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# API-sourced ETL
> Build a small ETL pipeline that fetches JSON from a public API, transforms it with pandas, and writes a CSV – all orchestrated by Prefect.
View on GitHub
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](https://docs.prefect.io/v3/develop/write-tasks#retries), [scheduling](https://docs.prefect.io/v3/deploy/index#workflow-scheduling-and-parametrization), and [observability](https://docs.prefect.io/v3/develop/logging#prefect-loggers) , 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](https://dev.to/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`](https://docs.prefect.io/v3/develop/write-tasks#write-and-run-tasks) – wrap any function in retries & observability.
* [`log_prints`](https://docs.prefect.io/v3/develop/logging#configure-logging) – surface `print()` logs automatically.
* Automatic [**retries**](https://docs.prefect.io/v3/develop/write-tasks#retries) with back-off, no extra code.
### 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.
### The Solution
Write three small Python functions (extract, transform, load), add two decorators, and
let Prefect handle [retries](https://docs.prefect.io/v3/develop/write-tasks#retries), [concurrency](https://docs.prefect.io/v3/develop/task-runners#configure-a-task-runner), and [logging](https://docs.prefect.io/v3/develop/logging#prefect-loggers). 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](https://www.prefect.io/blog/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
## 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.
```python theme={null}
from __future__ import annotations
from pathlib import Path
from typing import Any
import httpx
import pandas as pd
from prefect import flow, task
```
***
## Extract – fetch a single page of articles
```python theme={null}
@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()
```
***
## Transform – convert list\[dict] ➜ pandas DataFrame
```python theme={null}
@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
```
***
## Load – save DataFrame to CSV (or print preview)
```python theme={null}
@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")
```
***
## Flow – orchestrate the ETL with optional concurrency
```python theme={null}
@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)
```
## Run it!
```bash theme={null}
python 01_getting_started/03_run_api_sourced_etl.py
```
```python theme={null}
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)
```
## 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.
## 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! 🎉
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