Skip to main content
Connectors

Cloudbeds connector

Set up the Cloudbeds connector in Kaivo: authentication, configuration, the 6 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated 15 days ago

Kaivo is a fully managed data platform that syncs your Cloudbeds 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 e-commerce data instead of moving it.

What is the Cloudbeds connector

Sync your Cloudbeds property data into BigQuery with Kaivo to analyse reservations, revenue, and occupancy across your hotels.

CategoryE-commerce
AuthenticationAPI key
SetupSelf-service

Getting started with the Cloudbeds connector

  1. Sign up for Kaivo and create a workspace.
  2. Connect your Cloudbeds account.
  3. Choose which tables to sync.
  4. Wait for the initial sync to finish.
  5. Query your data in BigQuery or your favourite AI or BI tool.

Authenticating Cloudbeds

Authenticate with your API Key.

FieldDescription
API Key

Tables and columns synced from Cloudbeds

Kaivo syncs 6 tables from Cloudbeds into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

ColumnTypeDescription
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
date_createdSTRING
date_modifiedSTRING
guest_emailSTRING
guest_idSTRING
guest_nameSTRING
is_anonymizedBOOL
is_main_guestBOOL
is_mergedBOOL
new_guest_idSTRING
property_idSTRING
reservation_idSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.
ColumnTypeDescription
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
organization_idSTRING
property_currency__currency_codeSTRING
property_currency__currency_positionSTRING
property_currency__currency_symbolSTRING
property_descriptionSTRING
property_idSTRING
property_imageSTRING
property_nameSTRING
property_timezoneSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.
ColumnTypeDescription
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
descriptionSTRING
category_nameSTRING
grand_totalFLOAT64
item_codeSTRING
item_idSTRING
item_typeSTRING
nameSTRING
priceFLOAT64
price_without_fees_and_taxesFLOAT64
skuSTRING
stock_inventoryBOOL
total_feesFLOAT64
total_taxesFLOAT64
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.

Subtable: packages__fees

ColumnTypeDescription
_kaivo_parent_idSTRINGForeign key referencing _kaivo_id in the parent table. Auto-generated by Kaivo.
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
valueJSON
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.

Subtable: packages__taxes

ColumnTypeDescription
_kaivo_parent_idSTRINGForeign key referencing _kaivo_id in the parent table. Auto-generated by Kaivo.
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
tax_nameSTRING
tax_valueFLOAT64
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.
ColumnTypeDescription
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
adultsSTRING
balanceFLOAT64
childrenSTRING
date_createdSTRING
date_modifiedSTRING
end_dateSTRING
guest_idSTRING
guest_nameSTRING
originSTRING
profile_idSTRING
property_idSTRING
reservation_idSTRING
source_idSTRING
source_nameSTRING
start_dateSTRING
statusSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.
ColumnTypeDescription
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
property_idSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.

Subtable: rooms__room_blocks

ColumnTypeDescription
_kaivo_parent_idSTRINGForeign key referencing _kaivo_id in the parent table. Auto-generated by Kaivo.
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
valueJSON
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.
ColumnTypeDescription
_kaivo_idSTRINGPrimary key that uniquely identifies the row. Auto-generated by Kaivo.
descriptionSTRING
amountFLOAT64
card_typeSTRING
categorySTRING
currencySTRING
guest_check_inSTRING
guest_check_outSTRING
guest_idSTRING
guest_nameSTRING
is_deletedBOOL
item_category_nameSTRING
notesSTRING
parent_transaction_idSTRING
property_idSTRING
property_nameSTRING
quantitySTRING
reservation_idSTRING
room_nameSTRING
room_type_idSTRING
room_type_nameSTRING
sub_reservation_idSTRING
transaction_categorySTRING
transaction_codeSTRING
transaction_date_timeSTRING
transaction_date_time_utcSTRING
transaction_idSTRING
transaction_modified_date_timeSTRING
transaction_modified_date_time_utcSTRING
transaction_typeSTRING
user_nameSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.

How the Cloudbeds sync works

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

It depends on how much history is in your Cloudbeds 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 Cloudbeds'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 Cloudbeds, 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 Cloudbeds 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 Cloudbeds connector in Kaivo and all of its synced data is deleted with it.

Common use cases for Cloudbeds data

Occupancy and revenue

Use reservations and transactions to track occupancy and revenue over time.

Guest analysis

Join guests with reservations to measure repeat stays and booking patterns.

Property view

Use hotels and rooms to compare performance across properties.

Use Cloudbeds data in your AI and BI tools

Once Cloudbeds 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 Cloudbeds 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 Cloudbeds connector pricing and plan details.

Was this helpful?

Still need help? Share an idea