, as listed below:

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