How do I move API data into a database on a schedule?

A common way to do this is:

1. Pull data from the API on a schedule
2. Transform/clean it if needed
3. Insert or update it in your database
4. Repeat automatically

Basic architecture

• Scheduler: cron, Airflow, Prefect, Celery Beat, AWS EventBridge, GCP Cloud Scheduler, etc.
• ETL/ingestion job: script or service that calls the API and writes to DB
• Database: PostgreSQL, MySQL, MongoDB, etc.
• State tracking: store last sync time, page token, or cursor so you only fetch new data

Simple example in Python

1) A script that fetches API data and saves to PostgreSQL

import requests
import psycopg2
from datetime import datetime

API_URL = "https://api.example.com/items"
DB_CONFIG = {
    "host": "localhost",
    "dbname": "mydb",
    "user": "myuser",
    "password": "mypassword",
    "port": 5432,
}

def fetch_data():
    response = requests.get(API_URL, timeout=30)
    response.raise_for_status()
    return response.json()

def save_data(items):
    conn = psycopg2.connect(**DB_CONFIG)
    cur = conn.cursor()

    for item in items:
        cur.execute("""
            INSERT INTO items (id, name, updated_at)
            VALUES (%s, %s, %s)
            ON CONFLICT (id) DO UPDATE
            SET name = EXCLUDED.name,
                updated_at = EXCLUDED.updated_at
        """, (item["id"], item["name"], item["updated_at"]))

    conn.commit()
    cur.close()
    conn.close()

if __name__ == "__main__":
    data = fetch_data()
    save_data(data)
    print(f"Sync complete at {datetime.utcnow()}")

2) Run it on a schedule with cron

Open your crontab:

crontab -e

Add a job to run every hour:

0 * * * * /usr/bin/python3 /path/to/sync_script.py >> /var/log/api_sync.log 2>&1

If you want something more robust

Use a workflow tool if:

• you need retries
• multiple steps depend on each other
• you need monitoring and alerts
• jobs can take a long time

Good options:

• Airflow: great for complex pipelines
• Prefect: simpler than Airflow for many use cases
• Dagster: strong data pipeline tooling
• Celery Beat: good if you already use Celery

Important best practices

• Use incremental syncs if possible
  • fetch only records changed since last run
• Handle pagination
• Respect rate limits
• Add retries with backoff
• Use upserts to avoid duplicates
• Track failures and logs
• Store secrets securely
• Make it idempotent
  • rerunning the job should not corrupt data

If the API supports it

Prefer these patterns:

• updated_since parameter
• cursor-based pagination
• webhooks for near-real-time updates

Recommended setup for a small project

• Python script
• PostgreSQL
• cron
• upsert logic
• a table to store last_synced_at

If you want, I can give you:

1. a ready-to-run Python example,
2. a Node.js version, or
3. a cloud-based setup like AWS/GCP/Azure.