Cognite

Overview

Cognite Data Fusion (CDF) is an industrial data operations platform developed by Cognite, a company founded in 2016. The platform is designed to address the challenges of integrating, contextualizing, and operationalizing data from complex industrial environments. It targets asset-heavy industries such as oil and gas, manufacturing, power and utilities, and renewables, where operational technology (OT) and information technology (IT) data often reside in silos, hindering data-driven decision-making and AI/ML adoption.

CDF functions by ingesting data from various industrial sources, including historians, enterprise resource planning (ERP) systems, computer-aided design (CAD) models, and sensor data. It then applies a semantic data model to contextualize this raw data, linking related information across different systems. This contextualization process creates a unified, queryable representation of industrial assets and their operations, often referred to as a digital twin. The platform's capabilities are intended to enable developers and data scientists to build and deploy AI/ML models for use cases such as predictive maintenance, operational optimization, and energy management.

The platform's architecture emphasizes scalability and developer accessibility, offering SDKs for Python, JavaScript, and JVM languages, alongside a comprehensive API. This allows enterprises to integrate CDF into existing IT/OT landscapes and develop custom applications on top of the contextualized data. Cognite's approach aims to reduce the time and effort required to prepare industrial data for analytical and AI workloads, moving beyond traditional data warehousing by focusing on the operational context inherent in industrial processes. According to industry analysis, effective data contextualization is a critical factor in realizing value from industrial IoT and AI initiatives, as it transforms raw sensor readings into meaningful operational insights McKinsey & Company research on digital manufacturing.

Cognite offers a free trial of Cognite Data Fusion, allowing prospective users to explore its features for industrial data integration and contextualization. The platform is compliant with industry standards such as SOC 2 Type II, ISO 27001, and GDPR, addressing data security and privacy requirements for enterprise deployments.

Key features

• Industrial Data Ingestion: Connects to diverse OT and IT data sources, including historians, ERPs, MES, and IoT devices.
• Data Contextualization: Automatically links disparate data points to create a unified, semantic model of industrial assets and processes.
• Digital Twin Creation: Builds and maintains digital representations of physical assets, enabling comprehensive data access and analysis.
• Developer SDKs and APIs: Provides SDKs for Python, JavaScript, and JVM, along with a REST API for programmatic interaction and custom application development Cognite developer documentation.
• Data Governance and Security: Incorporates features for data access control, auditing, and compliance with standards like SOC 2 Type II, ISO 27001, and GDPR.
• Pre-built Industrial Solutions: Includes applications like Cognite Maintain for maintenance optimization and Cognite InRobot for robotic data integration.
• Scalable Data Storage: Manages large volumes of time-series, event, and asset data from industrial operations.

Pricing

As of May 2026, Cognite operates on a custom enterprise pricing model. Specific pricing details are not publicly disclosed and require direct engagement with their sales team.

Common integrations

• Historians: Integrates with industrial data historians like OSIsoft PI, AspenTech InfoPlus.21, and GE Predix for time-series data Cognite data integration connectors.
• ERP Systems: Connects with enterprise resource planning systems such as SAP and Oracle for master data and operational context.
• SCADA/DCS Systems: Direct integration with Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) for real-time operational data.
• IoT Platforms: Ingests data from various industrial IoT platforms and sensors.
• Cloud Data Platforms: Supports integration with major cloud providers like Azure, AWS, and Google Cloud for data warehousing and advanced analytics.
• Business Intelligence Tools: Connects with BI tools like Power BI and Tableau for visualization and reporting on contextualized industrial data.

Alternatives

• GE Digital: Offers a suite of industrial software, including the Predix platform, focusing on asset performance management and operational intelligence.
• AVEVA: Provides industrial software for engineering, operations, and performance, including data management and digital twin solutions.
• Seeq: Specializes in advanced analytics for process manufacturing data, enabling engineers and data scientists to analyze time-series data.

Getting started

To begin using Cognite Data Fusion, you typically start by installing the Cognite Python SDK and configuring your client. This example demonstrates fetching a time series and its data points.

from cognite.client import CogniteClient

# Replace with your project and API key or other authentication method
# For production, consider environment variables or secure credential management
client = CogniteClient(
    project="<YOUR_COGNITE_PROJECT>",
    # For API key authentication:
    api_key="<YOUR_API_KEY>", 
    # For client credentials flow (recommended for production):
    # client_id="<YOUR_CLIENT_ID>",
    # client_secret="<YOUR_CLIENT_SECRET>",
    # token_url="<YOUR_TOKEN_URL>"
)

try:
    # List some time series to find one to query
    print("Fetching a time series...")
    time_series_list = client.time_series.list(limit=1, name_prefix="pressure")

    if time_series_list:
        ts = time_series_list[0]
        print(f"Found time series: ID={ts.id}, Name='{ts.name}'")

        # Fetch data points for the time series
        print(f"Fetching data points for time series ID {ts.id}...")
        data_points = client.data_points.retrieve(
            id=ts.id,
            start="2d-ago", # Last 2 days
            end="now",
            limit=100 # Fetch up to 100 data points
        )

        if data_points.value:
            print(f"Retrieved {len(data_points.value)} data points:")
            for dp in data_points.value:
                print(f"  Timestamp: {dp.timestamp}, Value: {dp.value}")
        else:
            print("No data points found for this time series in the specified range.")
    else:
        print("No time series found with the specified prefix. Please adjust the search or create one.")

except Exception as e:
    print(f"An error occurred: {e}")

This Python script initializes a Cognite client, searches for a time series by a name prefix (e.g., "pressure"), and then retrieves the latest data points for that time series. Before running, replace <YOUR_COGNITE_PROJECT> and <YOUR_API_KEY> (or client credentials) with your actual Cognite project details Cognite Python SDK documentation. This setup provides a foundation for interacting with contextualized industrial data within Cognite Data Fusion.