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Welcome to the DeepLynx wiki!

This wiki endeavors to be a comprehensive look at DeepLynx. If after reading you have additional questions, spot an error, or simply wish to clarify something already written please don't hesitate to reach out to our development team at [email protected]

DeepLynx is a open-source data warehouse focused on enabling complex projects to embrace digital engineering. It accomplishes bringing digital thread and digital twins to these projects with integrations to a large collection of software systems across a project's lifecycle.

Data is stored in a graph-like format following a user-defined domain ontology. Using the provided GraphQL interface, users and applications can request exactly the data they need by using client side defined queries. This aids finding relationships between complex datasets enabling data science efforts and AI/ML.

The construction of megaprojects has consistently demonstrated challenges for project managers in regard to meeting cost, schedule, and performance requirements.

Currently, engineering teams operate in siloed tools and disparate teams. Data connections across design, procurement, construction, and operations systems are translated manually or over brittle point-to-point integrations.

This uncoordinated and disjoint data exchange across these siloes increases the risk of silent errors. These silent errors can cascade across the effort and lead to uncontrollable risk during construction, resulting in significant delays and cost overruns.

DeepLynx is a key tool in solving this problem for megaprojects by bringing those siloed efforts into an integrated platform that operates over the course of a project's lifecycle. DeepLynx integrates to widely used enterprise scale software. The list of software integrations include tools such as Innoslate for systems engineering, IBM's DOORS for requirements management, design tools such as AutoDesk's Revit, and asset management in ABB's AssetSuite.

Leveraging this rich set of integrations allows for projects to efficiently consolidate their data into a cohesive data lake. This data lake provides the foundation for digital thread and digital twin efforts.