From the course: Data Integration and API Development for AI Applications

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Phases of data integration: Data mapping, transformation, and loading

Phases of data integration: Data mapping, transformation, and loading

From the course: Data Integration and API Development for AI Applications

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Phases of data integration: Data mapping, transformation, and loading

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Once you've ingested the data from disparate sources, the next step is to map your data to the target schema. Data mapping defines how fields from different sources correspond to those in the target schema. This is about aligning data elements like column names, data types, and units to what you want it to be finally. This step is crucial when you integrate data from systems that use different terminologies or structures for similar concepts. Mapping ensures consistency in naming, interpretation, and usage of data. When you're ingesting data, you are likely using a special tool or technology, and these tools, especially the modern tools, often have a data mapping phase. They allow rule-based or visual mapping, which makes it easier for you to manage changes over time and to enforce standards. Mapping, of course, is needed for data that has a schema, whether it's structured or semi-structured. For example, your source data might have no column names, but you might want meaningful…

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