Freshdesk connector
Set up the Freshdesk connector in Kaivo: authentication, configuration, the 27 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 Freshdesk 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 Freshdesk connector
Sync your Freshdesk support data into BigQuery with Kaivo to measure ticket volume, response times, and team performance.
Getting started with the Freshdesk connector
- Sign up for Kaivo and create a workspace.
- Connect your Freshdesk 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 Freshdesk
Authenticate with your API Key.
Configuring the Freshdesk connector
When you set up the connector, you provide:
Tables and columns synced from Freshdesk
Kaivo syncs 27 tables from Freshdesk into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
agents (23 columns)
agents (23 columns)
business_hours (23 columns)
business_hours (23 columns)
canned_response_folders (8 columns)
canned_response_folders (8 columns)
canned_responses (9 columns)
canned_responses (9 columns)
Subtable: canned_responses__attachments
companies (12 columns)
companies (12 columns)
Subtable: companies__domains
contacts (21 columns)
contacts (21 columns)
conversations (15 columns)
conversations (15 columns)
Subtable: conversations__to_emails
Subtable: conversations__cc_emails
Subtable: conversations__bcc_emails
Subtable: conversations__attachments
discussion_categories (7 columns)
discussion_categories (7 columns)
discussion_comments (13 columns)
discussion_comments (13 columns)
discussion_forums (11 columns)
discussion_forums (11 columns)
Subtable: discussion_forums__company_ids
discussion_topics (17 columns)
discussion_topics (17 columns)
email_configs (12 columns)
email_configs (12 columns)
email_mailboxes (18 columns)
email_mailboxes (18 columns)
groups (12 columns)
groups (12 columns)
products (7 columns)
products (7 columns)
roles (8 columns)
roles (8 columns)
satisfaction_ratings (11 columns)
satisfaction_ratings (11 columns)
scenario_automations (8 columns)
scenario_automations (8 columns)
Subtable: scenario_automations__actions
settings (3 columns)
settings (3 columns)
Subtable: settings__supported_languages
Subtable: settings__portal_languages
sla_policies (29 columns)
sla_policies (29 columns)
Subtable: sla_policies__applicable_to__company_ids
Subtable: sla_policies__applicable_to__group_ids
Subtable: sla_policies__applicable_to__sources
Subtable: sla_policies__applicable_to__ticket_types
Subtable: sla_policies__applicable_to__product_ids
Subtable: sla_policies__escalation__response__agent_ids
Subtable: sla_policies__escalation__resolution__level1__agent_ids
Subtable: sla_policies__escalation__resolution__level2__agent_ids
Subtable: sla_policies__escalation__resolution__level3__agent_ids
Subtable: sla_policies__escalation__resolution__level4__agent_ids
solution_articles (17 columns)
solution_articles (17 columns)
Subtable: solution_articles__seo_data__meta_keywords
Subtable: solution_articles__tags
solution_categories (7 columns)
solution_categories (7 columns)
Subtable: solution_categories__visible_in_portals
solution_folders (8 columns)
solution_folders (8 columns)
Subtable: solution_folders__company_ids
Subtable: solution_folders__contact_segment_ids
Subtable: solution_folders__company_segment_ids
surveys (7 columns)
surveys (7 columns)
Subtable: surveys__questions
Subtable: surveys__questions__accepted_ratings
ticket_fields (22 columns)
ticket_fields (22 columns)
Subtable: ticket_fields__choices
tickets (27 columns)
tickets (27 columns)
Subtable: tickets__cc_emails
Subtable: tickets__fwd_emails
Subtable: tickets__reply_cc_emails
Subtable: tickets__ticket_cc_emails
Subtable: tickets__to_emails
Subtable: tickets__tags
time_entries (14 columns)
time_entries (14 columns)
How the Freshdesk sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Freshdesk 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 Freshdesk?
It depends on how much history is in your Freshdesk 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 Freshdesk'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 Freshdesk, 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 Freshdesk 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 Freshdesk connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Freshdesk data
Ticket volume
Use conversations and groups to track ticket volume and backlog over time.
SLA and response
Measure first response and resolution times across tickets.
Agent performance
Join tickets with agents and groups to compare workload and handling.
Customer view
Use companies and contacts to see which customers raise the most tickets.
Use Freshdesk data in your AI and BI tools
Once Freshdesk 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 Freshdesk 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 Freshdesk connector pricing and plan details.
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