IoT Database FAQs
Characteristics of IoT Databases
IoT applications connect to IoT devices that number in the millions or billions and generate real-time data in many formats. These IoT devices connect to data centers or the cloud and are distributed across ‘edge’ locations. Running these real-time IoT applications at scale with high speed and low-latency connectivity demands real-time data solutions with scalability, fault tolerance, flexible data modeling, and cloud/multi-region availability.
Database Requirements for IoT
To get the most out of your IoT platform, you need to use a database built to handle the following challenges:
Varied IoT database schema. Data IoT sensors can take many forms. A growing IoT ecosystem demands a database that can accommodate different data schemas easily and tier data automatically.
Scalability. IoT devices generate data in massive quantities. To avoid performance issues and dowtime, an IoT database must scale simply and automatically.
Data support. In order to reduce disk space and optimize data queries, both operational and time series data support is essential.
Flexible deployment. IoT databases should have on-premises, cloud/edge, and data center capabilities as well as multi-cloud and platform flexibility to manage different platforms, tools, and approaches.
Real-time analytics readiness. Keep data accessible, searchable, and functional.
There are several types of IoT databases. Managed IoT database design can be hot or cold—or streaming or static—depending on the designer’s goals for the application.
Hot databases. “Hot” or streaming databases are a kind of IoT database that is used to store data that is queried, updated, or accessed often. These provide read and write access capabilities at the lowest cost with little latency, so they are often a good choice for simply storing data. Performance-oriented NoSQL databases are commonly used for this use case.
To manage the load and scale requirements of real-time data collection, hotdatabases are generally distributed. The features most associated with hot databases are flexible data formats, messaging and queueing capability, querying abilities, and tiered memory models.
Cold databases. “Cold” databases, also called static or batch databases, store and manage data that is altered very little afterwards and keeps it at rest in its original state. A common use case for this kind of cold database is the storage of specific access management metadata for sensitive records. This kind of data is often managed by a database management system (DBMS) that typically, though not always, uses SQL.
Organizations that use streaming systems can still benefit from including a static database component inside their NoSQL Database system. In this way they can create a larger, unified database that has both streaming and static capabilities.
IoT graph databases. IoT graph databases preserve and leverage relationships between data generated by real-time IoT devices to query in real-time to improve performance. Popular use cases for IoT graph databases include fraud detection, 24-hour customer service, knowledge graphs, network management, personalization, and other areas. Next generation graph databases use artificial intelligence and machine learning for identity and access management, recommendation engines, supply chain management, scientific research, and more.
Why is an Effective Database for IoT Important?
Iot database systems store the data transmitted from different IoT devices and form an essential component of an IoT network. They help integrate data in real-time across a wide range of IoT databases.
IoT database infrastructures process a vast amount of data in real-time and assign meaningful context tags. The system can route tagged data within an IoT infrastructure using MQTT, HTTP, or CoAP, so the central application can use the data generated by smart devices and sensors.
Databases for IoT applications enable more efficient data analysis, improved security functionalities, and more agile data storage. The challenges of IoT include an expensive app development life cycle and the complexity of protecting large volumes of data against potential threats.
IoT Database Example Use Cases
IoT devices drive a number of IoT database use cases. For example:
- Predictive maintenance: Aggregate sensor data from industrial machinery and enable real-time analytics at scale to target maintenance and upgrades before failures and outages occur.
- Real-time analytics: Run heterogenous operational and analytics workloads in real time against the same dataset in a single distributed datastore.
- Asset tracking: Minimize loss, damage, and theft with real-time streaming geolocation data, across transportation, healthcare, agriculture, industrial, retail, and consumer markets.
- Supply chain: Track multi-modal movement of goods around the globe, while monitoring storage conditions and ensuring provenance and chain-of-custody.
Here are a some specific IoT database examples:
Augury: How Augury stores and serves time-series features from massive streams of IoT data, both for real-time insights, and offline learning and analytics.
GE Healthcare: How GE Healthcare employed ScyllaDB’s DynamoDB-compatible API to provide precision medical diagnoses in their Edison AI solution.
Mistaway: How Mistaway powers IoT-enabled mosquito abatement systems that poll time-series data from thousands of users.
Nauto: A look at the database strategy behind Nauto’s intelligent driver safety solution and fleet management safety application that increases fleet safety
SkyElectric: How SkyElectric scale its IoT-driven time series data management services.
SmartDeployAI: How SmartDeployAI builds data workflow pipelines for running large scale Industrial IoT applications.
Faraday Future: How Faraday Future uses database to store critical vehicle data in the cloud.
Types of IoT Database Databases
IoT data is essentially time series data, so the requirements for an IoT database and the requirements for a time series database are very similar. Either must write data in real-time; compress and store data efficiently; and downsample and query data rapidly. There are a range of IoT databases. Some among them are relational databases like PostgreSQL; there are also many IoT NoSQL databases like Cassandra and ScyllaDB that provide lower latencies than RDBMS solutions.
The best database for IoT data depends on the user and use case. SQL databases are generally stable and well-supported, but also typically less well-suited for managing big data. They handle small, personal IoT projects readily, but are not always ideal for high performance IoT systems. For this reason, a NoSQL database for IoT data is usually more common.
In theory, although there are both SQL and NoSQL options, in practice, the NoSQL vs. SQL decision is usually driven by IoT database design. The choice of which IoT database to use depends on the larger set of technical and functional requirements—performance, scalability, interoperability, administrative overhead, security, cost, and more.
Does ScyllaDB Offer Solutions for IoT Database?
Yes. In fact, ScyllaDB is the best IoT database for many use cases.
ScyllaDB’s consistent low latencies enable IoT systems to process billions of events in real time, without the lags that burden other databases. High throughput ingest and predictable performance support ever-increasing events and data streams at global scale.
ScyllaDB offers cross-regional data replication from the edge to the clouds and places storage close to globally distributed IoT operations, providing high availability and data resilience. Optimal hardware utilization, minimal operational overhead, and support for heterogeneous workloads combine to deliver significantly lower total cost of ownership.
Here is an IoT database tutorial that shows you how to build an IoT sensor simulator application that streams data to a ScyllaDB cluster hosted on ScyllaDB Cloud. And here is another IoT database tutorial that shows how to create an Golang IoT app from scratch and configure it to use ScyllaDB as the backend datastore.
Learn more about why ScyllaDB is the best NoSQL database for IoT and other use cases here.
