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Hey everyone! 👋 I've been diving into the world of CDF data modeling for the past couple of weeks, and I thought I'd share what I've learned so far in a way that's easy to understand.
First off, let's talk about the term "Data Modeling." It's everywhere in CDF - in API endpoints, UI, docs - but its meaning can vary depending on where you look. After some digging, I've figured out what it means specifically in CDF. Essentially, it's about organizing, storing, and working with data in a way that's perfect for handling complex industrial scenarios. Think of it as the foundation for unleashing the power of Cognite Data Fusion on your industrial data.
So, what exactly makes up a Data Model in CDF? Let's break it down:
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Space: Think of it like a folder where everything in a Data Model hangs out. Containers, views, instances - they all belong to a Space and have their own unique ID.
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Containers: These are like the storage units for properties. They live within a space and hold sets of properties that go together. You can think of them as the homes for your data.
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Views: Views are like magic tables created from database queries. They show specific data in a neat format without actually storing it, which makes it super easy to work with info from different tables.
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Instances (nodes and edges): This is where the real action happens. Nodes can be anything - like pumps or other real-world objects - while edges describe how these nodes are related. Each instance has its own unique ID within a space.
Usually when people refers to Data Model they also talk about a Knowledge graph. What is a knowledge graph? how does it relates to a Data Model? Well I am still in the quest for that answer. When I know more I will be back here and share with you
Before I sign off, I'd love to hear your thoughts! Do you find posts like this helpful? Give it a like or drop a comment. And if you've got any tips or experiences with CDF Data Modeling that could help others, feel free to share!
Thanks for reading!
Cheers,
Elka 🚀