Scala and Spark for Big Data and Machine Learning

Large language models are neural networks trained on a vast quantity of written text to predict the piece of text that comes next, an objective narrow enough to state in a line and broad enough to yield writing, translation, code and summary as side effects.

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.

SQL is the declarative language for asking questions of relational databases: you state which rows and columns you want and how tables relate, and the database engine works out how to retrieve them.

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.