GridDB, which specializes in working with big data, especially from IoT systems, is in use in several actual worksites, primarily systems used in societal infrastructure and factories. Let's look at one example.
GridDB is adopted in the quality management system of a hard disk manufacturer to accumulate and analyze sensor data from manufacturing equipment over a long period of time so that the findings can be used to make product quality improvements. This system is intended to accumulate all data related to manufacturing and quality, which has amounted to 1.9 petabytes over the course of five years. Before the introduction of GridDB, the company used specialized database devices to analyze the large volume of data. However, these specialized devices were expensive, and the company found itself having to add more of these expensive database analysis devices to keep up with the amount of data, which was growing year by year. This presented the company with a major cost problem.
That is why the company decided to switch from using specialized database devices to GridDB, which could be configured as a cluster of ordinary IA servers. Switching to GridDB reduced expenses significantly, and the company has been using GridDB for roughly three years.
While the factory is in operation, sensor data is constantly being generated by the factory's manufacturing equipment. GridDB distributes this data based on the key container data model, accumulating the data while maintaining a good balance of data storage across multiple nodes. In parallel with this data accumulation, the company is also analyzing the data by using a distributed query execution plan to execute SQL statements. GridDB is enabling the company to use tried-and-trusted BI tools to analyze manufacturing data over a wider range and over a longer period of time. This improves the accuracy of the analyses and enables results to be used in advanced quality management. GridDB has highly reliable cluster processing technologies, technologies for accumulating and analyzing data in a way that enables long-term, high-speed processing, and diverse interfaces, so it has broadened the company's ability to leverage its petabytes of manufacturing data.
GridDB is also adopted in systems that require high reliability and high performance, such as the low-voltage wheeling operation systems used by power companies.
* This is introduced in detail in DiGiTAL T-SOUL Vol. 22.
In Part 2, we have presented the technical features of GridDB, which are suited for working with massive time-series data, and an example of how its strengths are being put to use. In Part 3, we will look at GridDB Cloud, a managed service based on all of our GridDB operation expertise, and at GridDB's open source software (OSS) activities.