In particular, the ability to search petabytes of data around the world for a variety of projects by simply drawing a search box on a map, even in complex spatial data formats such as multibeam, sidescan, and sub-bottom profilers. , which no other software has. Currently available. By developing and integrating our own cloud-based data reader technology, we are able to handle complex binary data formats that would typically need to be processed by a number of specific desktop applications. The developed technology will serve as the foundation for domain-wide digital transformation.
offshore: How did the company develop geospatial technology?
Wendt:north.io’s geospatial data hub technology was developed over the past three years by over 65 software and research engineers, primarily based on lessons learned from over 13 years of cloud and geospatial expertise. Ta. The application is designed as a highly scalable microservices architecture with over 70 separate, scalable services powered by the powerful orchestration framework Kubernetes.
The two most important design decisions were neutrality and security. We designed our application to be completely cloud-agnostic. This means you are not tied to any cloud provider. This enables applications to be deployed in public, private, and hybrid environments, with complete isolation, and to immediately address a company’s IT department’s key security concerns.
The technology is designed to handle a wide range of data formats and types from different sensor types and research objectives. This includes data from hydroacoustic measurements, satellites, computer-aided design workflows, and geospatial sources common in large offshore infrastructure projects. Therefore, to achieve maximum efficiency and scalability, a completely new cloud-native binary and geospatial data reader technology has been developed based on modern programming languages Golang and Rust.
Additionally, it is optimized for cloud applications and leverages big data-enabled formats that enable efficient data management and collaboration. For example, the platform works internally using Apache Parquet and Apache Arrow. These data formats are column-based and are very advantageous for handling and processing complex and large-scale data. Parquet’s combination of efficiency, broad compatibility, and continuous enhancements make it well suited for cloud data storage and ocean big data management. By converting internally heterogeneous ocean data formats into a standardized and highly efficient columnar approach, a wide range of advanced big data capabilities can be applied at scale.
Another important technology development is the introduction of geospatial search capabilities. The development of new algorithmic approaches to data reduction and spatial data intersection, combined with high-performance architectures, enables users to quickly find and access data in petabyte-scale environments with the simplicity of map-based search.
