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With Cognite, you own your data. We use it only to provide agreed-upon services, handle it securely, and comply with privacy and legal regulations. If you leave our services, we ensure you continue to own your data. Cognite Data Fusion (CDF) runs in the cloud and has a modular design that lets you stream data from various sources, contextualize it, and make it available through dedicated workspaces, APIs, and SDKs.
The CDF architecture

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Implementation steps

The following steps provide a high-level overview of implementing CDF. For a complete implementation guide, see Deploy CDF.
1

Set up data management

Before integrating data, define your data governance policies. Appoint a CDF admin to work with IT to ensure CDF follows your organization’s security practices. Connect CDF to your identity provider (IdP) to manage access using existing user identities. CDF provides out-of-the-box data models to build a structured, flexible, contextualized knowledge graph.
2

Integrate data

Add data from your IT, OT, and ET sources into CDF, including industrial control systems, ERP systems, and 3D CAD models. Extract data using standard protocols like PostgreSQL and OPC-UA, then transform it in the CDF staging area to fit the CDF data model. Use automatic and interactive contextualization tools that combine AI, machine learning, and domain expertise to map resources from different source systems to each other.
3

Consume data and build solutions

With contextualized data in your industrial knowledge graph, use the built-in industrial tools and build powerful apps and AI agents to meet your business needs. All data is available through our REST-based API. Cognite provides connectors and SDKs for common programming languages and analytics tools, like Python, JavaScript, Spark, OData (Excel, Power BI), and Grafana. The Functions service provides a scalable, secure way to host and run Python code.