Gigasheet

@Gigasheet find all rows in the Q1_sales_data sheet where revenue exceeds $50,000 and region is 'West', then summarize the top 5 accounts by total.

Applied filter to Q1_sales_data (handle: abc123-def456). Found 847 matching rows. Top 5 accounts by revenue: Acme Corp ($2.1M across 34 transactions), Beta Industries ($1.8M, 29 transactions), Gamma LLC ($1.6M, 41 transactions), Delta Systems ($1.4M, 22 transactions), Epsilon Partners ($1.2M, 18 transactions). Total West region revenue meeting criteria: $12.7M. Filter template saved as 'high_value_west_q1' for reuse.

This example shows Gigasheet's ability to filter large datasets (millions of rows) and return aggregated insights without downloading the file. Requires read access to the specified sheet. Filter templates persist across sessions, useful for recurring reports. Response time scales with dataset size—expect 5-15 seconds for multi-million-row sheets.

@Gigasheet append these 3 new customer records to the master_contacts sheet: name, email, signup_date for users who registered today.

Appended 3 rows to master_contacts (handle: xyz789-ghi012). Verified column alignment: 'name', 'email', 'signup_date' match existing schema. New row IDs: 450123, 450124, 450125. Sheet now contains 450,125 total rows. No duplicate email addresses detected in append operation. Changes are immediately visible to all workspace members with sheet access.

Demonstrates write capability—appending rows to an existing sheet without re-uploading the entire file. Gigasheet validates column names before insertion; mismatched headers will fail. This is a direct mutation: there's no undo via the MCP (use Gigasheet's UI version history). Ideal for incremental data ingestion from external sources or AI-generated records.

@Gigasheet combine the jan_orders, feb_orders, and mar_orders files using the 'order_id' column, then export the merged result to our S3 bucket as Q1_combined.csv.

Combined 3 files by 'order_id' column (jan: 45K rows, feb: 52K rows, mar: 61K rows). Merged dataset contains 158,000 rows with deduplicated order_ids. Generated export handle exp-abc123. Initiated S3 export to s3://your-bucket/Q1_combined.csv (region: us-west-2). Export status: processing. Estimated completion: 2-3 minutes. You'll receive the S3 object key once the upload finishes. Verify your bucket policy allows PutObject from Gigasheet's IAM role.

Showcases Gigasheet's workflow orchestration: merge multiple large files, then push results to cloud storage—all without local downloads. S3 export requires pre-configured bucket permissions (see Gigasheet docs for IAM policy). The MCP returns an export handle, but completion is asynchronous; poll status or rely on S3 event notifications. Useful for ETL pipelines feeding downstream analytics tools.

When Gigasheet beats spreadsheets for multi-source customer records

A 6-person support team pulls customer data from Zendesk exports, Stripe CSVs, and internal usage logs—each file 50k+ rows. They need a single view to answer "what did this customer buy and when did they last contact us?" Gigasheet's Combine Files by Name tool merges these by email in under 30 seconds, no Excel crashes. The API_KEY auth means any agent can trigger the merge from Switchy without waiting on IT. This works until you hit truly massive files (5M+ rows) where dedicated ETL makes more sense, but for support teams dealing with quarterly exports under 500k rows, Gigasheet keeps the workflow in one place. If your team spends more than 20 minutes a week manually VLOOKUPing customer records, Gigasheet pays off immediately.

Filtering duplicate CRM exports without writing SQL

A 3-person sales ops team exports their CRM weekly to audit pipeline health—duplicates, stale leads, missing owner assignments. They used to eyeball it in Google Sheets or bug engineering for a one-off query. Gigasheet's Apply Filter Template On Sheet and Create/Update Filter Template tools let them save "show me leads older than 90 days with no activity" as a reusable filter, then run it on each week's export in seconds. The 29-tool catalog means they can also append corrected rows back to the sheet without round-tripping through CSV hell. This breaks down if your CRM is live-updating (use a real BI tool), but for weekly or monthly snapshot workflows where the data lands as a file, Gigasheet turns a 2-hour manual slog into a 5-minute routine.

When Gigasheet handles ad-hoc analytics faster than your data warehouse

A 5-person product team needs to answer "how many users hit this feature last month?" but the data warehouse queue is 3 days deep and the event logs are sitting in S3 as 200MB CSVs. Gigasheet's Export Gigasheet to S3 and filter tools let them pull the logs, slice by feature flag, and get a count in under 10 minutes—no SQL, no Looker ticket. The API_KEY setup means any PM can kick off the analysis from Switchy without credentials drama. This only works for one-off questions on static exports; if you need real-time dashboards or joins across 10 tables, you still need the warehouse. But for the 80% of product questions that are "count this thing in last month's logs," Gigasheet gets you the answer before the data team even sees your Slack message.