The Scenario
Your data pipeline runs at midnight. It ingests from the BigQuery raw_events table. The pipeline team told you this morning that last night's run was missing 200 rows — validated event data sitting in a staging Excel workbook that never made it into BigQuery before the job kicked off. They need those rows back-filled into the table before the next run at midnight tonight.
The bad version:
- Export the staging workbook to CSV, upload it to Google Cloud Storage, write a BigQuery load job to ingest from that GCS URI, configure the schema — or skip schema config and hope BigQuery infers it correctly.
- Run the job, check the job status, notice that 14 rows failed because of a type mismatch in the "amount" column that was stored as text in the workbook.
- Fix those 14 rows manually, run the job again, verify the row count.
This is legitimate data engineering work. If it's your job, fine. If it fell to you because you're the person who owns the staging workbook and the pipeline team is busy, the midnight deadline is not in your favor.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook. Through its Google BigQuery integration, it can insert rows from your workbook directly into a BigQuery table — and write the result of each insert back to the workbook so you can see exactly what happened.
Stream-insert every data row from my Excel sheet into the BigQuery table specified in cell A1 using the project in B1 and dataset in C1, and log the insert status back to the sheet
What You Get
- Every data row is inserted into the BigQuery table named in cell A1, using project (B1) and dataset (C1).
- A status column gets "success" for rows that landed, or the specific error message for rows that didn't.
- You can see at a glance which rows need attention — no digging through job logs in the BigQuery console.
- Rows that succeed don't get re-inserted if you run the prompt again — the status column makes it easy to filter to failures only.
What If the Data Is Not Quite Ready
Some values in the "amount" column are stored as text, not numbers
Before inserting rows from my Excel sheet into my-project.staging.raw_events, cast the values in column D (amount) to NUMERIC — if a row's amount can't be converted, skip that row and write "type error: amount" to the status column instead.
You need to de-duplicate before inserting
Insert rows from my Excel sheet into my-project.staging.raw_events, but skip any row where the event_id already exists in the BigQuery table. Write 'inserted', 'skipped (duplicate)', or the error to the status column.
The header row uses different names than the BigQuery table fields
Insert rows from my Excel sheet into my-project.staging.raw_events, mapping my column 'Order Date' to the BigQuery field 'order_date', 'Amount USD' to 'amount', and 'Customer ID' to 'customer_id'. Write the insert status to column Z.
Clean, validate, and insert in a single pass
For each row in my Excel sheet: trim whitespace from the email column, cast the timestamp column to TIMESTAMP, check that the user_id is not null — if any check fails, write the reason to the status column and skip insertion. For rows that pass, insert into my-project.staging.raw_events and write 'success'.
Every validation step and the insert happen in the same prompt.
Try It
Get the 7-day free trial of SheetXAI and open the staging Excel workbook you need to push into BigQuery — tell it the table name, point it at the right rows, and let it handle the inserts with per-row feedback. Also see pulling BigQuery query results into an Excel workbook or return to the Google BigQuery integration overview.