Cognite Data Fusion Tour - Part 4: Scaling

Cognite Data Fusion is a product built to address the challenges of working with industrial data by:

• Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale.

• Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data

• Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value

• Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation

With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.

The videos are based on the “ice cream factory” use case: a use case we created in Cognite, to replicate challenges the manufacturing industry usually faces. The series will be divided as follows: 

• Extraction and Contextualisation

• Deeper dive on Contextualisation

• Analytics

• Scaling

In the fourth and last of four posts series, we will have a deeper dive on Scaling: how to go from a proof of concept to a real use case, how to scale from one plant to many thanks to Cognite Data Fusion for example. @Hunter Beck and @Morten will guide you through some of the aforementioned Cognite Data Fusion capabilities in the videos below.

Scaling:

Deeper dive on Templates and GraphQL:

@Rocco Addona, @kelvin: maybe you could tell us an example of scaling with Cognite Data Fusion ?