Receive zero-party data in BigQuery

Connect a BigQuery dataset in Settings > Data Sources. Brand Control Center creates a genuin_data table and fills it with your community's event rows on a daily, weekly, or monthly schedule.

Your community generates zero-party data: what members watch, search for, join, post, react to, and click. Members produce it by taking part, not by being tracked across other sites. How zero-party data powers communities and media revenue explains the model. This page shows how to land that data in your own warehouse, where you own it and can join it with the rest of your business data.

What arrives in BigQuery

Brand Control Center creates one table, genuin_data, in the dataset you name. It never creates a second table or writes anywhere else in your project. Each row is one event from your apps, embeds, and communities, timestamped in UTC. The table has 145 nullable columns in seven groups.

Group	Example columns	What it tells you
Event	_timestamp, event, brand_id, content_id, content_type, conv_type	What happened, when, and to which piece of content
Member intent	topic_ids, communities_selected, keyword_searched, search_source, question, selected_option	Interests chosen, searches run, prompts answered
Watching and posting	video_view_length, length_at_video_paused, video_duration, upload_video_length, link_added	How much of a video was watched, how long a post ran, whether a link-out was added
App and device context	context_app_name, context_app_version, context_os_name, context_device_model, context_session_id, context_timezone, country_code	Where the event came from, grouped by session
Campaign	utm_source, utm_medium, utm_campaign, campaign, media_source, embed_id	Which campaign or embed brought the member in
Content records	master_video_, master_community_, master_loops_, master_groups_, master_brand_	The video, community, loop, group, and brand the event refers to, joined in so you don't have to
Comment and share records	master_c_video_, master_c_user_uuid, master_c_community_, master_s_user_uuid	For comments and shares, the video commented on and the member who shared

The master_* columns are joined from Genuin's own records, so a single WHERE master_community_handle = 'running' filter works without a second table. Rows are written in batches on the schedule you choose, not in real time.

Before you begin

A Google Cloud project with a BigQuery dataset that will hold the table. Note the project ID and dataset name. You enter them as one value, project_id.dataset_name.
An account for the connection. A Service account is the right choice for a production warehouse, because its access is scoped to one dataset and survives staff changes. A User account signs in with Google and needs permission to create, update, and list tables in the dataset.
Edit access to Data Sources in Brand Control Center. A role with view-only access sees the form disabled.

Step 1: Give a service account the BigQuery Data Editor role

Skip steps 1 and 2 if you'll sign in with a User account.

In Google Cloud, open IAM & Admin > Service Accounts and create a service account for the connection. Create a JSON key for it and download the file.
Open IAM, find the service account, and open Edit access.
Under Assign roles, pick BigQuery Data Editor. The role lets the connection create the table and append rows.

BigQuery Data Editor is the only role the connection needs.

Step 2: Limit the role to one dataset

Still in Edit access, add an IAM condition to the role.
In the condition builder, add two rules joined with AND:
- Service is bigquery.googleapis.com
- Name starts with projects/<project_id>/datasets/<dataset_name>
Save the condition, then save the role.

Two rules limit the service account to the one dataset Brand Control Center writes to.

With the condition in place, the service account has no access to any other dataset in the project.

Step 3: Open the BigQuery data source

In Brand Control Center, open Settings > Data Sources.
Expand BigQuery. The panel sits below S3 Storage and Mix Panel.

The BigQuery panel before a connection. Account type defaults to User.

Step 4: Connect the dataset

Fill the three fields, then connect with the account type you chose.

Field	Value
Account type	User or Service. Locked once connected.
Dataset ID	project_id.dataset_name. Letters, digits, hyphens, and underscores, with exactly one dot.
Data Sync Frequency	Daily, Weekly, or Monthly. The only setting you can change later.

User account

Pick User, enter the Dataset ID, and pick a frequency.
Select Sign in with Google and approve the request. The consent screen asks for your email, profile, and BigQuery access.

With a User account, the Google sign-in completes the connection.

Service account

Pick Service, enter the Dataset ID, and pick a frequency.
Under Upload JSON File, upload the key file from step 1. The file has to be a service account key under 10 KB, and the form reads the project ID and key ID from it.
Select Connect.

The Connect button becomes active once the Dataset ID, frequency, and key file are in place.

On connect, Brand Control Center lists the tables in the dataset. If genuin_data is missing, it creates the table with the schema above. If the table is already there, from an earlier connection, it keeps it and appends to it. A notification confirms the connection, the banner names the table, and the Dataset ID and Account type lock.

Once connected, the banner points to genuin_data and only the frequency stays editable.

If the connection fails, the usual cause is permissions. The user or service account lacks create, update, or list rights on the dataset, or the IAM condition names a different dataset.

Step 5: Query the table

Open the dataset in the BigQuery console. The genuin_data table appears under it, with the schema ready before the first rows arrive.

The table and its 145 columns exist as soon as the connection succeeds.

Rows land on the schedule you picked. A first query that shows which events arrived in the past week:

Events per day

sql

SELECT
  DATE(_timestamp) AS day,
  event,
  COUNT(*) AS events
FROM `project_id.dataset_name.genuin_data`
WHERE _timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
GROUP BY day, event
ORDER BY day DESC, events DESC;

From there, join on master_video_uuid or master_community_uuid to your own catalogue, or on utm_campaign to your campaign table.

Change the sync frequency

Open Settings > Data Sources > BigQuery.
Pick a new Data Sync Frequency. The button changes from Disconnect to Update.
Select Update.

Nothing else is editable while connected. To change the dataset or the account type, disconnect and connect again.

Disconnect

Open Settings > Data Sources > BigQuery and select Disconnect.
Confirm in the dialog.

Step 2: Disconnecting stops the schedule. It leaves the table and its rows in your dataset.

Brand Control Center drops its stored credentials and schedule. The genuin_data table and every row already written stay in your dataset. You can reconnect at any time. Reconnecting to the same dataset reuses the existing table, so history and new rows stay together.

Limits

Item	Limit
Datasets per brand	One
Table name	genuin_data, fixed
Columns	145, all nullable, managed by Genuin
Sync frequency	Daily, Weekly, or Monthly
Key file	Service account JSON, under 10 KB
Dataset ID	project_id.dataset_name, letters, digits, hyphens, underscores

Limitations

Batch delivery only. Rows arrive on the chosen schedule, not as events happen.
One dataset and one table. The brand connects a single dataset, and all events share genuin_data.
Schema is fixed. New columns arrive with product releases. Build queries that tolerate added columns.
Dataset and account type lock on connect. Changing either means disconnect and reconnect.