PandaDoc connector
Set up the PandaDoc connector in Kaivo: authentication, configuration, the 14 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 16 days ago
Kaivo is a fully managed data platform that syncs your PandaDoc data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your marketing and sales data instead of moving it.
What is the PandaDoc connector
Sync your PandaDoc document data into BigQuery with Kaivo to track proposals and document completion across your team.
Getting started with the PandaDoc connector
- Sign up for Kaivo and create a workspace.
- Connect your PandaDoc account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating PandaDoc
Authenticate with your API Key.
Configuring the PandaDoc connector
When you set up the connector, you provide:
Tables and columns synced from PandaDoc
Kaivo syncs 14 tables from PandaDoc into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
api_logs (8 columns)
api_logs (8 columns)
contacts (8 columns)
contacts (8 columns)
document_attachment (10 columns)
document_attachment (10 columns)
document_field (33 columns)
document_field (33 columns)
Subtable: document_field__assigned_to__roles
document_folders (7 columns)
document_folders (7 columns)
document_section (4 columns)
document_section (4 columns)
documents (10 columns)
documents (10 columns)
forms (7 columns)
forms (7 columns)
members (15 columns)
members (15 columns)
template_folders (7 columns)
template_folders (7 columns)
templates (7 columns)
templates (7 columns)
webhook_events (7 columns)
webhook_events (7 columns)
webhook_subscriptions (9 columns)
webhook_subscriptions (9 columns)
Subtable: webhook_subscriptions__payload
Subtable: webhook_subscriptions__triggers
workspaces (2 columns)
workspaces (2 columns)
How the PandaDoc sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where PandaDoc supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.
Frequently asked questions
How long does the initial sync take for PandaDoc?
It depends on how much history is in your PandaDoc account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.
Can I sync only some tables or columns?
Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.
What happens when PandaDoc's schema changes?
New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in PandaDoc, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in PandaDoc instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the PandaDoc connector in Kaivo and all of its synced data is deleted with it.
Common use cases for PandaDoc data
Document throughput
Use documents to track how many proposals are sent, viewed, and signed over time.
Template usage
Join templates with documents to see which templates close fastest.
Team activity
Use members and workspaces to compare document activity across the team.
Use PandaDoc data in your AI and BI tools
Once PandaDoc data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with PandaDoc data.
Claude
Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Excel
Connect via Power Query's BigQuery connector. Setup guide →
Metabase
Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for PandaDoc connector pricing and plan details.
Related connectors
- Google Ads: Sync Google Ads to BigQuery.
- Custobar: Sync Custobar to BigQuery.
- Google Search Console: Sync Google Search Console to BigQuery.
- HubSpot: Sync HubSpot to BigQuery.
- Adform: Sync Adform to BigQuery.
- Amplitude: Sync Amplitude to BigQuery.
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