Cognite Data Modeling

Explore the fundamental concepts, practical applications, and advanced techniques for effectively organizing, and managing industrial data.

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About this course

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 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).

By the end of this course, you will have a solid understanding of data modeling in Cognite Data Fusion (CDF) and its fundamental concepts. You will learn how to build a simple model, ingest data, and perform basic queries. Additionally, you will gain insight into how this model can be further applied within 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.

Who should take this course?
Anyone interested in knowing more about data modeling capabilities in Cognite Data Fusion.

Instructor
Cognite Academy has developed this course.

Elka Sierra
Lead Product Manager - Data Modeling

Content4 hrs

  • Welcome
  • Before you begin
  • 1. Cognite Data Modeling
  • What is Cognite Data Modeling?
  • Explore Cognite Data Models
  • Overview of Data Modeling phases in Cognite Data Fusion
  • Example
  • Summary
  • Check your knowledge
  • 2. Concepts in Data Modeling
  • Cognite's Data Modeling Services: basic concepts
  • Understanding instances in CDF
  • About views
  • Relationships
  • Exploring nodes, edges and direct relations
  • Example: how type nodes are used?
  • Summary
  • Check your knowledge
  • 3. Constructing a graph
  • Defining a schema
  • Understanding spaces
  • Understanding containers
  • Understanding views
  • View filters
  • Exploring property graphs: a guide to nodes, edges, and attributes
  • Example: Constraints in action
  • Example of a knowledge graph: industrial fluid system
  • Summary
  • Check your knowledge
  • 4. Cognite Core Data Model
  • Cognite Core Data Model
  • Building an asset hierarchy with the Cognite core data model
  • Summary
  • Check your knowledge
  • 5. Populate and consume
  • Populating containers with data
  • Retrieve data with queries in GraphQL
  • Tools to manage your Data Model
  • Cognite Data Fusion Toolkit
  • Python SDK
  • Explore data using Industrial Tools
  • Summary
  • Check your knowledge
  • End of course
  • Share your feedback
  • Additional resources

About this course

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 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).

By the end of this course, you will have a solid understanding of data modeling in Cognite Data Fusion (CDF) and its fundamental concepts. You will learn how to build a simple model, ingest data, and perform basic queries. Additionally, you will gain insight into how this model can be further applied within 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.

Who should take this course?
Anyone interested in knowing more about data modeling capabilities in Cognite Data Fusion.

Instructor
Cognite Academy has developed this course.

Elka Sierra
Lead Product Manager - Data Modeling

Content4 hrs

  • Welcome
  • Before you begin
  • 1. Cognite Data Modeling
  • What is Cognite Data Modeling?
  • Explore Cognite Data Models
  • Overview of Data Modeling phases in Cognite Data Fusion
  • Example
  • Summary
  • Check your knowledge
  • 2. Concepts in Data Modeling
  • Cognite's Data Modeling Services: basic concepts
  • Understanding instances in CDF
  • About views
  • Relationships
  • Exploring nodes, edges and direct relations
  • Example: how type nodes are used?
  • Summary
  • Check your knowledge
  • 3. Constructing a graph
  • Defining a schema
  • Understanding spaces
  • Understanding containers
  • Understanding views
  • View filters
  • Exploring property graphs: a guide to nodes, edges, and attributes
  • Example: Constraints in action
  • Example of a knowledge graph: industrial fluid system
  • Summary
  • Check your knowledge
  • 4. Cognite Core Data Model
  • Cognite Core Data Model
  • Building an asset hierarchy with the Cognite core data model
  • Summary
  • Check your knowledge
  • 5. Populate and consume
  • Populating containers with data
  • Retrieve data with queries in GraphQL
  • Tools to manage your Data Model
  • Cognite Data Fusion Toolkit
  • Python SDK
  • Explore data using Industrial Tools
  • Summary
  • Check your knowledge
  • End of course
  • Share your feedback
  • Additional resources