Crate.io launches CrateOM to optimize operational processes

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Crate.io Inc. has launched CrateOM, meant to convert device data into practical insights. As a result, CrateOM will help organizations reduce downtime, minimize cost and resource waste, and track equipment performance.

About Crate.io’s products

Crate.io, the creator of CrateDB, has now launched CrateOM, a revolutionary service meant to work in conjunction with its leading database to convert processed device data into practical insights that organizations can instantly use to their advantage.

It is alleged that CrateOM is meant to enable digital transformation using in-app communications and real-time insights to improve collaboration and decision-making.

Crate.io is popular for its database, called the CrateDB Structured Query Language database. It functions as a group of containers hosting hundreds of individual components. Since this is unique to SQL databases, the container-based architecture allows CrateDB to redistribute data easily as the clusters’ sizes change accordingly. Thus, making the capture and analysis of data by devices and machines easy.

About the development of CrateOM

CrateOM was created by Crate.io, who partnered with ALPLA-Werke Alwin Lehner GmbH & Co KG, a plastics manufacturer based in Austria. ALPLA runs CrateOM in over 20 German and US manufacturing plants within its ‘Smart Production Program.’

CrateOM is being used to collect data produced by several hundred factory machines to transform those into insights. These can then be used to enhance efficiency. Also, CrateOM can capture a lot of raw data which can be saved on the cloud, so it can come in handy for longer-term projects.

Eva Schönleitner, the CEO of Crate.io, stated, “ALPLA has been a great development partner in the creation of CrateOM. Together, we saw a need for a smart factory solution that would use the advanced functionalities of CrateDB on the shop floor. With the launch of CrateOM, we want to extend our value offering from data collection to enabling operational data analysis in real-time across several plants.”