juana
01/03/2024, 3:02 AMMarvin
01/03/2024, 3:03 AMNate
01/03/2024, 3:04 AMMarvin
01/03/2024, 3:04 AMNate
01/03/2024, 3:04 AMMarvin
01/03/2024, 3:05 AMpython
from prefect import flow, task
from prefect.artifacts import create_link, create_markdown
@task
def generate_data():
# Your logic to generate data
data = {"example": "data"}
return data
@task
def process_data(data):
# Your logic to process data
processed_data = f"Processed: {data}"
return processed_data
@flow
def my_flow():
data = generate_data()
processed_data = process_data(data)
# Create a Markdown artifact
create_markdown("# This is a Markdown artifact\nThis flow processed the following data:\n```\n" + str(processed_data) + "\n```")
# Suppose you have a link to a dashboard or external resource
dashboard_url = "<http://example.com/dashboard>"
# Create a link artifact
create_link(dashboard_url)
# Run the flow
my_flow()
In this code example:
- The generate_data
task simulates generating some data.
- The process_data
task simulates processing that data.
- In the my_flow
flow, after processing the data, we create a Markdown artifact using create_markdown
, which contains a description and the processed data.
- We also create a link artifact using create_link
, which could, for example, point to a dashboard related to the flow.
These artifacts will be stored and rendered in the Prefect UI, making it easy to monitor and share information with your team or stakeholders.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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