By Chris Davidson, founder of yForest · Updated September 26, 2026
A manually maintained planning sheet is often the least reliable input to a Postgres-backed app, not because the data's wrong, but because getting it there means someone remembering to export and import it. Data Sync removes the remembering part.
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Quick answer: Add Google Sheets and PostgreSQL as connections, open Pipelines, Build, New Sync, and map the sheet's columns to a Postgres table. Use Upsert with a real unique key if the sheet gets edited in place, then save it as a scheduled job. Pipelines tier.
Most teams have one: a planning sheet, a manually maintained exceptions list, a headcount tracker, that started as a scratch document and slowly became something an actual application or report depends on. Nobody set out to build production infrastructure on a spreadsheet, but once a Postgres-backed tool needs that data, someone has to get it there reliably, not by copy-pasting it in when they remember.
An operations team keeps a manually updated exceptions sheet, accounts that should be excluded from an automated billing run, with columns account_id, reason, and added_by:
Target table: billing.exceptions Columns: account_id, reason, added_by, added_at
Because entries get added and occasionally removed by hand, set MODE to Upsert and MATCH ON to account_id, assuming each account only appears once. QueryFlow handles the upsert into Postgres on that key, so a note added to an existing exception updates the row instead of creating a duplicate. Schedule it hourly, well ahead of whenever the billing run actually checks the table.
A manually maintained sheet gets edited in place more often than a system-generated export does. Insert alone would pile up a new row every run even when nothing changed, or duplicate a corrected entry. Upsert with a real unique key avoids both, provided the key actually is unique, which is worth double-checking on a sheet humans edit directly.
Data Sync's Upsert mode adds and updates rows; it doesn't delete a Postgres row just because it disappeared from the sheet. If an account gets removed from the exceptions list and should stop being excluded, that removal needs a separate cleanup step, a periodic query that removes rows in Postgres no longer present in the sheet, since Upsert alone won't infer a deletion from an absence.
It doesn't watch the sheet for a live edit and sync that instant, it's a scheduled job. For a billing exceptions list, an hourly lag is usually fine; for something that genuinely needs to reflect a change within seconds, this isn't the right tool, and a purpose-built real-time integration is worth the extra setup instead.
A sheet that feeds a billing exclusion list is exactly the kind of document where uncontrolled edit access becomes a real problem, not a hypothetical one, since anyone with edit rights can change what gets billed. Restricting the sheet's editors to the specific people responsible for it, and treating the Postgres table as downstream of that access control rather than a place to double-check who changed what, keeps the responsibility where it belongs.
Because Upsert only shows a row count in the job history, not which specific rows changed, a sensitive table like this benefits from an added_at or updated_at column in the mapping, even if the sheet itself doesn't naturally track it well. A timestamp lets you query for what changed since the last run, rather than relying on the sheet's own edit history, which Google Sheets does track but isn't queryable from Postgres.
A deleted or moved Google Sheet breaks the connection at the next scheduled run, and depending on how quickly that's noticed, the billing process downstream could start running without any exceptions applied at all, silently, which is a worse failure mode than an obvious error. Pairing this job with a failure notification, so a broken connection surfaces immediately rather than being discovered when someone asks why an excluded account got billed, matters more here than on a lower-stakes sync.
For a list with real financial consequences, some teams add a review column, a second person's initials confirming an addition before it's considered live, rather than trusting a single edit to the sheet. That's a process decision made in the sheet itself, not a QueryFlow feature, but it's worth building the source query to only pull rows marked as reviewed if that level of control matters for this particular list.
A live, on-demand API call to Google Sheets every time the billing run executes would technically remove the lag a scheduled sync introduces, but it also means the billing process now has a hard runtime dependency on Google's API being reachable and fast at exactly the moment billing runs. Decoupling the two, syncing on a schedule into Postgres and letting billing read from the local table, trades a small freshness lag for not making billing's reliability depend on an external API's uptime.
Once a spreadsheet feeds a billing process, it stops being a casual document even if it still looks like one. Naming conventions, access control, and a designated owner all matter more than they would for a sheet nobody downstream depends on, and it's worth treating those decisions with the same seriousness as any other piece of production infrastructure, even though the interface is still just a spreadsheet.
Fivetran's pricing page (September 26, 2026) meters Google Sheets as a standard connector, 500,000 MAR free, then a $5 base charge per connection between 1 and 1,000,000 MAR, usage above that behind a quote. Coefficient targets a different use case, spreadsheet-to-spreadsheet and BI-tool refreshes, not a database target like Postgres, so it doesn't directly compete on this specific pair. Either way, a hand-maintained exceptions sheet with a few hundred rows costs nothing extra under QueryFlow's flat $199.99-a-year Pipelines price.
Run Dry Run and check the preview against what the sheet currently shows. After a real run, check the job's history for rows synced with no errors.
| If you see | Fix |
|---|---|
| No Postgres connections available | Add a Postgres connection under Databases first. |
| Duplicate key values in source | The MATCH ON column isn't actually unique in the sheet; add another column to the key or clean up duplicates. |
| A blank row in the sheet breaks the run | A stray blank row past your data range reads as a row of nulls. Tighten the source range. |
No. Data Sync reads the sheet directly through QueryFlow's Google Sheets connection; there's no script to write, deploy, or debug inside the spreadsheet.
Insert alone would add a duplicate row every run. Use Upsert with a MATCH ON column that uniquely identifies each row, so a correction updates the existing row instead.
An INSERT with an ON CONFLICT clause against the MATCH ON column you set, Postgres's native upsert mechanism.
Pipelines. Studio covers querying both Google Sheets and Postgres, not building a sync between them.
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