Before you set up data destinations to load aggregating marketing data into your database or platform, learn about the different data schemas that are available.
For the data schemas for user-level data, see Data Destinations: User-Level Data Schemas.
For an introduction to Singular data destinations and how they work, see Singular Data Destinations (ETL) FAQ.
When you use Singular data destinations, Singular standardizes data from different sources into the same data schema so you can easily query on it in your database.
Singular offers a choice of data schemas. Each schema defines a different set of dimensions and metrics that the Singular data destination will load into your database.
Use the list below to help you choose the data schema that best fits your needs.
Data Schema Options
Note: For more information about each field, see the Singular Metrics and Dimensions Glossary.
Useful for campaign- and network-level optimization. Keeps your data footprint small and manageable.
Dimensions:
data_connector_source_name
data_connector_id
data_connector_username
data_connector_timestamp_utc
app
source
os
platform
country_field
adn_sub_adnetwork_ name
adn_account_id
adn_account_name
adn_campaign_id
adn_campaign_name
adn_sub_campaign_id
adn_sub_campaign_name
adn_campaign_url
Metrics:
adn_cost
adn_original_cost
adn_original_currency
adn_impressions
adn_clicks
adn_installsSupports keyword-level optimization for search campaigns.
Dimensions:
data_connector_source_name
data_connector_id
data_connector_username
data_connector_timestamp_utc
app
source
os
platform
country_field
adn_sub_adnetwork_ name
adn_account_id
adn_account_name
adn_campaign_id
adn_campaign_name
adn_sub_campaign_id
adn_sub_campaign_name
adn_campaign_url
keyword_id
keyword
Metrics:
adn_cost
adn_original_cost
adn_original_currency
adn_impressions
adn_clicks
adn_installsHelps you optimize publisher-based bids, whitelists, and blacklists. May create large volumes of data!
Dimensions:
data_connector_source_name
data_connector_id
data_connector_username
data_connector_timestamp_utc
app
source
os
platform
country_field
adn_sub_adnetwork_ name
adn_account_id
adn_account_name
adn_campaign_id
adn_campaign_name
adn_sub_campaign_id
adn_sub_campaign_name
adn_campaign_url
keyword_id
keyword
publisher_id
publisher_site_id
publisher_site_name
Metrics:
adn_cost
adn_original_cost
adn_original_currency
adn_impressions
adn_clicks
adn_installsSupports creative-based optimization.
Dimensions:
data_connector_source_name
data_connector_id
data_connector_username
data_connector_timestamp_utc
app
source
os
platform
country_field
adn_sub_adnetwork_ name
adn_account_id
adn_account_name
adn_campaign_id
adn_campaign_name
adn_sub_campaign_id
adn_sub_campaign_name
adn_campaign_url
creative_type
adn_creative_id
adn_creative_name
creative_url
creative_image
creative_text
creative_width
creative_height
creative_is_video
asset_id
asset_name
Metrics:
adn_cost
adn_original_cost
adn_original_currency
adn_impressions
adn_clicks
adn_installsHelps you track your ad revenue.
Dimensions:
data_connector_source_name
data_connector_id
data_connector_username
data_connector_timestamp_utc
date
etl_query_timestamp_utc
app
source
os
platform
ad_country
ad_type_id
ad_type_name
ad_placement_id
ad_placement_name
instance_id
instance_name
original_revenue_currency
Metrics:
ad_requests
ad_impressions
ad_clicks
ad_revenue
original_ad_revenue
These schemas provide raw SKAdNetwork data as received in SKAdNetwork postbacks.
Both schemas include the same fields. The difference is that in one, the date given is the date in which the postback was received, while in the other, it's the estimated install date as calculated by Singular (see How does Singular calculate the install date/attribution date?).
Note: Due to Facebook's limitations, this schema does not include Facebook data.
Dimensions:
data_connector_id
data_connector_timestamp_utc
source
app
country_field
skan_app_id
skan_network_id
skan_campaign_id
skan_publisher_id
skan_conversion_value
skan_conversion_value_description
skan_redownloads
skan_validate
tracker_campaign_id
tracker_campaign_name
Metrics:
skan_installs
These schemas provide data about SKAdNetwork campaigns, based on decoded SKAdNetwork postbacks, along with network data about the same campaigns (if available).
Both schemas include the same fields. The difference is that in one, the date given is the date in which the postback was received, while in the other, it's the estimated install date as calculated by Singular (see How does Singular calculate the install date/attribution date?).
Dimensions:
source
app
country_field
unified_campaign_id
unified_campaign_name
skan_validated
Metrics:
adn_cost
custom_installs
custom_impressions
custom_clicks
skan_redownloads
skan_installs
skan_estimated_revenue
skan_estimated_roiFAQ
Can I change the data schema in the future?
Once you create and save the data destination, you cannot switch to another data schema. This is because changing the table structure in a live database with existing data comes with a number of dangers and complications.
If you need a data schema change, simply create a new destination. You can either delete the old table before creating the new destination or configure the new destination to load data to a new table with a different name. This process will ensure that your data stays safe and existing queries and work-loads continue working.
Can I create a custom data schema?
Singular supports custom schemas for premium customers. Any field supported in Singular's API is also supported in Singular data destinations (see Metrics and Dimensions for a full list). If you need a specific combination of fields that is not found in the existing schemas, discuss it with your Singular Customer Success Manager or contact Singular's support team.
