@Scout With Dagster, during an ETL, is it possible to perform different transform and load steps depending on the data? For example, if we validate some data and it appears in one format, perform actions A and if it appears in any other format perform actions B. In other words, from asset “data_to_transform,” how do I pass data in format A to asset “format_A_data_asset” and data in format B to asset “format_B_data_asset”. Please let me know if this is an anti-pattern and there is a better way to do what I’m attempting to do.

Yes, with Dagster, it is possible to perform different transform and load steps depending on the data during an ETL process. You can set up conditional logic within your assets to handle different data formats and direct the data to appropriate downstream assets. In the case you described, you can have an asset

data_to_transform

that contains the logic to validate the data and determine its format. Based on the format, you can then pass the data to either

format_A_data_asset

or

format_B_data_asset

. This is not an anti-pattern; in fact, it's a common use case in data processing pipelines where the transformation logic may vary based on the characteristics of the input data. Here's a conceptual example based on the documentation:

python @asset def data_to_transform():     data = ...  # Your data extraction logic     return data  @asset def format_A_data_asset(data_to_transform):     if data_is_format_A(data_to_transform):         transformed_data = transform_format_A(data_to_transform)         return transformed_data     else:         return None  # Skip processing if data is not in format A  @asset def format_B_data_asset(data_to_transform):     if data_is_format_B(data_to_transform):         transformed_data = transform_format_B(data_to_transform)         return transformed_data     else:         return None  # Skip processing if data is not in format B

In this example,

data_to_transform

is an asset that extracts the data. The

format_A_data_asset

and

format_B_data_asset

assets take the data from

data_to_transform

as input and apply their respective transformation logic only if the data matches their expected format. If the data does not match the format, they return

None

or some other indication that they should not process the data. This approach allows you to create a flexible and maintainable ETL pipeline that can handle different data formats and direct them to the appropriate processing logic. Remember to handle the case where data does not match any expected format, and ensure that your pipeline can handle or log such occurrences appropriately.