Pylon connector
Set up the Pylon connector in Kaivo: authentication, configuration, the 15 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 Pylon 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 customer support data instead of moving it.
What is the Pylon connector
Sync your Pylon support data into BigQuery with Kaivo to measure issue volume, response times, and customer health.
Getting started with the Pylon connector
- Sign up for Kaivo and create a workspace.
- Connect your Pylon 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 Pylon
Authenticate with your API Token.
Configuring the Pylon connector
When you set up the connector, you provide:
Tables and columns synced from Pylon
Kaivo syncs 15 tables from Pylon into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
accounts (10 columns)
accounts (10 columns)
Subtable: accounts__domains
Subtable: accounts__channels
Subtable: accounts__external_ids
Subtable: accounts__tags
activity_types (6 columns)
activity_types (6 columns)
contacts (9 columns)
contacts (9 columns)
Subtable: contacts__emails
Subtable: contacts__phone_numbers
Subtable: contacts__integration_user_ids
custom_fields (12 columns)
custom_fields (12 columns)
issue_messages (8 columns)
issue_messages (8 columns)
Subtable: issue_messages__file_urls
issue_statuses (7 columns)
issue_statuses (7 columns)
issue_threads (8 columns)
issue_threads (8 columns)
issues (23 columns)
issues (23 columns)
Subtable: issues__tags
Subtable: issues__external_issues
Subtable: issues__attachment_urls
Subtable: issues__csat_responses
knowledge_base_articles (9 columns)
knowledge_base_articles (9 columns)
knowledge_bases (6 columns)
knowledge_bases (6 columns)
Subtable: knowledge_bases__supported_languages
tags (6 columns)
tags (6 columns)
teams (4 columns)
teams (4 columns)
Subtable: teams__users
ticket_forms (8 columns)
ticket_forms (8 columns)
Subtable: ticket_forms__fields
user_roles (5 columns)
user_roles (5 columns)
users (8 columns)
users (8 columns)
Subtable: users__emails
How the Pylon sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Pylon 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 Pylon?
It depends on how much history is in your Pylon 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 Pylon'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 Pylon, 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 Pylon 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 Pylon connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Pylon data
Issue volume
Use issues and issue_messages to track support volume and response over time.
Account view
Join accounts with issues to see which customers raise the most.
Knowledge base
Use knowledge_base_articles to report on help content alongside support activity.
Use Pylon data in your AI and BI tools
Once Pylon 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 Pylon 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 Pylon connector pricing and plan details.
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