Glossary

Before we dive into the concepts, let’s clarify the terminology we’ll be using:

Directed Acyclic Graph (DAG)	A directed acyclic graph is a computer science/mathematics term for representing the world with “nodes” and “edges”, where “edges” only flow in one direction. It is called a graph because it can be drawn and visualized.
Dataflow	The organization of functions and dependencies. This is a DAG – it’s directed (one function is running before the other), acyclic, (there are no cycles, i.e., no function runs before itself), and a graph (it is easily naturally represented by nodes and edges) and can be represented visually. See Functions, nodes & dataflow.
Node | Hamilton node | Transform	A single step in the dataflow DAG representing a computation – usually 1:1 with functions but decorators break that pattern – in which case multiple transforms trace back to a single function. See Functions, nodes & dataflow.
Function | Python function | Hamilton function | Node definition	A Python function written by a user to create a single node (in the standard case) or many (using function modifiers). See Functions, nodes & dataflow.
Module | Python module	Python code organized into a .py file. These are natural groupings of functions that turn to a set of nodes. See Code Organization for more details.
Driver | Hamilton Driver	An object that loads Python modules to build a dataflow. It is responsible for visualizing and executing the dataflow. See Driver.
script | runner | driver code	The piece of code where you create the Driver and execute the dataflow to get results.
Config	Data that dictates the way the DAG is constructed. See Driver.
Function modifiers | Decorators	A function that modifies how your Hamilton function is compiled into a Hamilton node. See Function modifiers.