> ## Documentation Index > Fetch the complete documentation index at: https://data-foundation.rockerbox.com/llms.txt > Use this file to discover all available pages before exploring further. # Platform - Google ## Description The **Platform – Google** dataset contains aggregated performance metrics from Google Ads (formerly AdWords), including spend, impressions, clicks, and conversion data. *** ## Partition Keys * `identifier` * `date` 💡 **Note:** Leverage partition keys when querying the table to improve query efficiency. *** ## Logical Primary Key These fields uniquely identify a record. While data warehouses do not enforce primary key constraints, this combination functions as the logical primary key for the table. * `identifier` * `date` * `utc_hour` * `campaign_id` * `ad_group_id` * `ad_network_type` *** ## Field Reference | Order | Name | Description | | ----- | --------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 1 | advertiser | The name of the Rockerbox account associated with the data export. | | 2 | type | The dataset type. For this table, the value will be `platform_data`. | | 3 | platform | The advertising platform name. For this dataset, the value is `Adwords` (Google Ads). | | 4 | report | The name of the dataset. For this table, the value is `platform_performance_ad`. | | 5 | identifier | The unique identifier of the advertiser account in Google Ads. | | 6 | date | The calendar date associated with the reported performance metrics. | | 7 | utc\_hour | The UTC hour during which the activity occurred. | | 8 | tier\_1 | Marketing channel categorization level 1, as defined in your Rockerbox configuration. | | 9 | tier\_2 | Marketing channel categorization level 2, as defined in your Rockerbox configuration. | | 10 | tier\_3 | Marketing channel categorization level 3, as defined in your Rockerbox configuration. | | 11 | tier\_4 | Marketing channel categorization level 4, as defined in your Rockerbox configuration. | | 12 | tier\_5 | Marketing channel categorization level 5, as defined in your Rockerbox configuration. | | 13 | mta\_tiers\_join\_key | The unique identifier used to join spend data from Google Ads to Rockerbox MTA datasets. This is typically the `ad_id`, but may vary depending on your account setup. | | 14 | ad\_network\_type | The Google Ads network where the ad was served (e.g., `SEARCH`, `SEARCH_PARTNERS`, `MIXED`, `YOUTUBE_WATCH`). | | 15 | advertising\_channel\_type | The primary advertising channel type for the campaign (e.g., `SEARCH`, `DISPLAY`, `SHOPPING`). | | 16 | campaign\_name | The name of the campaign in Google Ads. A campaign may contain one or more ad groups. | | 17 | campaign\_id | The unique identifier of the campaign in Google Ads. | | 18 | ad\_group\_type | The type of ad group (e.g., `SEARCH_STANDARD`, `DISPLAY_STANDARD`). | | 19 | ad\_group\_name | The name of the ad group. An ad group contains one or more ads. | | 20 | ad\_group\_id | The unique identifier of the ad group in Google Ads. | | 21 | spend | The total estimated spend in the account’s local currency for the given date and dimension breakdown. | | 22 | currency\_code | The local currency of the Google Ads account (e.g., `USD`, `EUR`). | | 23 | spend\_usd | The total estimated spend converted to USD. | | 24 | clicks | The number of clicks recorded on the ads. | | 25 | impressions | The number of times ads were served across Google properties or partner networks. | | 26 | cost\_micros | Ad spend in micro units of the account’s local currency. For example, \$1.23 is represented as 1,230,000 (1.23 × 1,000,000). | | 27 | all\_conversions\_by\_conversion\_date | The total number of conversions attributed based on the actual conversion date. | | 28 | all\_conversions | The total number of conversions attributed based on the date of the associated advertising interaction (impression or click). | | 29 | view\_through\_conversions | The number of conversions attributed to impressions without a click interaction. | | 30 | all\_conversions\_value\_by\_conversion\_date | The total value of conversions attributed based on the actual conversion date. | | 31 | all\_conversions\_value | The total value of conversions attributed based on the date of the associated advertising interaction (impression or click). | | 32 | view\_through\_lookback\_window\_days | The maximum number of days between an impression and a conversion for the conversion to be attributed without an interaction. | | 33 | click\_through\_lookback\_window\_days | The maximum number of days between a click and a conversion for the conversion to be attributed. | | 34 | rb\_sync\_id | Internal identifier used by Rockerbox to sync this dataset to your data warehouse. | | 35 | updated\_at | Timestamp indicating when the record was last updated in Rockerbox. | *** ## Nested Fields The following fields are nested JSON objects keyed by Facebook conversion event name: * `all_conversions_by_conversion_date` * `all_conversions` * `view_through_conversions` * `all_conversions_value_by_conversion_date` * `all_conversions_value` * `view_through_lookback_window_days` * `click_through_lookback_window_days` ### Example Stored Object ```json theme={null} { "purchase": 12, "add_to_cart": 41 } ``` ### Snowflake - Querying Nested Fields #### Extract a Single Event ```sql theme={null} select date, ad_id, all_conversions_by_conversion_date:"purchase"::number as purchase, all_conversions_value_by_conversion_date:"purchase"::float as purchase_value from ..; ``` #### Flatten All Events Into Rows ```sql theme={null} select t.date, t.ad_id, f.key as conversion_event, f.value::number as all_conversions from .. t, lateral flatten(input => t.all_conversions_by_conversion_date) f; ``` ### Redshift - Querying Nested Fields (SUPER type) #### Extract a Single Event ```sql theme={null} select date, ad_id, all_conversions_by_conversion_date['purchase']::int as purchase, all_conversions_value_by_conversion_date['purchase']::decimal(18,4) as purchase_value from ..; ``` #### Flatten All Events Into Rows ```sql theme={null} select select t.date, t.ad_id, kv.key as conversion_event, kv.value::int as conversions from .. t, t.all_conversions_by_conversion_date as kv; ``` ### BigQuery - Querying Nested Fields (JSON type) #### Extract a Single Event ```sql theme={null} select date, ad_id, cast(json_value(all_conversions_by_conversion_date, '$.purchase') as int64) as purchase, cast(json_value(all_conversions_value_by_conversion_date, '$.purchase') as float64) as purchase_value from ..; ``` #### Flatten All Events Into Rows ```sql theme={null} select t.date, t.ad_id, k as conversion_event, cast(json_value(t.all_conversions_by_conversion_date, concat('$.', k)) as int64) as conversions from .. t, unnest(json_keys(t.all_conversions_by_conversion_date)) as k; ```