16.2 Create your first query in BigQuery
- Explore the BigQuery UI
- Create a SQL query within BigQuery
- Save your SQL query's results in a dataset within BigQuery
When Google Analytics data is in BigQuery, dimensions, metrics and other variables are all nested. Also, Google Analytics data is loaded daily into different tables. This means that trying to connect Google Analytics tables within BigQuery to Adobe Experience Platform directly is very hard and not a good idea.
The solution to this problem is to transform Google Analytics data into a readable format to make the ingestion into Adobe Experience Platform easier.
Exercise 16.2.1 – Create a dataset to save new BigQuery Tables
Go to the BigQuery Console .
Under Resources , you'll see your Project ID:
Click your Project ID (don't click on the bigquery-public-data dataset).
You can see that there isn't a dataset yet, so let's create one now.
Click CREATE DATASET .
On the right side of your screen, you'll see the Create dataset menu.
For the Dataset ID , use the below naming convention. For the other fields, please leave the default settings.
Next, click Create dataset .
You'll then be back in the BigQuery Console with your dataset created.
Exercise 16.2.2 – Create your first SQL BigQuery
Next, you'll create your first query in BigQuery. The goal of this query is to take the Google Analytics sample data and transform it so that it can be ingested in Adobe Experience Platform.
Please copy the following SQL query and paste it into that Query Editor. Feel free to read the query and understand the Google Analytics BigQuery syntax.
SELECT CONCAT(fullVisitorId, CAST(hitTime AS String), '-', hitNumber) AS _id, TIMESTAMP(DATETIME(Year_Current, Month_Current, Day_Current, Hour, Minutes, Seconds)) AS timeStamp, fullVisitorId as GA_ID, -- Fake CUSTOMER ID CONCAT('3E-D4-',fullVisitorId, '-1W-93F' ) as customerID, Page, Landing_Page, Exit_Page, Device, Browser, MarketingChannel, TrafficSource, TrafficMedium, -- Enhanced Ecommerce TransactionID, CASE WHEN EcommerceActionType = '2' THEN 'Product_Detail_Views' WHEN EcommerceActionType = '3' THEN 'Adds_To_Cart' WHEN EcommerceActionType = '4' THEN 'Product_Removes_From_Cart' WHEN EcommerceActionType = '5' THEN 'Product_Checkouts' WHEN EcommerceActionType = '6' THEN 'Product_Refunds' ELSE NULL END AS Ecommerce_Action_Type, -- Entrances (metric) SUM(CASE WHEN isEntrance = TRUE THEN 1 ELSE 0 END ) AS Entries, --Pageviews (metric) COUNT(*) AS Pageviews, -- Exits SUM( IF (isExit IS NOT NULL, 1, 0)) AS Exits, --Bounces SUM(CASE WHEN isExit = TRUE AND isEntrance = TRUE THEN 1 ELSE 0 END ) AS Bounces, -- Unique Purchases (metric) COUNT(DISTINCT TransactionID) AS Unique_Purchases, -- Product Detail Views (metric) COUNT(CASE WHEN EcommerceActionType = '2' THEN fullVisitorId ELSE NULL END ) AS Product_Detail_Views, -- Product Adds To Cart (metric) COUNT(CASE WHEN EcommerceActionType = '3' THEN fullVisitorId ELSE NULL END ) AS Adds_To_Cart, -- Product Removes From Cart (metric) COUNT(CASE WHEN EcommerceActionType = '4' THEN fullVisitorId ELSE NULL END ) AS Product_Removes_From_Cart, -- Product Checkouts (metric) COUNT(CASE WHEN EcommerceActionType = '5' THEN fullVisitorId ELSE NULL END ) AS Product_Checkouts, -- Product Refunds (metric) COUNT(CASE WHEN EcommerceActionType = '7' THEN fullVisitorId ELSE NULL END ) AS Product_Refunds FROM ( SELECT -- Landing Page (dimension) CASE WHEN hits.isEntrance = TRUE THEN hits.page.pageTitle ELSE NULL END AS Landing_page, -- Exit Page (dimension) CASE WHEN hits.isExit = TRUE THEN hits.page.pageTitle ELSE NULL END AS Exit_page, hits.page.pageTitle AS Page, hits.isEntrance, hits.isExit, hits.hitNumber as hitNumber, hits.time as hitTime, date as Fecha, fullVisitorId, visitStartTime, device.deviceCategory AS Device, device.browser AS Browser, channelGrouping AS MarketingChannel, trafficSource.source AS TrafficSource, trafficSource.medium AS TrafficMedium, hits.transaction.transactionId AS TransactionID, CAST(EXTRACT(YEAR FROM CURRENT_DATE()) AS INT64) AS Year_Current, CAST(EXTRACT(MONTH FROM CURRENT_DATE()) AS INT64) AS Month_Current, CAST(EXTRACT(DAY FROM CURRENT_DATE()) AS INT64) AS Day_Current, CAST(EXTRACT(DAY FROM DATE_SUB(CURRENT_DATE(),INTERVAL 1 DAY)) AS INT64) AS Day_Current_Before, CAST(FORMAT_DATE('%Y', PARSE_DATE("%Y%m%d", date)) AS INT64) AS Year, CAST(FORMAT_DATE('%m', PARSE_DATE("%Y%m%d",date)) AS INT64) AS Month, CAST(FORMAT_DATE('%d', PARSE_DATE("%Y%m%d",date)) AS INT64) AS Day, CAST(EXTRACT (hour FROM TIMESTAMP_SECONDS(hits.time)) AS INT64) AS Hour, CAST(EXTRACT (minute FROM TIMESTAMP_SECONDS(hits.time)) AS INT64) AS Minutes, CAST(EXTRACT (second FROM TIMESTAMP_SECONDS(hits.time)) AS INT64) AS SecondS, hits.eCommerceAction.action_type AS EcommerceActionType FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*`, UNNEST(hits) AS hits WHERE _table_suffix BETWEEN '20170101' AND '20170331' AND totals.visits = 1 AND hits.type = 'PAGE' ) GROUP BY 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ORDER BY 2 DESC
When you are ready, click Run to run the query:
Executing the query can take a couple of minutes.
Once the query has finished running, you'll see the below output in the Query results .
Exercise 16.2.3 – Save the results of your BigQuery SQL query
The next step is to save the output of your query by clicking the SAVE RESULTS button.
As the location for your output, select BigQuery table .
You'll then see a new popup, where your Project Name and Dataset Name are pre-populated. The dataset name should be the dataset that you created in the beginning of this exercise, with this naming convention:
You now need to enter a Table name. Please use this naming convention:
Click SAVE .
In a real-world scenario, brands typically like to have new data coming in daily into the table that was just created. To do that, brands can use the Schedule Query option. This feature will schedule the query we created to run every day and save the output in this table.
For this module, it's not required to setup a Schedule .
It takes some time until the data is ready in the table we've created. After a couple of minutes, refresh the browser. You should then see within your dataset the ldap_GAdataTableBigquery table under Resources inside your BigQuery project.
You con now continue with the next exercise, where you'll connect this table to Adobe Experience Platform.