# Directed Acyclic Graphs

## If you don’t know your DAGs from your dogs, you can finally get some clarity and sleep easily tonight. Learn what makes a Directed Acyclic Graph a DAG.

## What is a DAG?

A Directed Acyclic Graph (or *DAG*) is a special type of graph made up of nodes (also known as *vertices*), and edges, in which:

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all edges have a direction associated with them, and→

the graph as a whole contains no cycles (aka.*loops*).

The below figure illustrates a classic DAG, in which all nodes are connected by at least one directional edge, and all pathways lead to a single end-state.

## Uses of DAGs in Data Science

In data applications like HASH, DAGs are commonly used to illustrate:

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**data pipelines:**the decision and processing steps taken as data flows through a pipeline→

**schedules:**any system of tasks with ordering constraints (not just a data pipeline) can be illustrated with a DAG→

**dependency/citation graphs:**a list of dependencies or citations that allows the provenance of work to be tracked

## More information

In mathematical terms, DAGs are a specific subclass of **oriented graphs** (graphs without bidirectional edges). Ultimately though, you don’t have to understand the technical ins and outs of DAGs in order to utilize them as part of a data pipeline.

Modern data engineering tools such as hCore abstract away complexity through simple, easy-to-use interfaces that provide prompts and feedback, preventing the creation of malformed DAGs (for example, those which may inadvertently contain circular loops or cycles).

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