Description
We present our solution for building an AI Architecture that provides engineering teams the ability to leverage data to drive insight and help our customers solve their problems. We started with siloed data, entities that were described differently by each product, different formats, complicated security and access schemes, data spread over numerous locations and systems. We discuss how we created a Delta Lake to bring this data together, instrumenting data ingestion from the various external data sources, setting up legal and security protocols for accessing the Delta Lake, and go into detail about our method of making all the data conformed into a Common Data Model using a metadata driven pipeline. This metadata driven pipeline or Configuration Driven Pipeline (CDP) uses Spark Structured Streaming to take change events from the ingested data, references a Data Catalog Service to obtain mapping and the transformations required to push this conformed data into the Common Data Model. The pipeline uses extensive Spark API to perform the numerous types of transformations required to take these change events as they come in and UPSERT into a Delta CDM. This model can take any set of r…
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