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Welcome
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Before you begin
- 1. Cognite Data Modeling
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Section overview
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What is Cognite Data Modeling?
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Uses of Cognite Data Modeling Services
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Cognite Data Fusion (CDF) data modeling and industrial knowledge graphs
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Example
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Core Data Modeling Concepts of Cognite's Data Modeling Services
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Exploring property graphs: a guide to nodes, edges, and attributes
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Summary
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Check your knowledge
- 2. Constructing an Industrial Knowledge Graph
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Section overview
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Spaces: organisational units
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Understanding instances in CDF
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Exploring nodes, edges and direct relations
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Example: how type nodes are used?
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Example of a knowledge graph: industrial fluid system
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Summary
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Check your knowledge
- 3. Crafting Schemas
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Section overview
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Crafting schemas
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Understanding containers
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Populating containers with data
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Navigating views
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View filters
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Polymorphism in views
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Managing data models
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Optimizing performance
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Constraints in action
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Summary
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Check your knowledge
- 4. System Schemas in Cognite Data Fusion
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Section overview
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System schemas in Cognite Data Fusion (CDF)
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Summary
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Check your knowledge
- 5. Instance Ingestion and Management in Graphs
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Section overview
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Ingesting instances
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Illustration on create, patch, and replace modes
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Autocreation
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Optimistic concurrency control part 1
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Optimistic concurrency control part 2
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Deleting instances safely part 1
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Deleting instances safely part 2
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Summary
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Check your knowledge
- 6. Query Features
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Section overview
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Query endpoints
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Constructing graph queries
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Constructing query using with, parameter and select
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Understanding result set expressions in graph queries
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Node result set expressions
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Edge result set expressions
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Navigating the 'selects' in graph query configurations
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Navigating advanced filters and traversal
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Specifying containers and views
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Parameterized filters
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Efficient pagination and sorting
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Sync and subscriptions
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Limitations of graph querying and syncing
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More sample queries
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Summary
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Check your knowledge
- 7. Control Access to a Graph
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Section overview
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Control access to a graph
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Summary
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Check your knowledge
- 8. Optimising Data Models for AI Search
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Section overview
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Optimizing data models for AI search
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Summary
- End of course
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Cognite Data Modeling
Explore the fundamental concepts, practical applications, and advanced techniques for effectively organizing, querying, and managing industrial data.
Welcome to a comprehensive course on Data Modeling in Cognite Data Fusion (CDF). In this course, we will explore the fundamental concepts, practical applications, and advanced techniques for effectively organizing, querying, and managing industrial data. Whether you're new to data modeling or looking to deepen your understanding, this course will equip you with the knowledge and skills to harness the power of data modeling in CDF.
Course Overview:
Data Modeling in CDF simplifies the understanding of complex data relationships. The core purpose of data modeling is to organize and standardize how we perceive real-world entities and their interactions within a system. We achieve this through Cognite Data Modeling, which includes building models, ingesting data, and querying models, ultimately enhancing industrial knowledge with Cognite Data Fusion (CDF).
Key Concepts:
The course covers essential concepts of Data Modeling, such as Data Models, Spaces, Containers, Views, and Instances, as well as its public REST and GraphQL APIs.
Lessons:
1. Constructing an Industrial Knowledge Graph: Learn how to build and manage an industrial knowledge graph and model relations between entities.
2. Crafting Schemas: Explore advanced structuring techniques for enhancing knowledge graphs through schemas, containers, views, and data models. Optimise performance for large-scale applications.
3. System Schemas: Dive deep into system schemas, their role, structure, and utilization within CDF. Understand how to work within system constraints and effectively use system schemas for data enrichment.
4. Instance Ingestion and Management: Learn instance ingestion and management using the /apply and /delete endpoints. Learn about data accuracy, atomic application, autocreation, and optimistic concurrency control.
5. Querying Graphs in CDF: Explore the capabilities of querying graphs within CDF, including various query endpoints, constructing graph queries, advanced filters, and sync and subscriptions.
6. Access Control in Data Modeling: Understand access control based on spaces, Access Control Lists (ACLs), granularity in permissions, and access restrictions.
7. Optimising AI Search: Enhance the effectiveness of AI search within CDF through optimized data models, semantic accuracy, documentation best practices, human-readable names, descriptive detailing, and specific recommendations.
By the end of this course, you will have the knowledge and practical skills to harness the full potential of data modeling in Cognite Data Fusion, enabling you to organize industrial data effectively, optimize performance, and ensure data integrity for various applications. Let's embark on this journey to unlock the power of data modeling in CDF!