Marketing · Source connector
LinkedIn ELT Connector
Extract brings LinkedIn marketing data into data warehouses and databases for analysis with the rest of your business data. Teams use it to compare channel performance, audience engagement, and campaign trends. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Marketing performance analysis
Compare LinkedIn channel, campaign, and engagement trends to identify opportunities for improvement.
Audience and content analysis
Use LinkedIn data to understand which audiences, content, and activities contribute to engagement.
Cross-channel reporting
Combine LinkedIn metrics with other marketing and business data for consistent performance reporting.
Flexible reporting cadence
Refresh LinkedIn reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make LinkedIn marketing 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 |
|---|---|---|
ad_campaign_groups | Fields include account, allowed_campaign_types, backfilled, change_audit_stamps.created.time. | Full refresh |
member_company_stats | Fields include campaign, date, member_company, action_clicks. | Incremental |
titles | Fields include function, id, name.localized.en_us, super_title. | Full refresh |
company_stats | Fields include campaign, company, date, action_clicks. | Incremental |
campaign_group_stats | Fields include campaign, campaign_group, date, action_clicks. | Incremental |
share_stats | Fields include campaign, date, share, action_clicks. | Incremental |
impression_device_type_stats | Fields include campaign, date, impression_device_type, action_clicks. | Incremental |
serving_location_stats | Fields include campaign, date, serving_location, action_clicks. | Incremental |
conversion_stats | Fields include conversion, creative, date, action_clicks. | Incremental |
posts | Fields include ad_context.dsc_ad_account, ad_context.dsc_ad_type, ad_context.dsc_name, ad_context.dsc_status. | Full refresh |
sponsored_conversations | Fields include first_message_content, headline_text, id, parent_account. | Full refresh |
member_company_size_stats | Fields include campaign, date, member_company_size, action_clicks. | Incremental |
member_seniority_stats | Fields include campaign, date, member_seniority, action_clicks. | Incremental |
account_stats | Fields include account, campaign, date, action_clicks. | Incremental |
industries | Fields include id, name.localized.en_us. | Full refresh |
sponsored_conversation_contents | Fields include body_source.lead_generation_form, body_source.media_attachment.attachment, body_source.media_attachment.text, body_source.text. | Full refresh |
placement_name_stats | Fields include campaign, date, placement_name, action_clicks. | Incremental |
member_region_v2_stats | Fields include campaign, date, member_region_v2, action_clicks. | Incremental |
seniorities | Fields include id, name.localized.en_us. | Full refresh |
functions | Fields include id, name.localized.en_us. | Full refresh |
campaign_stats | Fields include campaign, date, action_clicks, ad_unit_clicks. | Incremental |
event_stage_stats | Fields include campaign, date, event_stage, action_clicks. | Incremental |
member_job_title_stats | Fields include campaign, date, member_job_title, action_clicks. | Incremental |
member_job_function_stats | Fields include campaign, date, member_job_function, action_clicks. | Incremental |
creative_stats | Fields include campaign, creative, date, action_clicks. | Incremental |
card_index_stats | Fields include campaign, card_index, date, action_clicks. | Incremental |
member_country_v2_stats | Fields include campaign, date, member_country_v2, action_clicks. | Incremental |
ad_campaigns | Fields include account, associated_entity, audience_expansion_enabled, campaign_group. | Full refresh |
member_industry_stats | Fields include campaign, date, member_industry, action_clicks. | Incremental |
in_mail_contents | Fields include account, created_at, created_by, custom_footer. | Full refresh |
targeting_criteria | Fields include campaign_id, facet_name, facet_value, include. | Full refresh |
ad_account_users | Fields include account, change_audit_stamps.created.time, change_audit_stamps.last_modified.time, id. | Full refresh |
creatives | Fields include account, id, campaign, content.document_ad.gated_leadgen_preview_page_count. | Full refresh |
ad_accounts | Fields include change_audit_stamps.created.time, change_audit_stamps.last_modified.time, currency, id. | Full refresh |
Schema
Data model at a glance
ad_campaign_groups
accountstringallowed_campaign_typesarraybackfilledbooleanchange_audit_stamps.created.timeintegerchange_audit_stamps.last_modified.timeintegeridintegernamestringrun_schedulestring
member_company_stats
campaignstringdatestringmember_companystringaction_clicksintegerad_unit_clicksintegerapproximate_member_reachintegercard_clicksintegercard_impressionsinteger
titles
functionstringidintegername.localized.en_usstringsuper_titlestring
company_stats
campaignstringcompanystringdatestringaction_clicksintegerad_unit_clicksintegerapproximate_member_reachintegercard_clicksintegercard_impressionsinteger
campaign_group_stats
campaignstringcampaign_groupstringdatestringaction_clicksintegerad_unit_clicksintegerapproximate_member_reachintegercard_clicksintegercard_impressionsinteger
share_stats
campaignstringdatestringsharestringaction_clicksintegerad_unit_clicksintegerapproximate_member_reachintegercard_clicksintegercard_impressionsinteger
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with LinkedIn in Extract workflows.
Why Extract
Why Extract for LinkedIn
- Rust-native performanceUse Rust-native data movement to refresh LinkedIn reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect LinkedIn API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine LinkedIn 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 LinkedIn 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, partition-based extraction. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.
Can I backfill historical LinkedIn data with Extract?
Historical coverage for LinkedIn: 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 LinkedIn data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect LinkedIn 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 LinkedIn?
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 LinkedIn data model look like in the destination?
The Available streams table describes the datasets Extract can read from LinkedIn. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.