Exploring Resources with Dataframes

@spark.df_transform(inputs=[upstream_data])
def transformation_name(upstream):
  ...
client.dataframe(transformation_name)

In scenarios where the dataset is sizable enough to exceed memory limits or when a sample suffices, you can include the rows parameter to restrict the number of pulled rows.

client.dataframe(transformation_name, rows=10000)

Fetching training sets as dataframes is also feasible. In this case, Featureform’s serving API equips the training set object with a .dataframe() method.

client.training_set("name", "variant").dataframe()

These mechanisms offer an intuitive and efficient approach to delve into your data using the familiar Dataframe APIs.

Streaming Data: Real-time Updates Dataframe and SQL Transformation Support

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