Static and Interactive Data Visualizations in Python

A transformer is a neural-network design that takes a whole sequence in at once and, for every element in it, works out how strongly each of the other elements should influence that one.

Prompt engineering is the practice of composing what you send a generative model, the instruction, the supporting material and the worked examples, so that it returns something you can use.

Retrieval-augmented generation is a pattern that searches a body of documents when a question arrives and places the passages it finds into the model's prompt, so the answer is drawn from those sources rather than from training alone.

ETL is the pattern for moving data from the systems that produce it into the one place it is analysed: extract the records from each source, transform them into a common, cleaned shape, and load them into a warehouse; ELT keeps the same three steps and runs the transformation inside the warehouse after loading.

A data warehouse is a database built for analysis rather than for running the business: it holds copies of records from many operational systems, integrated under one set of definitions, kept as history rather than overwritten, and arranged so that questions across years and departments run fast.

A data lake is a store that keeps data in its original form, files of any shape in cheap object storage, loaded before anyone has decided what questions will be asked of it, so that the structure is applied when the data is read rather than when it is written.