Connect Google Maps to Airtable
Turn a list of addresses into latitude and longitude, or coordinates back into addresses, with the parsed components split into their own columns. Geocoding only — this is not a Places or routing connector.
What you can pull from Google Maps
Google Maps exposes 1 object you can pull, each as its own job. Every run pulls the records into a flat table, so nested fields arrive as ordinary columns that Airtable can sort, filter and total without further work.
Geocoding results
One row per input. The input address is echoed back verbatim so the frame can be joined onto whatever you sent, and the address components are split into their own columns rather than left as one string.
| Column | Type | Notes |
|---|---|---|
| input_address | string | Echoed verbatim, so results join back onto your input |
| status | string | OK or ZERO_RESULTS are normal per-row answers, not failures |
| formatted_address | string | — |
| latitude | float | — |
| longitude | float | — |
| place_id | string | — |
| location_type | string | ROOFTOP, RANGE_INTERPOLATED, GEOMETRIC_CENTER or APPROXIMATE |
| locality | string | — |
| postal_code | string | — |
| administrative_area_level_1 | string | — |
| country | string | — |
| country_code | string | — |
| partial_match | boolean | Google matched something, but not confidently |
Options you can set
- mode
- 'forward' geocodes addresses into coordinates; 'reverse' turns lat/lng pairs into addresses — defaults to
forward - components
- Component filter, e.g. country:GB|postal_code:SW1. Narrows results without making the address more specific
- region
- ccTLD region bias, e.g. gb - influences ambiguous place names
- language
- Language for returned addresses
- rate_per_second
- Outbound request pacing, up to 50 — defaults to
10 - on_row_error
- fail_fast, or continue past a transient per-row failure — defaults to
fail_fast
How it lands in Airtable
Airtable accepts append, upsert writes. Upsert is the one that matters on a schedule: it merges on a key you choose, so matching rows are updated in place and only genuinely new records are created. Append instead, and a daily job multiplies your data.
Upsert merges on up to three key fields. Matching records are updated in place; only genuinely new rows are created.
- At most 3 merge fields - Airtable rejects more.
- Writes go 10 records per request, at 4 requests per second by default (Airtable’s own ceiling is 5) - roughly 2,400 records a minute.
- typecast is on by default, letting Airtable coerce strings into select, number and date fields rather than rejecting the write.
- unknown_fields defaults to error, so a renamed source column fails loudly instead of silently dropping data. Set it to skip if you would rather drop.
Authentication
Google Maps authenticates with an API key held as a stored connection, so it never appears in a spreadsheet cell, a formula, or a shared copy of a sheet.
Keeping runs incremental
Not for this source: Each run geocodes the inputs it is given. Where that is expensive, narrow the job itself and run it less often.
Pair a narrowed read with an upsert write and a re-run costs almost nothing: the rows it already knows are updated, and nothing is duplicated. That combination is what makes a frequent schedule affordable.
Limits and pacing
These are the provider-side ceilings that shape a schedule, not ours. Knowing them up front is the difference between a job that runs quietly every morning and one that starts failing the week your data grows.
| Limit | Value | Applies to |
|---|---|---|
| Inputs per run About 25 seconds at the default pacing, and it keeps the per-run bill bounded on an API that charges per request. | 250 | Google Maps |
| Request pacing Defaults to 10. | up to 50/second | Google Maps |
Common gotchas
Most of what goes wrong with a scheduled sync is not a bug — it is a detail of how one side behaves that nobody wrote down. These are the ones that come up for this pair.
ZERO_RESULTS is an answer, not an error
An address Google cannot place returns a row with status ZERO_RESULTS rather than failing the run. Filter on status before you trust the coordinates.
partial_match means "close enough, probably"
Google matched something but was not confident. On a list of user-typed addresses these are the rows worth reviewing by hand.
Capped at 250 inputs per run
For a larger backlog, batch it across scheduled runs rather than expecting one job to geocode everything.
Do not key an upsert on email
People change their email address. When they do, upsert cannot match, so it creates a second record - the exact duplicate you were trying to prevent, plus a stale one. Use a stable ID from the source system.
The key field has to be written
If you restrict which columns get written and the key field is not among them, there is nothing to match on and every row is treated as new.
A row that is a fact per period needs a compound key
For a daily history, one row is one entity per day. Key on the entity alone and each day overwrites the last, leaving no history at all.
Set it up in four steps
- 1
Connect Google Maps
Your own Google Maps API key, stored as a connection. Billing and quota are yours, on your Google Cloud project.
- 2
Connect Airtable
Connect your Airtable account once, then pick a base and table.
- 3
Shape the data
Select the columns you want, filter rows, cast types and add computed fields. Everything else is dropped before it reaches the destination.
- 4
Schedule it
Run once, or on a cron. Every run refreshes Airtable with the latest Google Maps data.
Do I need an add-on or extension for this?
No. The job runs server-side and writes into Airtable through its API, so there is nothing installed in the destination itself. It keeps running when nobody has the file open, and a copy of the file does not need anything installed to work.
How often does the data refresh?
On whatever schedule you set with a cron expression — hourly, daily, or a specific time on specific days. Each run pulls the latest from Google Maps.
Do I need to write any code?
No. You connect both sides, map the fields in a wizard and set a schedule. Computed fields accept small expressions — abs, round, min, max, len — but there is nothing to host or maintain.
Who pays for the geocoding?
You do, on your own Google Cloud project, with your own key. Maps charges per request, which is why a run is capped at 250 inputs rather than being allowed to run away.
Can it look up businesses or directions?
No. This is the Geocoding API only - addresses to coordinates and back. Places and routing are deliberately out of scope.
How do I stop duplicate records appearing?
Use upsert rather than append, and key it on a stable identifier from the source. Append creates new records every run, so a daily job leaves seven copies of every row by the end of the week.
How fast can it write?
Airtable accepts 10 records per request, and writes are paced at 4 requests a second by default against a ceiling of 5 - roughly 2,400 records a minute. That is comfortable for thousands of rows and worth planning around for hundreds of thousands.
Is the Google Maps connector free to use?
You can connect Google Maps and start syncing on the free plan.
Other sources into Airtable
Related reading
Airtable upsert: stop creating duplicate records
Appending on every run turns a tidy base into six copies of every row. Here is how upsert works in Airtable, and how to choose a merge key that holds up.
Export Stripe data to Google Sheets
Stripe's dashboard exports are manual and stale the moment you download them. Here is how to keep customers, invoices and charges fresh in a sheet instead.
Import a CSV from a URL into Airtable
Point a job at a CSV link and it re-reads it on your schedule — no download, no manual import. Here is the setup, and the field-type trap to avoid first.