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Prefect turns everyday Python into production-grade workflows with zero boilerplate.

When you pair Prefect with pandas you get a versatile ETL toolkit:

  • Python supplies a rich ecosystem of connectors and libraries for virtually every data source and destination.
  • pandas gives you lightning-fast, expressive transforms that turn raw bits into tidy DataFrames.
  • Prefect wraps the whole thing in battle-tested orchestration: automatic retries, scheduling, and observability , so you don’t have to write reams of defensive code.

The result? You spend your time thinking about what you want to build, not how to keep it alive. Point this trio at any API, database, or file system and it will move the data where you need it while handling the messy details for you.

In this article you will:

  1. Extract JSON from the public Dev.to REST API.
  2. Transform it into an analytics-friendly pandas DataFrame.
  3. Load the result to a CSV – ready for your BI tool of choice.

This example demonstrates these Prefect features:

  • @task – wrap any function in retries & observability.
  • log_prints – surface print() logs automatically.
  • Automatic retries with back-off, no extra code.

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Rapid analytics from a public API

Your data team wants engagement metrics from Dev.to articles, daily. You need a quick, reliable pipeline that anyone can run locally and later schedule in Prefect Cloud.

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The Solution

Write three small Python functions (extract, transform, load), add two decorators, and let Prefect handle retries, concurrency, and logging. No framework-specific hoops, just Python the way you already write it.

For more background on Prefect’s design philosophy, check out our blog post: Built to Fail: Design Patterns for Resilient Data Pipelines

Watch as Prefect orchestrates the ETL pipeline with automatic retries and logging. The flow fetches multiple pages of articles, transforms them into a structured DataFrame, and saves the results to CSV. This pattern is highly adaptable - use it to build pipelines that move data between any sources and destinations:

  • APIs → Databases (Postgres, MySQL, etc.)
  • APIs → Cloud Storage (S3, GCS, Azure)
  • APIs → Data Warehouses (Snowflake, BigQuery, Redshift, etc.)
  • And many more combinations

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Code walkthrough

  1. Imports – Standard libraries for HTTP + pandas.
  2. fetch_page task – Downloads a single page with retries.
  3. to_dataframe task – Normalises JSON to a pandas DataFrame.
  4. save_csv task – Persists the DataFrame and logs a peek.
  5. etl flow – Orchestrates the tasks sequentially for clarity.
  6. Execution – A friendly if __name__ == "__main__" with some basic configurations kicks things off.
from __future__ import annotations

from pathlib import Path
from typing import Any, List

import httpx
import pandas as pd
from prefect import flow, task

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Extract – fetch a single page of articles

@task(retries=3, retry_delay_seconds=[2, 5, 15])
def fetch_page(page: int, api_base: str, per_page: int) -> list[dict[str, Any]]:
    """Return a list of article dicts for a given page number."""
    url = f"{api_base}/articles"
    params = {"page": page, "per_page": per_page}
    print(f"Fetching page {page} …")
    response = httpx.get(url, params=params, timeout=30)
    response.raise_for_status()
    return response.json()

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Transform – convert list[dict] ➜ pandas DataFrame

@task
def to_dataframe(raw_articles: list[list[dict[str, Any]]]) -> pd.DataFrame:
    """Flatten & normalise JSON into a tidy DataFrame."""
    # Combine pages, then select fields we care about
    records = [article for page in raw_articles for article in page]
    df = pd.json_normalize(records)[
        [
            "id",
            "title",
            "published_at",
            "url",
            "comments_count",
            "positive_reactions_count",
            "tag_list",
            "user.username",
        ]
    ]
    return df

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Load – save DataFrame to CSV (or print preview)

@task
def save_csv(df: pd.DataFrame, path: Path) -> None:
    """Persist DataFrame to disk then log a preview."""
    df.to_csv(path, index=False)
    print(f"Saved {len(df)} rows ➜ {path}\n\nPreview:\n{df.head()}\n")

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Flow – orchestrate the ETL with optional concurrency

@flow(name="devto_etl", log_prints=True)
def etl(api_base: str, pages: int, per_page: int, output_file: Path) -> None:
    """Run the end-to-end ETL for *pages* of articles."""

    # Extract – simple loop for clarity
    raw_pages: List[list[dict[str, Any]]] = []
    for page_number in range(1, pages + 1):
        raw_pages.append(fetch_page(page_number, api_base, per_page))

    # Transform
    df = to_dataframe(raw_pages)

    # Load
    save_csv(df, output_file)

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Run it!

python 01_getting_started/03_run_api_sourced_etl.py
if __name__ == "__main__":
    # Configuration – tweak to taste
    api_base = "https://dev.to/api"
    pages = 3  # Number of pages to fetch
    per_page = 30  # Articles per page (max 30 per API docs)
    output_file = Path("devto_articles.csv")

    etl(api_base=api_base, pages=pages, per_page=per_page, output_file=output_file)

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What just happened?

  1. Prefect registered a flow run and three task runs (fetch_page, to_dataframe, save_csv).
  2. Each fetch_page call downloaded a page and, if it failed, would automatically retry.
  3. The raw JSON pages were combined into a single pandas DataFrame.
  4. The CSV was written to disk and a preview printed locally (the flow’s log_prints=True flag logs messages inside the flow body; prints inside tasks are displayed in the console).
  5. You can view run details, timings, and logs in the Prefect UI.

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Key Takeaways

  • Pure Python, powered-up – Decorators add retries and logging without changing your logic.
  • Observability first – Each task run (including every page fetch) is logged and can be viewed in the UI if you have a Prefect Cloud account or a local Prefect server running.
  • Composable – Swap save_csv for a database loader or S3 upload with one small change.
  • Reusable – Import the etl flow and run it with different parameters from another flow.

Prefect lets you focus on data, not orchestration plumbing – happy ETL-ing! 🎉