KAWASAKI―Toshiba Digital Solutions Corporation (Toshiba) today announced the general availability of GridDB 5.0 Enterprise Edition, a purpose-built database for IoT and Big Data workloads with a new architecture that can handle multiple data model.
In the past, GridDB developed Event-Driven Engine*1 and Autonomous Data Distribution Algorithm (ADDA)*2 to support Big Data and IoT systems that require fast, scalable and reliable data store. However, in recent years, IoT data and its utilization have diversified immensely resulting in the need to handle different types of data using different data models accordingly. The current solutions are to utilize multiple Database Management Systems (DBMS) or to store all data into a single data model, which introduces a variety of problems such as system complexity, high operational costs, and a drop in performance.
GridDB 5.0 Enterprise Edition comes with a revamped architecture that features pluggable data store where multiple data model can be managed in a single DBMS. In addition to the current data store optimized for high-frequency high-volume data ingestion, other data stores for executing complex analyses at high speed and for storing text such as logs can be incorporated.
Recently, in addition to the value provided by IoT systems in storing and visualizing large amounts of sensor data, there has been a growing need to utilize the data by performing complex analyses to gain new business insights. The ability to store large volumes of high frequency data and perform complex analyses at high speed are conflicting requirements for a DBMS.
The pluggable data store function makes it possible to integrate multiple data stores optimized for specific workload into a single DBMS. Rather than using multiple DBMSs, integrated processing can be executed in a single DBMS and thus avoiding increased system complexity, and higher construction and operational costs.
A data store that is able to perform complex analyses at high speed and another optimized for storing text data will be provided sequentially in the future.