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Use Cognite Data Fusion with Power BI and Excel

Connect a Cognite Data Fusion (CDF) project as a data source and use Microsoft Power BI and Microsoft Excel to query, transform, and visualize your data and share insights across your organization or embed them in your app or website.

Power BI REST API connector

  • Power BI connector (REST API) — use the Power BI REST API connector to fetch data with Cognite's OData services or other Cognite APIs using GET, POST, and with GraphQL queries for Cognite data models.

  • Power BI REST API functions — learn the details about the Power BI REST API functions.

  • Power Query functions and example queries — combine the functions exposed by the Cognite Data Fusion (REST API) connector for Power BI with Power Query to fetch and transform data with the Cognite API.

Power BI OData connector

  • Power BI connector (OData) — use the Power BI OData connector to fetch data using Cognite's OData services. The connector is backward-compatible with existing reports.

Excel

  • Excel: Retrieve data from CDF — connect a Cognite Data Fusion (CDF) project as a data source and use Excel to query, transform, and visualize the CDF data.

OData services

  • Cognite OData services — Cognite provides OData services to fetch data from Cognite Data Fusion (CDF) using OData clients such as Microsoft Power BI, Microsoft Excel, and TIBCO Spotfire.

  • Asset-centric OData service — connect Cognite Data Fusion (CDF) as a data source and use OData clients to query, transform, and visualize data stored in CDF asset-centric resources.

  • Data modeling OData service — connect a Cognite Data Fusion (CDF) data model as a data source and use OData clients to query, transform, and visualize data stored in CDF data models.

  • Custom OData queries — both the asset-centric and data-modeling OData services support custom queries to filter properties and retrieve specific datasets.

  • Get aggregated time series data — Cognite Data Fusion (CDF) pre-calculates the most common aggregates for numerical data points in time series. These aggregates are available with short response times even when you are querying across large data sets.

  • Best practices and troubleshooting — get the most out of your OData client with these best practices and troubleshooting tips.