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Updated the docs: Using pipeline.default_schema.toprettyyaml() #1660

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Aug 14, 2024
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Updated the docs: Using pipeline.default_schema.toprettyyaml() #1660

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53ebc76
Updated the documentation
dat-a-man Aug 3, 2024
3869bbf
Updated for the example
dat-a-man Aug 3, 2024
86d9f7b
Updated as per comments.
dat-a-man Aug 14, 2024
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24 changes: 23 additions & 1 deletion docs/website/docs/general-usage/schema.md
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Expand Up @@ -352,8 +352,30 @@ load_info = pipeline.run(source_data)
```
This example iterates through MongoDB collections, applying the complex [data type](schema#data-types) to a specified column, and then processes the data with `pipeline.run`.

## Export and import schema files
## View and print the schema
To view and print the default schema in a clear YAML format use the command:
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```py
pipeline.default_schema.to_pretty_yaml()
```
This can be used in a pipeline as:

```py
# Create a pipeline
pipeline = dlt.pipeline(
pipeline_name="chess_pipeline",
destination='duckdb',
dataset_name="games_data")

# Run the pipeline
load_info = pipeline.run(source)

# Print the default schema in a pretty YAML format
print(pipeline.default_schema.to_pretty_yaml())
```
This will display a structured YAML representation of your schema, showing details like tables, columns, data types, and metadata, including version, version_hash, and engine_version.

## Export and import schema files

Please follow the guide on [how to adjust a schema](../walkthroughs/adjust-a-schema.md) to export and import `yaml`
schema files in your pipeline.
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