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Cognite Data Fusion (CDF) Documentation

website·Indexed 36 days ago

https://docs.cognite.com/

Documentation for Cognite Data Fusion (CDF), covering data engineering, data governance, and data workflows. It includes detailed guides on the Data Modeling Service (DMS) for building industrial knowledge graphs using property graphs, GraphQL queries and mutations, and the Data Modeling Language (DML) extension.

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What's inside Cognite Data Fusion

  1. Overview of Atlas AI agent evaluations

    Atlas AI agent evaluations allow developers to measure performance and verify agent behavior by testing how agents respond to specific prompts (e.g., questions about equipment status or maintenance history). Evaluations compare the agent's generated response against a predefined expected response to identify performance gaps and areas for improvement.

  2. Overview of legacy Assets in Cognite Data Fusion

    Assets are digital representations of physical objects or groups (e.g., pumps, machine rooms, plants) used to contextualize data in Cognite Data Fusion (CDF). They act as a hub to connect related data from different sources, such as time series, events, and files.

    Note: This is part of the legacy asset-centric data model. For new projects, use the Data Modeling service and the CogniteAsset concept for greater flexibility.

  3. Overview of 3D data contextualization in Cognite Data Fusion

    Contextualization in Cognite Data Fusion (CDF) is the process of linking 3D geometry—specifically CAD models, point clouds, and 360° images—to assets. The specific APIs and methods used for this process depend on whether the CDF project is a Data Modeling project or a Legacy/Hybrid project.

  4. Overview of the SAP writeback service

    The SAP writeback service enables the creation and updating of SAP records and attachments in SAP S/4HANA by sending data from Cognite Data Fusion (CDF) through configured OData V2.0 endpoints. It currently supports Maintenance notifications and Attachments.

  5. Overview of builder skills for CDF Flows

    Builder skills are specialized knowledge and workflows that extend the capabilities of AI coding agents (such as Claude Code, Cursor, GitHub Copilot, and Windsurf) when building and deploying custom apps for Cognite Data Fusion (CDF). They encapsulate Cognite best practices, API guidance, and opinionated workflows to ensure agents remain aligned with development standards.

  6. Overview of Cognite Functions

    Cognite Functions is a feature within Cognite Data Fusion (CDF) that enables developers to deploy Python code to the platform. Once deployed, these functions can be triggered on-demand or scheduled to run at regular intervals.

  7. Overview of GraphQL for Cognite Data Fusion (CDF) Data Modeling

    Cognite Data Fusion (CDF) utilizes GraphQL as a flexible interface for interacting with data models. It serves three primary purposes: as a query language for reading data, a mutation language for writing data, and a data modeling language (DML) for defining schemas, managing data models, and creating views.

  8. Overview of Python Data Modeling SDK Generator (Pygen)

    Pygen is a Python package and command-line interface (pygen) designed to generate Python SDKs from Cognite Data Fusion (CDF) data models. It serves three primary purposes: acting as an exploration tool to discover existing data models and their contents, providing a standardized interface for developers building solutions on top of data models, and facilitating the population of data models using data from external sources.

  9. Overview of the PostgreSQL gateway for CDF ingestion

    The Cognite PostgreSQL gateway provides a PostgreSQL interface to Cognite Data Fusion (CDF), allowing CDF resource types (such as assets, events, and datapoints) and CDF RAW to appear as tables in a PostgreSQL database. It is primarily intended for data ingestion from ETL tools.

    Key Considerations:

    • Supported Tools: Microsoft Azure Data Factory is the officially supported ETL tool.
    • Use Case: Ideal for integrating new data sources via ADF or other ETL tools that support PostgreSQL sinks, or for extractors previously built for PostgreSQL.
    • BI Tools: Do not connect business intelligence tools directly to the gateway. For aggregation-heavy queries or BI, use CDF Transformations to materialize data to CDF RAW or a data model.
    • Performance: For extremely high-performance ingestion (10k-50k rows/points per second), consider alternative solutions.

  10. Overview of Cognite Data Fusion (CDF) build tools

    Cognite Data Fusion (CDF) provides several pathways for building and deploying industrial solutions depending on the use case:

    • AI & Integration: Use the Industrial MCP server to allow external AI tools to query CDF, or use Atlas AI to build and deploy AI agents and tools for advanced analytics.
    • Application Development: Build interactive data apps using Streamlit or develop production-ready React applications using Cognite Flows (which includes scaffolding and authentication). The Aura design system provides the UI primitives and patterns for Flows apps.
    • Automation & Analysis: Use serverless Functions to automate tasks, or Jupyter notebooks for interactive data analysis and visualization.
    • Visualization: Create dashboards using Grafana, Power BI, or OData.
    • Data Science: Develop, train, and deploy machine learning models and data science workflows.

  11. Overview of the Cognite Data Fusion (CDF) Knowledge Graph

    The Data Modeling Service (DMS) allows users to model industrial reality by connecting physical assets, processes, and work orders without rigid hierarchy limits. It is the recommended approach for all new projects. In this model, assets are represented as instances using the CogniteAsset concept, supporting flexible schemas, relations, and hierarchies.

  12. Overview of Cognite Data Source for Grafana

    The Cognite Data Source for Grafana allows users to connect a Cognite Data Fusion (CDF) project as a data source within Grafana to query, transform, and visualize data. It is compatible with Grafana Pro, Advanced, Enterprise, and self-hosted Grafana instances.