Track PMS connector

Set up the Track PMS connector in Kaivo: authentication, configuration, the 76 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated About 6 hours ago

Kaivo is a fully managed data platform that syncs your Track PMS 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 data from Track PMS instead of moving it.

What is the Track PMS connector

Sync your Track PMS property management data into BigQuery with Kaivo to analyse bookings, charges, and accounting.

Category	Other
Status	Generally available
Authentication	API key
Setup	Self-service

Getting started with the Track PMS connector

Sign up for Kaivo and create a workspace.
Connect your Track PMS 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 Track PMS

Authenticate with your API Secret.

Field	Description
API Secret	The Server API Secret shown alongside the Key when it is created. Copy it immediately, as it is shown only once.

Configuring the Track PMS connector

When you set up the connector, you provide:

Field	Description
Customer Domain	Your Track HS domain, e.g. `yourcompany.trackhs.com` — the part before `/api/` in your account URL.
API Key	The Server API Key. In Track HS go to Configuration → Company Setup → API Keys and click “+ Server Key” to get the Key and Secret.

Tables and columns synced from Track PMS

Kaivo syncs 76 tables from Track PMS into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

How the Track PMS sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Track PMS 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 Track PMS?

It depends on how much history is in your Track PMS 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 Track PMS'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 Track PMS, 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.

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 Track PMS 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 Track PMS connector in Kaivo and all of its synced data is deleted with it.

Common use cases for Track PMS data

Revenue and charges

Use charges and accounting_transactions to track revenue and fees over time.

Booking analysis

Join contacts with charges to understand guest activity and spend.

Accounting reporting

Use accounting_accounts and accounting_bills to report on your books.

Use Track PMS data in your AI and BI tools

Once Track PMS 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 Track PMS 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 Track PMS connector pricing and plan details.

Demo: Sync Demo to BigQuery.
Navan: Sync Navan to BigQuery.
Eventbrite: Sync Eventbrite to BigQuery.
Fleetio: Sync Fleetio to BigQuery.
Google Classroom: Sync Google Classroom to BigQuery.
Granola: Sync Granola to BigQuery.

What is the Track PMS connector

Getting started with the Track PMS connector

Authenticating Track PMS

Configuring the Track PMS connector

Tables and columns synced from Track PMS

accounting_accounts (31 columns)

accounting_bills (24 columns)

accounting_charges (41 columns)

accounting_deposits (13 columns)

accounting_deposits_payments (14 columns)

accounting_items (16 columns)

accounting_transactions (60 columns)

booking_fees (35 columns)

charges (72 columns)

companies (35 columns)

contacts (43 columns)

contacts_companies (7 columns)

contacts_pii_redacted (43 columns)

contracts (19 columns)

crm_company_attachment (14 columns)

crm_tasks (14 columns)

custom_fields (22 columns)

date_groups (10 columns)

documents (19 columns)

folios (34 columns)

folios_logs (9 columns)

folios_rules (21 columns)

folios_transactions (338 columns)

fractionals (18 columns)

fractionals_inventory (372 columns)

fractionals_owners (15 columns)

groups (62 columns)

groups_blocks (9 columns)

groups_tags (12 columns)

housekeeping_clean_types (35 columns)

housekeeping_task_list (10 columns)

housekeeping_work_orders (52 columns)

lodging_types (16 columns)

maintenance_problems (4 columns)

maintenance_work_orders (58 columns)

nodes (48 columns)

nodes_types (16 columns)

owners (45 columns)

owners_contracts (19 columns)

owners_pii_redacted (45 columns)

owners_statements (24 columns)

owners_statements_transactions (182 columns)

owners_units (14 columns)

promo_codes (27 columns)

quotes (307 columns)

rate_types (42 columns)

reservations (242 columns)

reservations_cancellation_policies (32 columns)

reservations_cancellation_reasons (15 columns)

reservations_discount_reasons (10 columns)

reservations_guarantee_policies (28 columns)

reservations_types (39 columns)

reservations_v2 (203 columns)

reviews (28 columns)

roles (288 columns)

suspend_code_reasons (10 columns)

tags (11 columns)

tax_districts (15 columns)

tax_policies (10 columns)

taxes (24 columns)

travel_insurance_products (27 columns)

units (81 columns)

units_amenities (18 columns)

units_amenity_groups (8 columns)

units_bed_types (14 columns)

units_blocks (49 columns)

units_channel (198 columns)

units_daily_pricing_parent (81 columns)

units_daily_pricing_v2 (4 columns)

units_taxes (8 columns)

units_taxes_parent (81 columns)

units_types (44 columns)

units_types_daily_pricing_parent (44 columns)

units_types_daily_pricing_v2 (4 columns)

users (11 columns)