From the course: Knowledge Graph Data Engineering for Generative AI Use Cases

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Data transformation

Data transformation - Neo4j Tutorial

From the course: Knowledge Graph Data Engineering for Generative AI Use Cases

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Data transformation

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- [Instructor] A common issue is transforming data from one model or source to another. If your source data is using a custom model, which is pretty common, you'll need to first transition the source model to then map or align it with the new model you are creating. You can do this by creating an ETL mapping file where the incoming source data via API or data dump is mapped to where the data should go in our new model. And if data needs to be merged or reformatted, this is also listed in a traditional ETL mapping file to then execute when using whichever ETL tool or approach you choose or that you're already using in your data pipeline. But many graph native tools have ETL built in for small batch updates. So we will use that in our Stardog instance after we model the ETL in the rest of our design doc. In our raw data, we now can address some of those limitations we identified earlier in the course. So the first thing is we have an anonymous customer. We know from our business rules…

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