Extract, transform, load (ETL) moves enterprise data from source systems into a form a generative AI application can use. For retrieval-augmented generation (RAG), the pipeline must do more than transfer records: it must prepare, index, and store content so the retriever can find information relevant to a user’s question. section 8.4.1 section 10.4.1
Design the pipeline around the data and the retrieval use case:
- Profile the sources first. Examine their structure, entity relationships, data types, and data distribution; document users’ information needs and success measures. section 8.4.1
- Extract through supported APIs or plugins, accounting for formats, authentication, and refresh needs. Plan for batch or streaming input where the source requires it. section 10.2.3
- Transform content into a usable format. Depending on the data, preprocessing may include tokenization, stop-word removal, or lemmatization; a grounding layer may also create embeddings. section 8.4.1 section 10.2.3
- Index and store the prepared data for retrieval. Choose search and storage components according to the data’s size, type, and speed requirements. section 8.4.1
An enterprise grounding pipeline
Suppose an application needs to answer questions using CRM and ERP information. Its pipeline connects to those systems of record, prepares their data for retrieval, and makes it available to a RAG application. Azure AI Search is one possible full-text index; a vector database is another grounding-layer component when embeddings are used. The design must account for how often the underlying data changes, as well as integration cost and data quality. section 10.4.1 section 8.4.1 section 10.2.3
Treat ETL as part of the application architecture, not a one-time import. Enterprise pipelines may need custom APIs or transformation middleware, scalable processing, and deployment choices that reflect data sensitivity. Poorly planned preprocessing can undermine search even when the data reaches its destination. section 10.4.1 section 10.2.1 section 8.4.1
