Here are the main terms used in ETL documentation relative to its internal software conception:
BOM: Byte-Order-Mark (CSV header).
Cloud: Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user.
CSV: comma-separated values file.
Data engineering: Data engineering refers to the building of systems to enable the collection and usage of data.
Data mining: Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Data Warehouse: a Data Warehouse is a relational database hosted on a server in a Data Center or in the Cloud.
ETL pipeline: it is the set of processes used to format and move data from one or multiple sources into a database such as a data warehouse.
GIS: A geographic information system (GIS) consists of integrated computer hardware and software that store, manage, analyze, edit, output, and visualize geographic data.
Action: (sometimes differenciated from a user action by the term "processing action") a piece of software visually represented by a box in the pipeline editor canvas, with a set of parameters accessible in the bottom left panel. An action generally has one or several sources and one or several destinations, except the special runToFinishLine which has only sources. Sources and destinations are either files, databases, online resources or other actions.
Pipeline: A series of actions organized in a way ment to get some data somewhere, transform it, then save is somewhere.
Python: Python is a high-level, general-purpose programming language.
R: R is a programming language for statistical computing and graphics.
Text mining: Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text.