Spark

Featureform supports Spark as an Offline Store.

This means that Featureform can handle all flavors of Spark using S3, GCS, Azure Blob Store or HDFS as a backing store.

Common use cases of Featureform with Spark include:

Spark with EMR

Spark with Databricks

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Understanding The Different Flavors of Spark

Spark is a powerful, open-source general computing framework developed for large-scale data processing.

Databricks is a managed data and analytics platform developed on top of Spark.

Both Spark and Databricks can be self-hosted in Kubernetes and other non-cloud implementations, as well as hosted with popular cloud providers such as AWS, Azure, GCP and on Databricks.

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Transforming Data

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Transformation Sources

Using Spark and a file store (GCS, Azure Blob Storage, S3, HDFS) as an Offline Store, you can define new transformations via SQL and Spark DataFrames. Using either these transformations or pre-existing files in your file store, a user can chain transformations and register columns in the resulting tables as new features and labels.

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Training Sets and Inference Store Materialization

Any column in a preexisting table or user-created transformation can be registered as a feature or label. These features and labels can be used, as with any other Offline Store, for creating training sets and inference serving.

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Dataframe Transformations

Using Spark with Featureform, a user can define transformations in SQL like with other offline providers.

transactions = spark.register_file(
   name="transactions",
   variant="kaggle",
   owner="featureformer",
   file_path="<insert_file_path_here", )  @spark.sql_transformation() def max_transaction_amount(): """the average transaction amount for a user """ return "select customerid as user_id, max(transactionamount) " \ "as max_transaction_amt from {{transactions.kaggle}} group by user_id"< code></insert_file_path_here",>

In addition, registering a provider via Spark allows you to perform DataFrame transformations using your source tables as inputs.

@spark.df_transformation(
   inputs=[("transactions", "kaggle")], variant="default")
def average_user_transaction(df):
   from pyspark.sql.functions import avg
   df.groupBy("CustomerID").agg(avg("TransactionAmount").alias("average_user_transaction"))
   return df