How do I set up recurring pulls from a dataset API?

To set up recurring pulls from a dataset API, you usually need to combine:

1. A way to call the API
2. A scheduler to run the call on a timetable
3. Storage or processing for the results
4. Handling for pagination, retries, and auth refresh

Basic approach

1) Make the API request script

Write a small script that:

• authenticates to the API
• requests the dataset
• saves the response to a file/database
• handles pagination if the API returns data in pages

Example in Python:

import requests

API_URL = "https://api.example.com/dataset"
API_KEY = "your_api_key_here"

headers = {
    "Authorization": f"Bearer {API_KEY}"
}

response = requests.get(API_URL, headers=headers)
response.raise_for_status()

data = response.json()

with open("dataset.json", "w") as f:
    import json
    json.dump(data, f)

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2) Schedule it to run repeatedly

Option A: Cron (Linux/macOS)

Run it every day at 2 AM:

0 2 * * * /usr/bin/python3 /path/to/script.py

You can edit cron with:

crontab -e

Option B: Windows Task Scheduler

Create a task that:

• runs python.exe
• passes the script path as an argument
• triggers on your chosen schedule

Option C: Cloud scheduler

If you’re using cloud infrastructure:

• AWS: EventBridge + Lambda / ECS / Step Functions
• GCP: Cloud Scheduler + Cloud Run / Cloud Functions
• Azure: Logic Apps / Azure Functions timer trigger

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3) Handle common API needs

Pagination

If the API returns only part of the data at a time:

import requests

url = "https://api.example.com/dataset"
headers = {"Authorization": "Bearer your_api_key"}
all_data = []

while url:
    resp = requests.get(url, headers=headers)
    resp.raise_for_status()
    payload = resp.json()
    all_data.extend(payload["results"])
    url = payload.get("next")  # or however the API provides the next page

Retries

APIs can fail temporarily, so add retries with backoff.

Incremental pulls

If the dataset supports it, pull only new/changed records using:

• updated_since
• modified_after
• cursor tokens
• timestamps

Example:

params = {"updated_since": "2026-01-01T00:00:00Z"}

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4) Store the data somewhere

Depending on your goal:

• JSON/CSV file for simple batch downloads
• Database if you need querying
• Data warehouse for analytics
• Object storage like S3/GCS/Azure Blob for raw archives

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Best practices

• Keep API keys in environment variables or secrets manager
• Log success/failure and row counts
• Add alerting if a scheduled run fails
• Respect rate limits
• Use incremental syncs when possible

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If you want a no-code option

Many tools can do this with scheduling built in:

• Zapier / Make
• Airbyte / Fivetran
• n8n
• Talend / Matillion

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If you tell me:

• the API type (REST/GraphQL)
• your language (Python, JS, etc.)
• where you want the data to go
• how often to run it

…I can give you a ready-to-use setup.