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Registering Primary Data Sources

All features and labels originate from a set of initial data sources. These sources can be streams, files, or tables. A feature can simply be a single field of a data source. More commonly, it’s created via a set of transformations from one or multiple sources.

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Files

In the case of Offline Stores that work with a file system, the primary data may be a file or a set of files. Even when the Offline Store is a database, the primary data may be files that should be copied into a table in the Offline Store.

s3.register_file(
    name = "titanic",
    variant = "kaggle",
    description = "The Titanic Dataset from Kaggle",
    path = "bucket/kaggle/titanic.csv",
)

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Tables

In the case of an Offline Store, the primary data may be a table that already exists. In this case, we can register the table with Featureform.

titanic = postgres.register_table(
    name = "titanic",
    variant = "kaggle",
    description = "The Titanic Dataset from Kaggle",
    table = "Titanic", # This is the table's name in Postgres
)

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Streams

Streaming support in Enterprise Featureform. Book a call with us today!

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Defining transformations

There are two supported transformation types: SQL and Dataframes. Not all providers support all transformation types. For example, a dataframe transformation cannot currently be run on Snowflake. Each transformation definition also includes a set of metadata like its name, variant, and description.

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SQL

SQL transformations are defined by a decorated python function that returns a templated SQL string. The decorator specifies the necessary metadata, and the SQL string has its table names replaced with templated source names and versions.

@postgres.register_transformation(variant="quickstart")
def fare_per_family_member():
    """ The average fare paid per family member in a family
    """
    return "SELECT PassengerId, Fare / Parch FROM {{titanic.kaggle}}"

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Dataframes

Dataframe are defined by a decorated python function that takes a set of input dataframes and returns an output dataframe. The decorator specifies the necessary metadata and inputs, then the function outlines the transformation logic.

@spark.register_transformation(
    variant="quickstart",
    inputs=[("titanic", "kaggle")],
)
def fare_per_family_member(titanic):
    """ The average fare paid per family member in a family
    """
    titanic["Fare/Parch"] = titanic["Fare"] / titanic["Parch"]
    return titanic[["PassengerId", "Fare/Parch"]]

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Chaining transformations

Transformations can be chained together, where one transformation is the input into another transformation. Transformations will wait for any dependent transformations to complete successfully before running.

@postgres.register_transformation(variant="quickstart")
def survival_first_class():
    """ The age and survival status of first class passengers
    """
    return "SELECT age, survival FROM {{titanic.kaggle}} WHERE pclass='1st'"

@postgres.register_transformation(variant="quickstart")
def average_age_survival_first_class():
    """ The average survivability of first class passengers by age
    """
    return "SELECT age, AVG(survival) FROM {{survival_first_class.quickstart}} GROUP BY age"

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Checking status

Since some registrations may take longer than other depending on size of dataset and complexity of a query, the Featureform API has the ability to check the status of any resource programatically.

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Fetching the resource

import featureform as ff
client = ff.ResourceClient(host)

source = client.get_source("name", "variant")
training_set = client.get_training_set("name", "variant")
label = client.get_label("name", "variant")
feature = client.get_feature("name", "variant")

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Checking the resource status

import featureform as ff

training_set = client.get_training_set("name", "variant")
print(training_set.status == "READY")
print(training_set.get_status() == ff.ResourceStatus.READY)
print(training_set.is_ready())