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Lexicon
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Before we dive into the concepts, let's clarify the terminology we'll be using:
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* - Directed Acyclic Graph (DAG)
- A `directed acyclic graph <https://en.wikipedia.org/wiki/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.
* - Transform Function, or simply Function
- A python function used to represent a Hamilton transform -- it can compile to one (in the standard case) or \
many (with the use of decorators) transforms. See :doc:`hamilton-function-structure` for more details.
* - Transform
- A 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.
* - Node
- Synonymous with Transform. A node in the DAG is equivalent to a transform step in the DAG.
* - Hamilton Driver
- A specific class written to enable execution of the DAG with certain parameters. This will be your main \
interface with your Hamilton code. See :doc:`driver-capabilities` for more detail.
* - "*driver*" script/code
- Code that you write to tell Hamilton what the DAG is, what outputs you want, and where the inputs come from.
* - Config
- Data that dictates the way the DAG is constructed. See :doc:`driver-capabilities` for more details.
* - Decorators
- A python function that modifies another python function in some way. Used in Hamilton to compile functions to \
sets of transforms. See :doc:`decorators-overview` for more detail.