The Scenario
You are a data quality manager. Your company's Algolia products index has 12,000 records. The engineering team is migrating to a new index schema next quarter, and before the migration, you need to know which records are incomplete.
Specifically: which records are missing category or imageUrl? You cannot answer that from the Algolia dashboard alone, it only shows individual records, not a bulk quality report.
Your manager wants a sheet on her desk by end of day Friday.
The bad version of Thursday:
- You write a script that calls Algolia's browse endpoint with a cursor
- The cursor-based pagination takes 12 API calls to get all 12,000 records
- The script dumps a 50 MB JSON file
- You write a second script to flatten and filter it
- You import the result into Google Sheets
- Sheets crashes on import because the file is too large
- You spend Friday morning fighting with Sheets import limits instead of doing QA
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your spreadsheet that can browse a complete Algolia index and write the results directly into the sheet, without an intermediate JSON file.
Open the SheetXAI sidebar and type:
Export all records from my Algolia index 'products' into this sheet, one row per record. Flag any row missing a value in the 'category' or 'imageUrl' fields by writing "MISSING" in a new column called DataQuality. Sort the sheet so flagged rows appear at the top.
SheetXAI browses the index using cursor-based pagination, writes every record into the sheet, adds the DataQuality column, and sorts it so the problem rows are immediately visible.
What You Get
A complete index export with quality flags:
- 12,000 rows — one per record, no manual pagination
- DataQuality column — "MISSING" for incomplete records, blank for clean ones
- Sort order — flagged rows at the top so QA starts immediately
- Full field set — all indexed fields written to columns, ready for bulk editing and re-upload
The count of flagged rows is your migration risk number. If 800 of 12,000 records are missing category, that is 800 records that will break your new schema on day one.
What If the Data Is Not Quite Ready
Index exports are complete by definition, but the QA process often requires more analysis than a simple null check.
When you want to check more fields, not just two
The new schema requires six fields to be present. You need a quality report across all six.
Export all records from my Algolia index 'products' into this sheet. For each record, check whether objectID, title, price, category, imageUrl, and brand are present. Write the count of missing fields to a column called MissingCount. Highlight rows where MissingCount is greater than 0.
When you want to group the quality report by category
You want to know which product categories have the worst data quality, not just which individual records are incomplete.
Export all records from my Algolia index 'products'. Count the number of records missing imageUrl grouped by category. Write category name and missing-imageUrl count to columns A and B in a new summary sheet called QA Summary, sorted by count descending.
When you want to find records that exist in Algolia but not in the master catalog sheet
The migration audit requires confirming Algolia has exactly the records it should have.
Export all objectIDs from my Algolia index 'products' into column A of a new sheet called Index IDs. Then compare them against the objectIDs in column A of the Products sheet. Write any objectID that appears in the Algolia export but not in the Products sheet to column C and label it "Orphan."
When you want to clean up the data in the sheet and re-upload in one operation
You have done the audit. Now you want to fill the missing fields and push the corrected records back.
Look at this sheet where flagged rows have "MISSING" in the DataQuality column. For each flagged row missing category, infer a category from the title field and write it to the category column. For rows still missing imageUrl, write "placeholder.png" as a default. Then re-upload all rows to my Algolia index 'products' using objectID as the key.
The pattern: export once, flag the problems, fix them in the sheet, and re-upload in one connected workflow. The sheet is the working layer between the broken state and the clean state.
Try It
Get the 7-day free trial of SheetXAI and open a blank sheet, then ask it to export your Algolia index and flag incomplete records. The Algolia integration is included in every SheetXAI plan. For related workflows, see how to enrich Algolia records with partial updates or the Algolia in Google Sheets overview.