The Scenario
You're a data engineer at a B2B services firm. You've spent the last week enriching 500 lead records in a Google Sheet — appending company size, industry, and LinkedIn profile URLs from various sources — and now those records need to land in a named Apify dataset so a downstream automation pipeline can pick them up tonight.
You inherited this pipeline from someone who left the team six weeks ago. The documentation says "push the enriched leads into Apify before 6 PM." There are no instructions for how.
The bad version:
- Look up the Apify API documentation for the dataset push endpoint, find the right API token in the Apify console, write a script that reads the sheet and serializes each row as a JSON object, test it, fix the column index mismatch, re-run it on the first 10 rows, and then run it on all 500.
- Notice that rows with an empty 'email' field were pushed as null instead of being skipped, go back into the script, add a filter, and re-run.
- Realize the dataset name has a typo — you pushed to "enriched-leads-205" instead of "enriched-leads-2025" — and figure out how to delete it and re-push.
It's 5:20 PM and the pipeline runs at 6.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the data in your Enriched Leads sheet and through its built-in Apify integration it can serialize the rows as dataset items and push them into a named dataset — no script required.
Take all rows in my 'Enriched Leads' sheet and store them as items in my Apify dataset named 'enriched-leads-2025'
What You Get
- Every populated row in the Enriched Leads sheet is serialized as a JSON object and stored as a dataset item in the named Apify dataset.
- Column headers become field names in the dataset items.
- The dataset ID is written back into a note in cell A1 so you have a reference without opening Apify.
- Rows with completely empty data are skipped rather than pushed as blank items.
What If the Data Is Not Quite Ready
You want to skip rows where the email field is empty before pushing
Take all rows in my 'Enriched Leads' sheet where column C (email) is not empty and store them as items in my Apify dataset named 'enriched-leads-2025'
The dataset doesn't exist yet and you want SheetXAI to create it, then return the dataset ID
Upload every row from columns A–F of my 'Enriched Leads' sheet into a new Apify dataset called 'campaign-contacts' and write the returned dataset ID into cell H1
You want to normalize phone number formatting before pushing
For all rows in the Enriched Leads sheet, strip non-numeric characters from column D (phone), then push every row where column C (email) is not empty into the Apify dataset named 'enriched-leads-2025'
You want dedup, format normalization, empty-row filter, and the push in one shot
Take all rows in the Enriched Leads sheet, remove duplicates by column C (email), skip rows where both email and phone (column D) are empty, strip non-numeric characters from phone numbers, then push the remaining records into the Apify dataset named 'enriched-leads-2025' and write the dataset ID into cell I1
Clean, filtered, and in Apify before the pipeline runs.
Try It
Get the 7-day free trial of SheetXAI and open any sheet with enriched records you need to move into Apify, then ask it to push the rows into a named dataset. You can also look at how to import an Apify dataset and deduplicate results or browse the Apify integration overview.