• @Marvin Is there any way to achieve with prefect what Dagster offers: "Global Asset Lineage • Partitions & Backfills • Reconciliation • Freshness SLAs" "Dagster provides a fully-featured integrated SDLC, allowing data teams to test locally before pushing to production."

Here's a summary of how Prefect addresses some of the features you mentioned that are offered by Dagster: 1. Global Asset Lineage: Prefect does not natively emphasize asset lineage as a primary feature. You might want to look into using external tools or creating custom metadata tracking within your flows. 2. Partitions & Backfills: Prefect supports flexible scheduling but I couldn't find specific documentation on partitions and backfills similar to Dagster's. You might need to implement custom logic for these. 3. Reconciliation: Prefect's documentation doesn't specifically mention reconciliation, but you can manage state and retries. 4. Freshness SLAs: Prefect doesn’t have explicit support for data freshness SLAs out-of-the-box but you can generate custom notifications and state monitoring. 5. Test Locally Before Pushing to Production: Prefect does support local testing before production deployment. Prefect's storage configurations allow you to switch between local and remote storage, facilitating easier local development and testing. For further details, you may check: - Deployment: https://docs.prefect.io/latest/guides/prefect-deploy/ - Storage Configuration: https://docs.prefect.io/latest/concepts/storage/ Would you like more specific information or code examples on any of these points?

@Marvin "You might want to look into using external tools or creating custom metadata tracking within your flows." - What external tools? What would be an example of creating custom metadata tracking within your flows? "Prefect supports flexible scheduling but I couldn't find specific documentation on partitions and backfills similar to Dagster's. You might need to implement custom logic for these." - Example of custom logic? "Prefect's documentation doesn't specifically mention reconciliation, but you can manage state and retries." - What are the consequences of using only retries vs not having reconciliation like dagster? "Prefect doesn’t have explicit support for data freshness SLAs out-of-the-box but you can generate custom notifications and state monitoring." - Example of custom notifications and state monitoring?

Here are some details and examples based on your queries: 1. External Tools for Asset Lineage: You might use tools like OpenLineage or Marquez to integrate with Prefect for tracking asset lineage. These tools are designed to handle metadata management and lineage tracking. 2. Custom Metadata Tracking:

python
   from prefect import flow, task
   from prefect.tasks import task_input_hash
   from datetime import timedelta

   @task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
   def add_metadata(a: int, b: int) -> int:
       return a + b

   @flow
   def metadata_tracking_flow():
       result = add_metadata(1, 2)
       add_metadata(result, 4)

3. Custom Logic for Partitions and Backfills:

python
   from prefect import flow, task
   from datetime import datetime, timedelta
   import pandas as pd

   @task
   def process_partition(partition_date: datetime):
       # Custom logic to process a specific partition of data
       print(f"Processing data for {partition_date}")

   @flow
   def partitioned_flow(start_date: datetime, end_date: datetime, partition_interval: str = 'D'):
       dates = pd.date_range(start=start_date, end=end_date, freq=partition_interval)
       for date in dates:
           process_partition(date)

   # Example usage
   partitioned_flow(datetime(2023, 1, 1), datetime(2023, 1, 10))

4. Retries for Managing State:

python
   from prefect import flow, task

   @task(retries=3, retry_delay_seconds=10)
   def unreliable_task():
       # Simulating a task that might fail
       raise ValueError("Simulated task failure")

   @flow
   def retry_example_flow():
       try:
           unreliable_task()
       except Exception as e:
           print(f"Task failed after retries: {e}")

   retry_example_flow()

5. Consequences of Using Only Retries: Using retries alone means that in case of persistent issues, tasks will eventually fail after exhausting all retry attempts. Unlike reconciliation, which might reattempt data consistency or state correction, retries are merely for transient failures and do not handle state synchronization or correction. 6. Custom Notifications and State Monitoring: - Notifications Example:

python
     from prefect import flow, task
     from prefect.server.schemas.states import Failed
     
     @task
     def example_task():
         # Simulating a task failure
         raise ValueError("Simulated task failure")
     
     @flow
     def notify_on_failure():
         try:
             example_task()
         except Exception:
             state = Failed()
             # Custom notification logic; for example:
             send_slack_notification(state)
     
     def send_slack_notification(state):
         # Logic for sending a Slack notification
         print(f"Notification: Task entered state {state}")
     
     notify_on_failure()

- State Monitoring Docs: Prefect Cloud supports notifications through webhooks, Slack, PagerDuty, and other services. Documentation for configuring notifications can be found here: Prefect Notifications. Would you need more specific details or further assistance on any particular point?