Support · Source connector
Zendesk ELT Connector
Extract brings Zendesk support data into data warehouses and databases for analysis with the rest of your business data. Teams use it to measure support workload, service quality, and customer experience. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Support performance reporting
Use Zendesk data to measure workload, response patterns, resolution trends, and service quality.
Customer health analysis
Combine Zendesk support activity with customer and product data to identify accounts needing attention.
Workload planning
Analyze Zendesk volume and issue trends to plan staffing, routing, and operational follow-up.
Flexible reporting cadence
Refresh Zendesk reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make Zendesk support data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
ticket_activities | Fields include id, actor.id, actor_id, user.id. | Incremental |
hc_article_translations | Fields include hc_article_id, id, _singular_extract_timestamp, body. | Incremental |
hc_posts | Fields include id, author_id, _singular_extract_timestamp, closed. | Incremental |
groups_memberships | Fields include id, group_id, user_id, _singular_extract_timestamp. | Incremental |
side_conversions | Fields include ticket_id, id, _singular_extract_timestamp, created_at. | Incremental |
hc_articles | Fields include id, author_id, _singular_extract_timestamp, body. | Incremental |
hc_article_comments | Fields include hc_article_id, id, author_id, non_author_editor_id. | Incremental |
tickets | Fields include id, assignee_id, group_id, organization_id. | Incremental |
tags | Fields include name, _singular_extract_timestamp, count. | Full refresh |
users | Fields include id, default_group_id, organization_id, _singular_extract_timestamp. | Incremental |
jira_links | Fields include id, _singular_extract_timestamp, created_at, issue_id. | Incremental |
ticket_forms | Fields include id, _singular_extract_timestamp, active, agent_conditions. | Incremental |
satisfaction_reasons | Fields include id, _singular_extract_timestamp, created_at, deleted_at. | Incremental |
hc_sections | Fields include id, category_id, _singular_extract_timestamp, created_at. | Incremental |
hc_categories | Fields include id, _singular_extract_timestamp, created_at, description. | Incremental |
groups | Fields include id, _singular_extract_timestamp, created_at, is_public. | Incremental |
hc_post_comments | Fields include post_id, id, author_id, non_author_editor_id. | Incremental |
ticket_skips | Fields include id, ticket_id, user_id, _singular_extract_timestamp. | Incremental |
ticket_trigger_categories | Fields include id, _singular_extract_timestamp, created_at, name. | Incremental |
organizations | Fields include id, group_id, _singular_extract_timestamp, created_at. | Incremental |
ticket_triggers | Fields include id, _singular_extract_timestamp, actions, active. | Incremental |
satisfaction_ratings | Fields include id, assignee_id, group_id, ticket_id. | Incremental |
ticket_comments | Fields include ticket_id, id, author_id, _singular_extract_timestamp. | Incremental |
hc_votes | Fields include id, _singular_extract_timestamp, created_at, item_id. | Incremental |
users_identities | Fields include user_id, id, _singular_extract_timestamp, created_at. | Incremental |
ticket_metrics | Fields include id, ticket_id, _singular_extract_timestamp, agent_wait_time_in_minutes.business. | Incremental |
tickets_custom_fields | Fields include ticket_id, id, _singular_extract_timestamp, value. | Incremental |
hc_article_attachments | Fields include article_id, id, _singular_extract_timestamp, content_type. | Incremental |
sla_policies | Fields include id, _singular_extract_timestamp, created_at, description. | Incremental |
hc_article_labels | Fields include id, hc_article_id, _singular_extract_timestamp, created_at. | Incremental |
hc_topics | Fields include id, _singular_extract_timestamp, created_at, description. | Incremental |
organizations_memberships | Fields include id, organization_id, user_id, _singular_extract_timestamp. | Incremental |
hc_article_subscriptions | Fields include hc_topic_id, id, _singular_extract_timestamp, content_id. | Incremental |
Schema
Data model at a glance
ticket_activities
idintegeractor.idintegeractor_idintegeruser.idintegeruser_idinteger_singular_extract_timestampstringactor.namestringcreated_atstring
hc_article_translations
hc_article_idintegeridinteger_singular_extract_timestampstringbodystringcreated_atstringcreated_by_idintegerdraftedbooleanhtml_urlstring
hc_posts
idintegerauthor_idinteger_singular_extract_timestampstringclosedbooleancomment_countintegercontent_tag_idsarraycreated_atstringdetailsstring
groups_memberships
idintegergroup_idintegeruser_idinteger_singular_extract_timestampstringcreated_atstringupdated_atstringurlstring
side_conversions
ticket_idintegeridstring_singular_extract_timestampstringcreated_atstringexternal_idsjsonmessage_added_atstringparticipantsjsonpreview_textstring
hc_articles
idintegerauthor_idinteger_singular_extract_timestampstringbodystringcomments_disabledbooleancontent_tag_idsarraycreated_atstringdraftboolean
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Zendesk in Extract workflows.
Why Extract
Why Extract for Zendesk
- Rust-native performanceUse Rust-native data movement to refresh Zendesk reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Zendesk API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Zendesk data with customer and finance models, then send approved outputs through a separately configured Reverse ETL destination.
FAQ
Frequently asked questions
How does Extract handle incremental and full Zendesk syncs?
An incremental sync processes only new or changed data since a stored checkpoint or new date partition, while a full sync processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental syncs can reduce destination database updates and warehouse compute costs. Stream schemas define full refresh, incremental changes, partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical Zendesk data with Extract?
Historical coverage for Zendesk: Initial Backfill (DAYS) (backfill): On the very first load of a stream, that has no set cursor, how far back do we want to pull records. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor Zendesk data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Zendesk API activity, execution steps, and timing. This helps teams distinguish a source-data delay from a failed or incomplete run.
How often can Extract sync data from Zendesk?
Extract supports schedules every 15 minutes, hourly, daily, or on a custom cadence. Teams can choose a frequency that matches routine reporting, time-sensitive monitoring, or operational activation needs.
What does the Zendesk data model look like in the destination?
The Available streams table describes the datasets Extract can read from Zendesk. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.