feat(docs): tutorial for writing a custom transformer

metadata-ingestion/README.md

@@ -948,7 +948,9 @@ sink:
 
 ## Transformations
 
-See the [transformers guide](./transformers.md).
+If you'd like to modify data before it reaches the ingestion sinks – for instance, adding additional owners or tags – you can use a transformer to write your own module and integrate it with DataHub.
+
+Check out the [transformers guide](./transformers.md) for more info!.
 
 ## Using as a library
 

metadata-ingestion/examples/transforms/custom_transform_example.py

@@ -1,3 +1,4 @@
+# see https://datahubproject.io/docs/metadata-ingestion/transformers for original tutorial
 from datahub.configuration.common import ConfigModel
 
 

metadata-ingestion/transformers.md

@@ -27,7 +27,7 @@ transformers:
 
 :::tip
 
-If you'd like to add more complex logic for assigning tags, you can use the more generic [`add_dataset_tags` transformer](./src/datahub/ingestion/transformer/add_dataset_tags.py), which calls a user-provided function to determine the tags for each dataset.
+If you'd like to add more complex logic for assigning tags, you can use the more generic add_dataset_tags transformer, which calls a user-provided function to determine the tags for each dataset.
 
 :::
 
@@ -47,12 +47,6 @@ transformers:
         - "urn:li:corpGroup:groupname"
 ```
 
-:::tip
-
-If you'd like to add more complex logic for assigning ownership, you can use the more generic [`add_dataset_ownership` transformer](./src/datahub/ingestion/transformer/add_dataset_ownership.py), which calls a user-provided function to determine the ownership of each dataset. See below for a guide on how to set this up.
-
-:::
-
 ## Writing a custom transformer from scratch
 
 In the above couple of examples, we use classes that have already been implemented in the ingestion framework. However, it’s common for more advanced cases to pop up where custom code is required, for instance if you'd like to utilize conditional logic or rewrite properties. In such cases, we can add our own modules and define the arguments it takes as a custom transformer.
@@ -188,7 +182,9 @@ def transform_one(self, mce: MetadataChangeEventClass) -> MetadataChangeEventCla
 
 ### Installing the package
 
-Now that we've defined the transformer, we can set up the package to install it and make it visible to the ingestion framework. To do so, create a `setup.py` in the same directory:
+Now that we've defined the transformer, we need to make it visible to DataHub. This can be done by making sure the Python file is available as a local import.
+
+Alternatively, create a `setup.py` in the same directory as our transform script to make it visible globally. After installing this package (e.g. with `python setup.py` or `pip install -e .`), our module will be installed and importable as `custom_transform_example`.
 
 ```python
 from setuptools import find_packages, setup
@@ -198,14 +194,12 @@ setup(
     version="1.0",
     packages=find_packages(),
     # if you don't already have DataHub installed, add it under install_requires
-		# install_requires=["acryl-datahub"]
+	# install_requires=["acryl-datahub"]
 )
 ```
 
 ### Running the transform
 
-After installing this package (e.g. with `python setup.py` or `pip install -e .`), our module will be installed and importable.
-
 ```yaml
 transformers:
   - type: "custom_transform_example.AddCustomOwnership"