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Connectors

Eventzilla connector

Set up the Eventzilla connector in Kaivo: authentication, configuration, the 6 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 Eventzilla 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 Eventzilla instead of moving it.

What is the Eventzilla connector

Sync your Eventzilla data into BigQuery with Kaivo to analyse ticket sales, attendees, and event revenue.

CategoryOther
AuthenticationAPI key
SetupSelf-service

Getting started with the Eventzilla connector

  1. Sign up for Kaivo and create a workspace.
  2. Connect your Eventzilla 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 Eventzilla

Authenticate with your API Key.

FieldDescription
API Key

API key to use. Generate it by creating a new application within your Eventzilla account settings under Settings > App Management.

Tables and columns synced from Eventzilla

Kaivo syncs 6 tables from Eventzilla 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.
bar_codeSTRING
buyer_first_nameSTRING
buyer_last_nameSTRING
emailSTRING
event_dateSTRING
event_idFLOAT64
first_nameSTRING
idFLOAT64
is_attendedSTRING
last_nameSTRING
payment_typeSTRING
refnoSTRING
ticket_typeSTRING
transaction_amountFLOAT64
transaction_dateSTRING
transaction_statusSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.

Subtable: attendees__questions

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.
categorySTRING
_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
bgimage_urlSTRING
categoriesSTRING
currencySTRING
dateidFLOAT64
description_htmlSTRING
end_dateSTRING
end_timeSTRING
idFLOAT64
invite_codeSTRING
languageSTRING
logo_urlSTRING
show_remainingBOOL
start_dateSTRING
start_timeSTRING
statusSTRING
tickets_soldFLOAT64
tickets_totalFLOAT64
time_zoneSTRING
timezone_codeSTRING
titleSTRING
twitter_hashtagSTRING
urlSTRING
utc_offsetSTRING
venueSTRING
_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
additional_instructionsSTRING
allow_partial_paymentBOOL
boxoffice_onlyBOOL
group_discountFLOAT64
group_percentageFLOAT64
group_priceFLOAT64
idFLOAT64
is_visibleBOOL
limit_maximumFLOAT64
limit_minimumFLOAT64
partial_payment_amountFLOAT64
partial_payment_frequencySTRING
partial_payment_installmentsFLOAT64
priceFLOAT64
quantity_totalFLOAT64
sales_end_dateSTRING
sales_end_timeSTRING
sales_start_dateSTRING
sales_start_timeSTRING
ticket_typeSTRING
titleSTRING
unlock_codeSTRING
_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.
buyer_first_nameSTRING
buyer_last_nameSTRING
checkout_idFLOAT64
commentsSTRING
emailSTRING
event_dateSTRING
event_idFLOAT64
eventzilla_feeFLOAT64
payment_typeSTRING
promo_codeSTRING
refnoSTRING
tickets_in_transactionFLOAT64
titleSTRING
transaction_amountFLOAT64
transaction_dateSTRING
transaction_discountFLOAT64
transaction_statusSTRING
transaction_taxFLOAT64
user_idFLOAT64
_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.
address_countrySTRING
address_line1STRING
address_line2STRING
address_localitySTRING
address_regionSTRING
avatar_urlSTRING
companySTRING
emailSTRING
facebook_idSTRING
first_nameSTRING
idFLOAT64
last_nameSTRING
last_seenSTRING
phone_primarySTRING
timezoneSTRING
twitter_idSTRING
user_typeSTRING
usernameSTRING
websiteSTRING
zip_codeSTRING
_kaivo_extracted_atTIMESTAMPTimestamp that shows when the row was extracted. Auto-generated by Kaivo.

How the Eventzilla sync works

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

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

Common use cases for Eventzilla data

Ticket sales

Use tickets and transactions to track sales and revenue by event over time.

Attendance

Join events with attendees to measure turnout.

Category view

Use categories to compare performance across event types.

Use Eventzilla data in your AI and BI tools

Once Eventzilla 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 Eventzilla 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 Eventzilla connector pricing and plan details.

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