From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
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Aspects of data engineering - Neo4j Tutorial
From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
Aspects of data engineering
- [Instructor] Data engineering always starts with gathering data and governance requirements for the use case, as well as understanding what can be reused from your current data and architecture capabilities. We will go over this first, starting in Chapter 2, and then walk through the other aspects of data engineering throughout this course. The second aspect we will cover is extraction of data. Here, we will assess what you have, its quality and what scheme it's in, where it came from, how it's delivered, and how it's updated and accessed currently. From there, we'll get into the third step, creating our data model, the blueprint that the data will populate so that our AI can use this. We will be focusing on semantic data models in this course. Then, we will move to the fourth step and go through the various parts of transforming the data, the tools to use and documenting decisions for data management and downstream applications. Finally, we'll finish up with the fifth step and…
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