Data Modeling: Knowledge Graph Creation

Data Modeling: Knowledge Graph Creation

In this learning path, we will focus on creating a Knowledge Graph. Focusing on the Population. This focuses exclusively on getting the data into an existing schema. It introduces the data sources and the specific methods/tools needed for loading nodes and edges.

This is a foundational learning path for anyone new to Data Modeling, but with some knowledge of Cognite Data Fusion. The path consists of 2 courses and an assessment, and takes about 2 hours to complete. Passing the assessment earns you a badge and a certificate to display on Cognite Hub and LinkedIn. 

Learning goals:

• When to use Direct Relations vs Edge Instances to model complex industrial assets
• Write SQL transformations using SQL functions to link instances across different spaces
• Diagnose and fix common ingestion errors where relationships fail to form due to data quality issues
• Optimize and maintain graph health by implementing high-performance ingestion patterns and managing the full data lifecycle
• Learn the difference between the Core Data Model (CDM) Core Features and Core Concepts
• Learn about the CDM concepts, as CogniteAssets, CogniteEquipment, CogniteFiles, and CogniteTimeseries 
• Know about the industry-specific extensions

Prerequisites:

• Data Modeling: Schema Design and Structure (learning path, 5 hours) (link to the learning path) 
• CDF Transformations (link to the course)
• Visual Studio Code or similar, for the hands-on
• CDF Toolkit version 0.6.87 installed
• Familiar with CDF Toolkit and its basic usage
  
• Read and write access to a CDF project (this will not be provided to you)