Guides
You must grant ‘bigquery.dataViewer’ access to Analytics’ service account for your BigQuery project. To perform the following steps, you must have administrative access to the BigQuery console and your BigQuery database.
For this self-service integration, we also have some data requirements:
We can still support any integrations that do not meet the above requirements, but you must contact a product specialist. If additional enrichments are required, such as joining with user property tables or deriving custom user_ids, please contact us.
This integration works by sharing the dataset with Analytics’ service account and only requires read-only access to that dataset. Analytics takes on the cost of the query and caches this data in Analytics’ proprietary analytics engine.
integrations@indicative-988.iam.gserviceaccount.com
Start Date
Select the date from where Analytics should load your data from.
If your data history exceeds 1 billion events, a Solutions Engineer will contact you to assist with the integration.
etl_tstamp
. If you select to load data every 3, 6, or 12 hours, make sure to select a load timestamp field with at least hour precision (not a date only field).For example, if an event with an event timestamp of 12/1 was published to the table on 12/3, this will not be collected unless we use the publishing timestamp since every daily extract would look for events that occurred on 12/3. Using the publishing timestamp will allow us to extract all new data that was published to the table on a nightly basis.
se_action
field, but it completely depends on your implementation. We will first look at this field’s value to use as the event name in Analytics. If this value is null
, then we will use the event_name
field. If this field’s value is also null
, we will then use the event
field. If you are not using Snowplow structured events, select none
.derived_tstamp
.collector_tstamp
- Timestamp for the event recorded by the collector.dvce_created_tstamp
- Timestamp for the event recorded on the client device.dvce_sent_tstamp
- When the event was actually sent by the client device.etl_tstamp
- Timestamp for when the event was validated and enriched. Note: the name is historical and does not mean that the event is loaded at this point (this is further downstream).derived_tstamp
- Timestamp making allowance for inaccurate device clock.true_tstamp
- User-set “true timestamp” for the event.After this step, we will perform a few checks on your data with the model that you provided. The checks are:
After some basic checks, we can define your users within your data. For more information on User Identification (Aliasing), please refer to this article.
Atomic - If the anonymous ID field is an atomic field, select this option.
Context - If your anonymous ID is contained within the Contexts field, choose this option.
Type - Select the Snowplow field type
Atomic - If the anonymous ID field is an atomic field, select this option.
Context - If your anonymous ID is contained within the Contexts field, choose this option.
If you have a non-null value that represents null UserID values, please click on the Show Advanced button. In this field, please enter these non-null values.
After this step, we will perform additional checks on your data with the user model that you provided. The checks are:
You should see a summary of your data based on the last 7 days in three main blocks.
You should only be concerned if the margin of error is significant. If so, please reach out to a product specialist:
If you would like to exclude any events by regex or property value, please contact a product specialist.
If this section looks good, click Next.
If you require more advanced configurations such as parsing out JSON fields, creating derived properties, or excluding properties based on regex, please contact a product specialist.
If this section looks good, click Next.
If this section looks good, click Next.
If you see this screen, you’re all done! You should see your data in Analytics within 48-72 hours and will be notified by email.
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