GridGain can be used in Internet of Things (IoT) use cases for IoT database acceleration of common relational databases. High volumes of incoming data which must be processed, analyzed and acted upon in real-time is characteristic of IoT applications, especially for Industrial Internet of Things (IIoT) use cases. Hybrid transactional/analytical processing (HTAP) solutions can address the need for both a high data ingestion rate and real-time speed . These solutions typically rely on database scalability so they can scale out as data volumes grow. By deploying IoT database acceleration strategies, companies can evolve their existing RDBMS using in-memory computing which are able to deliver real-time OLTP and OLAP performance on a single data set.
IoT Database Acceleration and In-Memory Computing
The GridGain in-memory computing platform is deployed for IoT database acceleration between the application and data layers on a cluster of servers. GridGain supports ACID transactions and distributed ANSI-99 SQL including DDL and DML. GridGain can allow RDBMSs to function as in-memory HTAP databases which are extremely fast and can be easily scaled by adding more nodes to the GridGain cluster. Built on Apache® Ignite™, GridGain has native support for Apache® Kafka™ as well as integrations for many other commonly used solutions. The GridGain fast data ingest capabilities make it ideal as an IoT database acceleration solution for applications ranging from smart power grids to self-driving cars to connected buildings or any IoT use case which generates large quantities of real-time data that requires immediate analysis to drive real-time action. When deployed as an in-memory database, GridGain can function as an IoT database without requiring an underlying RDBMS.
Leading IoT platform vendors incorporate GridGain into their architecture to create IoT solutions 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.