Marketing · Source connector
Vibe ELT Connector
Extract brings Vibe marketing data into data warehouses and databases for analysis with the rest of your business data. Teams use it to analyze campaign, creative, placement, spend, and revenue performance at a granular level. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Granular campaign optimization
Analyze Vibe campaign, creative, placement, geo, device, spend, and revenue detail to find opportunities to improve performance.
Direct-network reporting
Compare granular Vibe data with standard MMP and cost-aggregation reporting to work with dimensions those tools do not make available.
Spend and revenue reconciliation
Reconcile Vibe spend and revenue with finance, attribution, and internal performance data to investigate discrepancies.
Flexible performance monitoring
Refresh Vibe pacing, anomaly, and performance views every 15 minutes, hourly, daily, or on a custom schedule.
Warehouse and database analysis
Use Extract to make Vibe marketing data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Audience and campaign activation
Combine Vibe performance data with approved customer models, then use Extract Reverse ETL to activate audience or campaign updates downstream.
Connector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
advertiser_app_ids | Fields include advertiser_id, app_id, _singular_extract_timestamp. | Full refresh |
advertisers | Fields include advertiser_id, advertiser_name, _singular_extract_timestamp. | Full refresh |
campaign_details | Fields include advertiser_id, campaign_id, campaign_name, strategy_id. | Full refresh |
purchase_ids | Fields include impression_timestamp, event_timestamp, event_date, purchase_id. | Incremental |
report_advertiser | Fields include impression_date, advertiser_id, advertiser_name, spend. | Incremental |
report_app | Fields include impression_date, app_id, os, spend. | Incremental |
report_campaign | Fields include impression_date, advertiser_id, advertiser_name, campaign_id. | Incremental |
report_channel | Fields include impression_date, channel_name, spend, impressions. | Incremental |
report_content | Fields include impression_date, content_name, spend, impressions. | Incremental |
report_creative | Fields include impression_date, advertiser_id, advertiser_name, campaign_id. | Incremental |
report_custom_audiences | Fields include impression_date, custom_audiences, spend, impressions. | Incremental |
report_daily | Fields include impression_date, spend, impressions, households. | Incremental |
report_geo_city | Fields include impression_date, geo_city, spend, impressions. | Incremental |
report_geo_metro | Fields include impression_date, geo_metro, spend, impressions. | Incremental |
report_geo_region | Fields include impression_date, geo_region, spend, impressions. | Incremental |
report_geo_zip | Fields include impression_date, geo_zip, spend, impressions. | Incremental |
report_screen | Fields include impression_date, screen, spend, impressions. | Incremental |
report_segment_ages | Fields include impression_date, segment_ages, spend, impressions. | Incremental |
report_segment_career | Fields include impression_date, segment_career, spend, impressions. | Incremental |
report_segment_educations | Fields include impression_date, segment_educations, spend, impressions. | Incremental |
report_segment_estimated_incomes | Fields include impression_date, segment_estimated_incomes, spend, impressions. | Incremental |
report_segment_estimated_net_worth | Fields include impression_date, segment_estimated_net_worth, spend, impressions. | Incremental |
report_segment_ethnicity | Fields include impression_date, segment_ethnicity, spend, impressions. | Incremental |
report_segment_family_composition | Fields include impression_date, segment_family_composition, spend, impressions. | Incremental |
report_segment_genders | Fields include impression_date, segment_genders, spend, impressions. | Incremental |
report_segment_interests | Fields include impression_date, segment_interests, spend, impressions. | Incremental |
report_segment_languages | Fields include impression_date, segment_languages, spend, impressions. | Incremental |
report_segment_political_status | Fields include impression_date, segment_political_status, spend, impressions. | Incremental |
report_segment_retargeting | Fields include impression_date, segment_retargeting, spend, impressions. | Incremental |
report_strategy | Fields include impression_date, advertiser_id, advertiser_name, campaign_id. | Incremental |
Schema
Data model at a glance
advertiser_app_ids
advertiser_idstringapp_idstring_singular_extract_timestampstring
advertisers
advertiser_idstringadvertiser_namestring_singular_extract_timestampstring
campaign_details
advertiser_idstringcampaign_idstringcampaign_namestringstrategy_idstringstrategy_namestringstart_datestringend_datestring_singular_extract_timestampstring
purchase_ids
impression_timestampstringevent_timestampstringevent_datestringpurchase_idstringevent_typestringevent_valuenumberadvertiser_idstringadvertiser_namestring
report_advertiser
impression_datestringadvertiser_idstringadvertiser_namestringspendnumberimpressionsnumberhouseholdsnumberfrequencynumbercpmnumber
report_app
impression_datestringapp_idstringosstringspendnumberimpressionsnumberhouseholdsnumberfrequencynumbercpmnumber
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Vibe in Extract workflows.
Why Extract
Why Extract for Vibe
- Rust-native performanceUse Rust-native data movement to refresh Vibe reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Vibe API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Vibe 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 Vibe 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 Vibe data with Extract?
Historical coverage for Vibe: Backfill (Days) (backfill): The backfill setting revisits and updates data from the past X days. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor Vibe data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Vibe 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 Vibe?
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 Vibe data model look like in the destination?
The Available streams table describes the datasets Extract can read from Vibe. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.