Internet of Things (IoT) use cases can demand performance which is not easily achievable by most common relational databases. IoT can be characterized by high volumes of incoming data which must be processed, analyzed and acted upon in real-time. This can especially be the case for Industrial Internet of Things (IIoT) use cases. To address the need for both a high data ingestion rate and real-time speed, many organizations look to hybrid transactional/analytical processing (HTAP) solutions. These solutions typically rely on databases which provide distributed scalability so they can scale out as data volumes grow. The newest wave of IoT databases are HTAP solutions based on in-memory computing which are able to deliver real-time OLTP and OLAP performance.
IoT Databases and In-Memory Computing
The GridGain in-memory computing platform is deployed for IoT use cases between the application and database layers on a server cluster. With ACID transactions and distributed ANSI-99 SQL support, GridGain can function as an in-memory HTAP database which is extremely fast and can be easily scaled by adding more nodes to the cluster. Built on Apache® Ignite™, the system has native support for Apache® Kafka™ as well as integrations for many other commonly used solutions. The fast data ingest capabilities of GridGain make it ideal as an IoT database solution for applications such as for smart cars, smart cities, airplane fleet management, or any other IoT use case which generates large quantities of real-time data and requires immediate analysis in order to drive real-time decision making.
Leading IoT platform vendors incorporate GridGain into their architecture to create IoT databases which can deliver the performance and scalability their customers demand. GridGain is already used in IoT platforms which ingest and process data from millions of sensors in real-time.