Internet of Things (IoT) use cases can demand IoT database performance which is not easily achievable by most common relational databases. IoT use cases 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 an IoT database which provides both a high data ingestion rate and real-time processing speeds, many organizations look to hybrid transactional/analytical processing (HTAP) solutions. These solutions typically rely on databases which provide distributed database scalability in order to 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 using the same dataset.
IoT Databases and In-Memory Computing
The GridGain in-memory computing platform can be deployed as an in-memory Internet of Things database. With ACID transaction support and ANSI-99 SQL compliance including DDL and DML, GridGain can function as an in-memory HTAP database which is 1,000 times faster than disk-based databases and can be easily scaled to petabytes by adding more nodes to the cluster. Built on Apache® Ignite™, GridGain has native support for Apache® Kafka™ as well as native integration support for many other commonly used solutions. The fast data ingest capabilities of GridGain make it ideal as an IoT database for applications such as for smart cars, smart cities, airplane fleet management, facilities management or any IoT use case which generates large quantities of data and requires immediate analysis in order to drive real-time decision making.
Leading IoT platform vendors incorporate GridGain into their architecture to deliver the performance and scalability their customers require.