Guide

Export large SQL results to CSV or Excel

When results get big, exports fail for predictable reasons: row limits, memory spikes, or slow downloads. This guide shows how to ship CSV or XLSX reliably—without losing traceability in run history and notifications.

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Related: scheduled exports and on-prem execution.

CSV vs Excel for large exports

Choose CSV when you need the fastest, smallest “snapshot” file for sharing or downstream tooling. Choose Excel (XLSX) when stakeholders expect a workbook, multiple sheets, or light formatting—then stream it to avoid memory blow-ups.

  • CSV: quickest to generate, easiest to open in most tools, best for raw data handoff.
  • XLSX: better for business users; use streaming for large row counts.

Limits, paging, and performance

Large exports are about controlling scope. Increase limits only when necessary, and prefer paging when a dataset can be shipped in chunks. This keeps runs predictable and helps teams avoid downloading “the wrong giant file”.

When to raise limits

Raise limits when you truly need a full extract (e.g., a daily delivery) and you’re confident in execution time and network bandwidth.

When to page results

Page when you can ship by time window, ID range, or partition key. It’s also safer for ad-hoc exports from shared queries.

LargeXlsx streaming for Excel

LargeXlsx writes the workbook incrementally so DataPilot doesn’t need to hold the entire file in memory. That’s what makes “millions of rows” exports practical in both scheduled runs and Run Now.

What you get

A predictable export pipeline: the run completes, the notification includes the download CTA, and run history keeps the audit trail.

Where it applies

Run Now, recurring schedules, and on-prem workers can all produce the same XLSX output behavior.

Where the file shows up

Exports are delivered through the workspace flow: completed runs appear in notifications with a download action. This keeps “the latest file” discoverable for the whole team, not buried in personal folders.