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When teams look for Airtable ETL, they’re usually trying to pull data from APIs, shape it to fit Airtable, and keep it up to date over time.
In practice, this breaks down quickly. APIs return messy data, Airtable expects clean records, and one-off imports or general-purpose tools don’t scale well as data changes.
Data Fetcher is built specifically for running ETL pipelines inside Airtable — handling extraction, transformation, and loading in a predictable way that holds up over time.
This tutorial walks through how to build a simple but reliable ETL pipeline directly inside Airtable using Data Fetcher.
The process follows the standard ETL pattern:
Start by creating a new request in Data Fetcher inside your Airtable base.
You can extract data from:
REST APIs
GraphQL APIs
CSV or JSON endpoints
Internal or private services
During this step you configure:
the request URL
authentication (API keys, headers, OAuth, etc.)
pagination, if the API returns large result sets
For example, you might pull:
ad performance data from an ads API
payments or subscriptions from a billing system
product or usage data from an internal service
Data Fetcher handles pagination and retries, so the extraction step continues to work as data volumes grow.
Most APIs return data as nested JSON, which doesn’t map cleanly to Airtable tables.
Data Fetcher automatically converts JSON responses into tabular records, creating one row per item and one column per field. You can then adjust and refine that structure before the data is written to your table.
Common transformations include:
selecting only the fields you need
renaming fields to match your Airtable schema
flattening nested objects into columns
filtering out incomplete or irrelevant records
converting values into Airtable-friendly types
For example, an API might return a deeply nested JSON object, but your Airtable table expects a flat set of columns. Transforming the data before loading keeps your base clean and predictable.
Once the data is structured correctly, you can load it into Airtable.
In this step, you decide how incoming data should behave over time. This is what separates ETL from one-off imports.
You can configure your pipeline to:
create new records
update existing records using a unique ID
de-duplicate records automatically
append new rows for time-series data
update linked records across tables
For example, when syncing data daily, you might update existing records based on an external ID rather than creating duplicates each time the pipeline runs.
ETL pipelines are designed to run repeatedly, not just once.
After your request is working, you can schedule it to run automatically:
every few minutes
hourly
daily
or on demand
This keeps your Airtable data in sync with external systems without manual imports or scripts.
For reporting or dashboards, scheduled ETL pipelines ensure the data in Airtable is always current.
Teams use Airtable ETL pipelines to keep external data in sync without manual imports or brittle scripts. Here are a few common examples.
Pull campaign data from platforms like Google Ads or Facebook Ads into Airtable on a schedule, transform it into reporting-ready rows, and keep metrics up to date automatically.
Fetch payment or subscription data from Stripe, map it to Airtable records, and update existing rows as customers upgrade, cancel, or renew.
Use Airtable as a lightweight backend or reporting layer by syncing data from internal services or custom APIs into structured tables.
Send Airtable data to AI APIs for enrichment or extraction, then write structured results back into your base.
ETL stands for Extract, Transform, Load — pulling data from a source, reshaping it, and loading it into a destination. Data Fetcher lets you run complete ETL workflows directly inside Airtable by extracting data from APIs, transforming it before it’s written, and loading it into your base on a schedule.
Teams rely on Data Fetcher to import external data into Airtable — without scripts or manual work.
"Having Data Fetcher with Airtable unlocks so much potential when working with APIs. No more fussing with pagination, transforming XML or JSON"
Mark Campos
Chief Product Officer, XRay Tech, Inc.
"I set up and scheduled our sync jobs nine months ago when starting with the product, and they have just worked reliably running every day since."
Jason Samuels
IT Operations Manager, American Craft Council
"Need data pumped into Airtable? Data Fetcher is the solution. We have no API or data experience, yet our team can seamlessly integrate external data."
Thomas Coiner
CEO, ProU Sports
