diff --git a/docs/source/about-dag.md b/docs/source/about-dag.md
new file mode 100644
index 000000000..9978f1d1c
--- /dev/null
+++ b/docs/source/about-dag.md
@@ -0,0 +1,69 @@
+# About the Merlin Directed Acyclic Graph
+
+Merlin uses a directed acyclic graph (DAG) to represent operations on data such as filtering or bucketing and to represent operations in a recommender system such as creating an ensemble or filtering candidate items during inference.
+
+Understanding the Merlin DAG is helpful if you want to develop your own operator (Op) or building a recommender system with Merlin.
+
+## DAG Terminology
+
+node
+: A node in the DAG is a group of columns and at least one _operator_.
+  The columns are specified with a _column selector_.
+  A node has an _input schema_ and an _output schema_.
+  Resolution of the schemas is delayed until you run `fit` or `transform` on a dataset.
+
+column selector
+: A column selector specifies the columns to select from a dataset using column names or _tags_.
+
+operator
+: An operator performs a transformation on data and return a new _node_.
+  The data is identified by the _column selector_.
+  Some simple operators like `+` and `-` add or remove columns.
+  More complex operations are applied by shifting the operators onto the column selector with the `>>` notation.
+
+schema
+: A Merlin schema is metadata that describes the columns in a dataset.
+  Each column has its own schema that identifies the column name and can specify _tags_ and properties.
+
+tag
+: A Merlin tag categorizes information about a column.
+  Adding a tag to a column enables you to select columns for operations by tag rather than name.
+
+  For example, you can add the `USER` and `ITEM` tags to columns.
+  Modeling and inference operations can use that information to act accordingly on the dataset.
+
+## Syntax and Sample Code
+
+The following code block shows the typical syntax for building a workflow that operates on DAG components.
+
+```{rubric} Syntax
+```
+
+```python
+result = [column_selector, ...] >> op1 >> op2 >> ...;
+```
+
+Starting with the `column_selector`, the brackets group one or more column selectors that identify columns in the input data.
+
+The `op1` and `op2` represent operators.
+When an operator performs its operation on the input data, the operator returns a node.
+
+The `result` object is the graph.
+It contains the sequence of operations to perform.
+
+```{rubric} Sample Code
+```
+
+```python
+item_features = (
+    ["item_category", "item_shop", "item_brand"] >> Categorify(dtype="int32") >> TagAsItemFeatures()
+)
+```
+
+In the sample code, the column selector is created by specifying the item-related column names.
+
+The {py:class}`~nvtabular.ops.Categorify` operator transforms the categorical features into unique integer values, adds the {py:attr}`~merlin.schema.Tags.CATEGORICAL` tag, and returns a node.
+
+The {py:class}`~nvtabular.ops.TagAsItemFeatures` operator applies the {py:attr}`~merlin.schema.Tags.ITEM` tag and returns a node.
+
+When the `item_features` variable is included in a transformation and applied to input data, it will traverse the nodes in order and apply the data transformation and tagging.
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 779dc587d..a51bd04eb 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -118,6 +118,14 @@
 
 autosummary_generate = True
 
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "merlin-core": ("https://nvidia-merlin.github.io/core/main", None),
+    "merlin-systems": ("https://nvidia-merlin.github.io/systems/main", None),
+    "merlin-models": ("https://nvidia-merlin.github.io/models/main", None),
+    "NVTabular": ("https://nvidia-merlin.github.io/NVTabular/main", None),
+}
+
 copydirs_additional_dirs = ["../../examples/", "../../README.md"]
 
 copydirs_file_rename = {
diff --git a/docs/source/toc.yaml b/docs/source/toc.yaml
index aa384075a..3226c5806 100644
--- a/docs/source/toc.yaml
+++ b/docs/source/toc.yaml
@@ -46,5 +46,7 @@ subtrees:
                     title: Deploy the HugeCTR Model with Triton
                   - file: examples/scaling-criteo/04-Triton-Inference-with-Merlin-Models-TensorFlow.ipynb
                     title: Deploy the TensorFlow Model with Triton
+      - file: about-dag.md
+        title: Merlin DAG
       - file: containers.rst
       - file: support_matrix/index.rst
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