06:00 UTC
PostHog
Selected accounts
Configured job
BigQuery
Destination table
Join marketing, finance, CRM, and product data inside the same warehouse.
Metrics availables
Dimensions availables
PostHog
3 advertising accounts
Connected
Google Ads
2 advertising accounts
Connected
Google Analytics 4
1 web property
Connected
HubSpot
1 CRM portal
Connected
Select your Google Cloud project and BigQuery dataset. Catchr automatically prepares the destination and maps your data into analytics-ready tables.
Catchr continuously imports new and updated data into BigQuery.
Your warehouse stays fresh automatically, ready for SQL, dbt, BI tools, and AI workloads.
Open the dedicated route for each platform. Its field catalog, reporting grain, historical constraints, and warehouse use cases should be specific to that source.
Answers about available Meta Ads data, reporting cadence, history, table settings, attribution changes, and downstream joins.
Add PostHog as a source in Catchr, enter the API key and instance base URL, choose Big Query as the destination, and authorize the target Google Cloud project and dataset.
You can export raw PostHog event rows with the connector’s available fields, including a Records row counter, UTM Campaign, Date, Hour of the day, Week, Month, Quarter, and Year dimensions.
Yes. Catchr requires the explicit base URL for your PostHog instance, allowing the connector to work with US, EU, and self-hosted deployments.
Use the exported Date, Hour, Week, Month, Quarter, and Year dimensions to group event rows into reporting periods and compare activity across campaigns or time ranges.
Yes. Keep PostHog and other sources in separate Big Query tables, then use SQL views or models to join compatible fields such as date or UTM campaign for broader marketing reporting.
If your selected schema contains an eligible date field, you can configure that field as the optional date partition for the destination table. Catchr does not configure table clustering or design your downstream analytical models.
Yes. Configure the historical period supported by the source for the initial import, then keep the table updated with recurring exports. The available range is determined by the source API and the settings of your job, not an unlimited-history promise.
Our teams is always here to responds to any question you could have about our data connector.
Connect your accounts, choose the fields then send the configured export to your BigQuery project.
.svg)
.png){kind=logo}
