BigQuery destination for batch exports

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Batch exports can be used to export data to a BigQuery table.

Setting up BigQuery access

To set up the right permissions for a batch export targeting BigQuery, you need:

  1. A Service Account.
  2. A dataset which has permissions allowing the service account to access it.

Here's how to set these up so that the destination has access only to the dataset it needs:

  1. Create a Service Account.

Create service account

  1. Create a key for the Service Account you created in the previous step.
  2. Save the key file as JSON to upload it when configuring a batch export.

Create JSON private key

  1. Create a role which has only the specific permissions the batch export requires:
    • bigquery.datasets.get
    • bigquery.tables.create
    • bigquery.tables.get
    • bigquery.tables.list
    • bigquery.tables.updateData
    • (Optional, for mutable models) bigquery.tables.delete

Create custom role for batch exports

This step can be skipped if using the built-in roles BigQuery Data Editor and BigQuery Job User in the steps that follow.

  1. Grant the Service Account access to run jobs in your Google Cloud project. This can be done by granting the BigQuery Jobs User role or the role we created in the previous step on your project.

Navigate to IAM and click on Grant Access to arrive at this screen:

Project grant

In the screenshot above, we have used a custom role named Testing PostHog BatchExports with the permissions listed in the previous step.

  1. Create a dataset within a BigQuery project (ours is called BatchExports, but any name will do).
  2. Use the Sharing and Add Principal buttons to grant access to your dataset with your Service Account created in step 1. Next, assign either the BigQuery Data Editor role or your custom role created in step 4 to provide permissions for the dataset access. Read the full instructions on granting access to the dataset in BigQuery if unclear.

Sharing dataset Add principal

In the screenshot below, we grant our Service Account access to the BatchExports data set and assign the Testing PostHog BatchExports role permissions for it.

Dataset grant access

  1. All done! After completing these steps you can create a BigQuery batch export in PostHog and your data will start flowing from PostHog to BigQuery.


This section describes the models that can be exported to BigQuery.

Events model

This is the default model for BigQuery batch exports. The schema of the model as created in BigQuery is:

uuidSTRINGThe unique ID of the event within PostHog
eventSTRINGThe name of the event that was sent
propertiesSTRING or JSONA JSON object with all the properties sent along with an event
elementsSTRINGThis field is present for backwards compatibility but has been deprecated
setSTRING or JSONA JSON object with any person properties sent with the $set field
set_onceSTRING or JSONA JSON object with any person properties sent with the $set_once field
distinct_idSTRINGThe distinct_id of the user who sent the event
team_idINT64The team_id for the event
ipSTRINGThe IP address that was sent with the event
site_urlSTRINGThis field is present for backwards compatibility but has been deprecated
timestampTIMESTAMPThe timestamp associated with an event
bq_ingested_timestampTIMESTAMPThe timestamp when the event was sent to BigQuery

Some fields can be either STRING or JSON type depending on whether the corresponding checkbox is marked or not when creating the batch export.

Persons model

The schema of the model as created in BigQuery is:

team_idINT64The id of the project (team) the person belongs to
distinct_idSTRINGA distinct_id associated with the person
person_idSTRINGThe id of the person associated to this (team_id, distinct_id) pair
propertiesSTRING or JSONA JSON object with all the latest properties of the person
person_versionINT64The version of the person properties associated with a (team_id, distinct_id) pair
person_distinct_id_versionINT64The version of the person to distinct_id mapping associated with a (team_id, distinct_id) pair

The BigQuery table will contain one row per (team_id, distinct_id) pair, and each pair is mapped to their corresponding person_id and latest properties. The properties field can be either STRING or JSON, depending on whether the corresponding checkbox is marked or not when creating the batch export.

How is the persons model kept up to date?

Exporting mutable data (like the persons model) requires executing a merge operation to apply new updates to existing rows. Executing a merge in BigQuery involves the following steps:

  1. Creating a stage table.
  2. Inserting new data into stage table.
  3. Execute a merge operation between existing table and stage table. a. Any rows that match in the final table and for which the stage table's version is higher are updated. b. Any new rows not found in the final table are inserted.
  4. Drop the stage table.

Besides the permissions required for exporting the events model, and since we need to clean-up a stage table, exporting the persons model requires also bigquery.tables.delete permissions.

Creating the batch export

  1. Subscribe to data pipelines add-on in your billing settings if you haven't already.
  2. Click Data pipelines in the navigation and go to the exports tab in your PostHog instance.
  3. Click "Create export workflow".
  4. Select BigQuery as the batch export destination.
  5. Fill in the necessary configuration details.
  6. Finalize the creation by clicking on "Create".
  7. Done! The batch export will schedule its first run on the start of the next period.

BigQuery configuration

Configuring a batch export targeting BigQuery requires the following BigQuery-specific configuration values:

  • Table ID: The ID of the destination BigQuery table. This is not the fully-qualified name of a table, so omit the dataset and project IDs. For example for the fully-qualified table name project-123:dataset:MyExportTable, use only MyExportTable as the table ID.
  • Dataset ID: The ID of the BigQuery dataset which contains the destination table. Only the dataset ID is required, so omit the project ID if present. For example for the fully-qualified dataset project-123:my-dataset, use only my-dataset as the dataset ID.
  • Google Cloud JSON key file: The JSON key file for your BigQuery Service Account to access your instance. Generated on Service Account creation. See here for more information.


These examples illustrate how to use the data from batch exports in BigQuery.


Two batch exports need to be created:

  • An events model batch export.
  • A persons model batch export.

For the purposes of these examples, assume that these two batch exports have already been created and have exported some data to BigQuery in tables and example.persons.

Example: Count unique persons that have triggered an event

The following query can be used to count the number of unique persons that have triggered events:

COUNT(persons.person_id) AS unique_persons_count
FROM AS events
example.persons AS persons ON events.distinct_id = persons.distinct_id AND events.team_id = persons.team_id
persons.person_id IS NOT NULL
unique_persons_count DESC


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