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\n \n \n \n \n \n \n \n Prerequisites

\n

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.

\n

For this self-service integration, we also have some data requirements:

\n
    \n
  1. All of your events must be unified into one table instead of having separate tables for each event type.
    2. \n
  2. There can only be a maximum of one authenticatedID and one unauthenticated ID for aliasing.
    3. \n
  3. The event timestamp must be in UTC.
    4. \n
  4. All joins must be done beforehand.
    5. \n
  5. Shared tables, meaning if your BigQuery tables end with the _MMDDYYYY format, are not currently supported.
    6. \n
\n

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.

\n

\n \n \n \n \n \n \n \n Instructions

\n

\n \n \n \n \n \n \n \n Adding a Data Source In Analytics

\n
    \n
  1. In Analytics, click on the gear icon and select Project Settings.\n\"Project
    2. \n
  2. Select the Data Sources tab.\n\"Data
    3. \n
  3. Select New Data Source.\n\"New
    4. \n
  4. Select Connect via Data Warehouse or Lake.\n\"Connect
    5. \n
  5. Select BigQuery as your data connection and Snowplow as the connection schema and click Connect.\n\"BigQuery
    6. \n
  6. You should see this Google + Snowplow Overview screen. Click Next.\n\"Google
    7. \n
\n

\n \n \n \n \n \n \n \n Connection Information

\n

\"Connection

\n
    \n
  1. Open the BigQuery console on Google Cloud Platform and Select a project.
    2. \n
  2. Enter the GCP Project ID containing your Snowplow data.\n\"GCP
    3. \n
  3. Enter the Dataset Name.\n\"Dataset
    4. \n
  4. Enter the Table Name and click Next in Analytics.\n\"Table
    5. \n
\n

\n \n \n \n \n \n \n \n Grant Permissions

\n

\"Grant

\n

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.

\n
    \n
  1. Within the BigQuery Console, select your Project and your dataset from the previous section.
    2. \n
  2. Click on Share Dataset.
    3. \n
\n

\"BigQuery

\n
    \n
  1. In the Dataset Permissions panel, in the Add Members field, place the user below.
    2. \n
\n

integrations@indicative-988.iam.gserviceaccount.com

\n
    \n
  1. In the Select a Role dropdown, select BigQuery Data Viewer and click Add.\n\"BigQuery
    2. \n
\n

\n \n \n \n \n \n \n \n Data Loading

\n

\"Data

\n
    \n
  1. \n

    Start Date\nSelect the date from where Analytics should load your data from.

    \n
    \n

    If your data history exceeds 1 billion events, a Solutions Engineer will contact you to assist with the integration.

    \n
    \n
    2. \n
  2. Schedule Interval\nSelect the frequency to make new data available in Analytics.
    3. \n
  3. Processing Delay\nSelect when we should start extracting your data in UTC. This time should be when all of your previous day’s data is fully available in your table for extraction.
    4. \n
  4. Load Timestamp Field\nSelect the field used to identify new data. We recommend using a timestamp that denotes when the event was published, not the actual event timestamp to allow for late data to be collected. This will not impact your analyses since we reference the event timestamp for our queries. For Snowplow, we recommend 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).
    5. \n
\n

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.

\n

\n \n \n \n \n \n \n \n Event Modeling

\n

\"Event

\n
    \n
  1. In the Structured Event Name section, select the field that should be used to derive Analytics event names. Typically, most customers use the 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.
    2. \n
  2. For Timestamp, select the field that represents the time that the event was performed. Analytics will use this field to run its queries. If unsure, leave as derived_tstamp.
    3. \n
\n\n
    \n
  1. For Vendor Name, input the Snowplow vendor names used so we can simplify your event property names.
    2. \n
  2. Click Next.
    3. \n
\n

After this step, we will perform a few checks on your data with the model that you provided. The checks are:

\n\n

\n \n \n \n \n \n \n \n User Modeling

\n

\"User

\n

After some basic checks, we can define your users within your data. For more information on User Identification (Aliasing), please refer to this article.

\n
    \n
  1. If you choose to enable Aliasing, click on Enabled:
    2. \n
  2. Type - Select the Snowplow field type
    3. \n
\n\n
    \n
  1. If you choose to disable Aliasing, press Disabled:
    2. \n
\n\n

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.

\n
\n

After this step, we will perform additional checks on your data with the user model that you provided. The checks are:

\n \n
\n

\n \n \n \n \n \n \n \n Assisted Modeling

\n

\"Assisted

\n

You should see a summary of your data based on the last 7 days in three main blocks.

\n
\n

You should only be concerned if the margin of error is significant. If so, please reach out to a product specialist:

\n
\n
    \n
  1. Events Summary\nYou should see a daily breakdown of your Total Event Count, and the number of Unique Event Names. If there are certain events to exclude, please click on the Exclude checkbox for those events.
    2. \n
\n

If you would like to exclude any events by regex or property value, please contact a product specialist.

\n

If this section looks good, click Next.

\n
    \n
  1. Properties Summary\nHere you will see the number of Unique Event Property Names. If there are certain properties to exclude, please click on the Exclude checkbox for those events.
    2. \n
\n

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.

\n

If this section looks good, click Next.

\n
    \n
  1. Users Summary\nThis section lists the number of Unique users seen. If the numbers do not look correct, please go back to the User Modeling section to confirm that the correct ID was chosen. Please note that the counts may not reflect exactly what gets loaded into Analytics due to aliasing and other event modeling configurations.
    2. \n
\n

If this section looks good, click Next.

\n

\n \n \n \n \n \n \n \n Waiting For Data

\n

\"Waiting

\n

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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