snowplow-signals 0.4.5rc3

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Snowplow Signals Python SDK

The Snowplow Signals Python SDK enables you to interact with the Snowplow Signals Profile API. It provides a simple interface to define, deploy, and retrieve user attributes for personalization.

Installation

pip install snowplow-signals

Quickstart

from snowplow_signals import Signals, SignalsSandbox, Attribute, Event, StreamAttributeGroup, domain_sessionid

# Initialize the SDK with BDP authentication (default)
signals = Signals(
    api_url="API_URL",
    api_key="API_KEY",
    api_key_id="API_KEY_ID",
    org_id="ORG_ID",
)

# Or initialize with SANDBOX authentication
signals = SignalsSandbox(
    api_url="API_URL",
    sandbox_token="YOUR_SANDBOX_TOKEN",
)

# Define an attribute
page_view_count = Attribute(
    name="page_view_count",
    type="int32",
    events=[
        Event(
            vendor="com.snowplowanalytics.snowplow",
            name="page_view",
            version="1-0-0",
        )
    ],
    aggregation="counter"
)

# Create and deploy a view
stream_attribute_group = StreamAttributeGroup(
    name="my_attribute_group",
    version=1,
    attribute_key=domain_sessionid,
    attributes=[page_view_count],
)
signals.publish([stream_attribute_group])

# Retrieve attributes
response = signals.get_group_attributes(
    name="my_attribute_group",
    version=1,
    attribute_key="domain_sessionid",
    attributes=["page_view_count"],
    identifier="abc-123",
)

Key Features

• Define attributes based on Snowplow events
• Create attribute groups for related attributes
• Deploy attribute groups to the Profile API
• Retrieve real-time user attributes

DBT Project Generation

The SDK includes functionality to automatically generate DBT projects for Snowplow data. This makes it easy to set up and maintain DBT projects that work with Snowplow data.

Using the SDK

from snowplow_signals import Signals

# Initialize the signals client
signals = Signals(api_url="https://your-api-url.com")

# Initialize a DBT project
signals.batch_autogen.init_project(
    repo_path="path/to/your/repo",
    target_type="snowflake" # or bigquery / databricks
    project_name="your_project_name"  # Optional
)

# Generate DBT models
signals.batch_autogen.generate_models(
    repo_path="path/to/your/repo",
    target_type="snowflake" # or bigquery / databricks
    project_name="your_project_name",  # Optional
    update=True  # Whether to update existing files
)

Using the Command Line

The SDK also includes a command-line interface for DBT project generation. To make your workflow smoother, you can set up your API credentials as environment variables. This way, you won't need to type them in every command:

# For BDP authentication (default)
export SNOWPLOW_API_URL="YOUR_API_URL"
export SNOWPLOW_API_KEY="YOUR_API_KEY"
export SNOWPLOW_API_KEY_ID="YOUR_API_KEY_ID"
export SNOWPLOW_ORG_ID="YOUR_ORG_ID"
export SNOWPLOW_REPO_PATH="./my_snowplow_repo"

# For SANDBOX authentication
export SNOWPLOW_API_URL="YOUR_API_URL"
export SNOWPLOW_AUTH_MODE="sandbox"
export SNOWPLOW_SANDBOX_TOKEN="YOUR_SANDBOX_TOKEN"
export SNOWPLOW_REPO_PATH="./my_snowplow_repo"

# Initialize a DBT project
snowplow-batch-engine init --repo-path=path/to/your/repo --target-type=snowflake [--project-name=your_project_name]

# Generate DBT models
snowplow-batch-engine generate --repo-path=path/to/your/repo --target-type=bigquery [--project-name=your_project_name] [--update]