Mailjet Mail connector

Set up the Mailjet Mail 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 Mailjet Mail 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 survey and email data instead of moving it.

What is the Mailjet Mail connector

Sync your Mailjet email data into BigQuery with Kaivo to measure campaigns, deliverability, and audience growth.

Category	Surveys & Email
Authentication	API key
Setup	Self-service

Getting started with the Mailjet Mail connector

Sign up for Kaivo and create a workspace.
Connect your Mailjet Mail 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 Mailjet Mail

Authenticate with your API Secret Key.

Field	Description
API Secret Key	Your API Secret Key. See here.

Configuring the Mailjet Mail connector

When you set up the connector, you provide:

Field	Description
API Key	Your API Key. See here.

Tables and columns synced from Mailjet Mail

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

campaign (24 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`campaign_type`	`INT64`
`click_tracked`	`INT64`
`created_at`	`STRING`
`custom_value`	`STRING`
`first_message_id`	`INT64`
`from_email`	`STRING`
`from_id`	`FLOAT64`
`from_name`	`STRING`
`has_html_count`	`FLOAT64`
`has_txt_count`	`FLOAT64`
`id`	`INT64`
`is_deleted`	`BOOL`
`is_starred`	`BOOL`
`list_id`	`INT64`
`news_letter_id`	`INT64`
`open_tracked`	`FLOAT64`
`send_end_at`	`STRING`
`send_start_at`	`STRING`
`spamass_score`	`INT64`
`status`	`INT64`
`subject`	`STRING`
`unsubscribe_tracked_count`	`INT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

contacts (15 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`created_at`	`STRING`
`delivered_count`	`INT64`
`email`	`STRING`
`exclusion_from_campaigns_updated_at`	`STRING`
`id`	`INT64`
`is_excluded_from_campaigns`	`BOOL`
`is_opt_in_pending`	`BOOL`
`is_spam_complaining`	`BOOL`
`last_activity_at`	`STRING`
`last_update_at`	`STRING`
`name`	`STRING`
`unsubscribed_at`	`STRING`
`unsubscribed_by`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

contactslist (8 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`address`	`STRING`
`created_at`	`STRING`
`id`	`INT64`
`is_deleted`	`BOOL`
`name`	`STRING`
`subscriber_count`	`INT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

listrecipient (10 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`contact_id`	`INT64`
`id`	`INT64`
`is_active`	`BOOL`
`is_unsubscribed`	`BOOL`
`list_id`	`INT64`
`list_name`	`STRING`
`subscribed_at`	`STRING`
`unsubscribed_at`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

message (25 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`arrived_at`	`STRING`
`attachment_count`	`INT64`
`attempt_count`	`INT64`
`campaign_id`	`INT64`
`contact_alt`	`STRING`
`contact_id`	`INT64`
`delay`	`INT64`
`destination_id`	`INT64`
`filter_time`	`INT64`
`id`	`INT64`
`is_click_tracked`	`BOOL`
`is_html_part_included`	`BOOL`
`is_open_tracked`	`BOOL`
`is_text_part_included`	`BOOL`
`is_unsub_tracked`	`BOOL`
`message_size`	`INT64`
`sender_id`	`INT64`
`spamass_rules`	`STRING`
`spamassassin_score`	`INT64`
`state_permanent`	`BOOL`
`status`	`STRING`
`subject`	`STRING`
`uuid`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

stats_api_lifetime_message (24 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`api_key_id`	`INT64`
`event_click_delay`	`INT64`
`event_clicked_count`	`INT64`
`event_open_delay`	`INT64`
`event_opened_count`	`INT64`
`event_spam_count`	`INT64`
`event_unsubscribed_count`	`INT64`
`event_workflow_exited_count`	`INT64`
`message_blocked_count`	`INT64`
`message_clicked_count`	`INT64`
`message_deferred_count`	`INT64`
`message_hard_bounced_count`	`INT64`
`message_opened_count`	`INT64`
`message_queued_count`	`INT64`
`message_sent_count`	`INT64`
`message_soft_bounced_count`	`INT64`
`message_spam_count`	`INT64`
`message_unsubscribed_count`	`INT64`
`message_work_flow_exited_count`	`INT64`
`source_id`	`INT64`
`timeslice`	`STRING`
`total`	`INT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

How the Mailjet Mail sync works

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

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

Common use cases for Mailjet Mail data

Campaign performance

Use campaign and stats_api_lifetime_message to track email results over time.

Deliverability

Use message and listrecipient to monitor delivery and engagement.

List growth

Use contactslist and contacts to see how your audience grows.

Use Mailjet Mail data in your AI and BI tools

Once Mailjet Mail 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 Mailjet Mail 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 Mailjet Mail connector pricing and plan details.

Gmail: Sync Gmail to BigQuery.
Iterable: Sync Iterable to BigQuery.
Klaviyo: Sync Klaviyo to BigQuery.
Mailchimp: Sync Mailchimp to BigQuery.
Twilio SendGrid: Sync Twilio SendGrid to BigQuery.
Surveypal: Sync Surveypal to BigQuery.

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