Selection of the right database systems for your IoT applications is a challenging task. Large-scale IoT solutions are distributed across various geographical locations. Compared to the cloud-based systems, more solutions are now adopting a mix of fog computing at the edge and cloud computing for better performance. So, the database platforms you choose must offer the needed flexibility to process your data at the edge and effectively synchronize between the cloud and the edge servers.
Next, based on your IoT use cases, the abilities you want in your enterprise database may include, but not limited to:
- Real-time data streaming
- Data filtering
- Data aggregation
- Near to zero latency in read and write operations
- Instant analytics
- High availability
- Flexibility in the schema and so on.
This article will discuss different steps in choosing the most appropriate databases for your Internet of Things solutions.
Identify your data needs
IoT solutions largely depend on collecting data from various connected devices and processing it to make intelligent situational decisions like triggering some action, getting real-time analytics, using historical data to derive patterns, and so on.
In typical IoT solutions, you may have actuators and sensors which will be installed across the locations. There may be thousands of such units connected through the edge server. IoT solutions will collect data from all these sensors throughout the time, analyze and make real-time decisions for controlling the actuators and alert the system monitors on any unusual activities and offer analytics based on the historical data to end users.
Before you decide on IoT services and databases to go with, you must have clarity on what you will do with the data and where you store it. Some key questions to ask to understand it better are:
- What type of decision-making and data processing methods are delegated to edge servers?
- Is your cloud solution deployed in a single location or dispersed across various locations?
- What is the data volume transferred from the devices to your edge server and from the edge server to the central server?
- What is the peak volume you estimate?
- Whether your IoT solution control the connected devices or actuators?
- What insights do you need to derive through historical data analysis?
Split your IoT solution into standalone software services
Next, you have to design the components and software services that can perform different tasks independently. While breaking down the IoT solutions into independent services, one should also get the design specifications for your IoT solutions. If the IoT solution is geographically distributed where some components are deployed on edge and remaining are at the centralized location, you have to split the services for individual locations. For reliable consulting for IoT system database management, you can rely on services offered by RemoteDBA.com.
We may further break up the IoT architecture into various services to make it easier to analyze their responsibilities and data needs. Here is an outline of the same.
- Data Ingest to collect and store the messages from the connected devices and to support high-speed write queries as data may arrive in bursts, and ensure data is not lost under any unusual circumstances.
- Edge Analytics will help perform data-related tasks like classification, translation, aggregation, filtering, and other incoming data functions. It is also responsible for real-time decision-making on edge. It will support the high-speed writes and reads with negligible latency and provide the needed commands and tools to perform more complex computations on data.
- Device Manager to communicate the messages to devices. It will help to easily and quickly access messages and deliver them to the devices with minimal latency.
- System-wide Analytics will also help data form the edge servers and do the data transformation and analytical operations. It will provide commands to run analytical computations on given data and store it for a longer period as needed by the analytics engines.
- C&C Dashboard to deliver the visual representation of the current state of a given IoT ecosystem. It helps maintain the data accurately and updated as well as read data with only sub-millisecond latency.
- Business Intelligence will help to run easily understandable reports and derive insights from historical data. It can store data cost-effectively for a longer period and offer more tools to analyze and query data.
- IoT Data Stream Outlet will help normalize data to a standard format and also push them to subscribers. It can perform operations related to data transformation and support subscription and publishing capabilities.
Group the micro-services in light of data needs by each and choose the apt database
The next step in choosing the apt IoT database is to choose the apt databases based on your data needs for each service. You will see that data is coming and going into the database at higher speeds through the Data Ingest Server and stays in the DB for a limited period. Along with this, at the same time, more data will come in at higher velocity in increased volume. So, we need to have a high-speed DB with only low latency to hold data for ingestion. Business intelligence services, however, may rely largely on historical data. To make it easier, you can group the services with the same data characteristics and requirements, which will help limit the number of databases to be considered. This will further reduce the operational overhead.
Evaluate the cost and efficiency
After classifying the database based on your needs, you may further assess the cost and efficiency of the DBs in consideration. Remember, the IoT application stack’s increases in complexity will further add to the operational cost and overhead. The total cost of the database relies on many parameters. The cost of the database itself comes as a small portion of the actual cost. There may be database license cost, infrastructure cost, data loss cost, data restoration cost, etc. The need for automation and deployment, failover, provisioning, partitioning, scaling, backup, and recovery need may add to the operational overhead.
Once you go run through these steps effectively, you can surely get hold of the right databases for your next-gen IoT solutions.