I have an issue with the GEO and Google Storage Transfer service. I use it to pull Google Ads data in to BigQuery. It's not empty but the numbers don't match. E.g. a query like this
SELECT _DATA_DATE, sum(clicks) FROM project.dataset.GeoStats_XXX where LocationType='LOCATION_OF_PRESENCE' and IsTargetingLocation IN (true, false) group by 1
returns a smaller number than the real one, the one that I get if I just create the same report via Google Ads UI. I've tried different manipulations with the LocationType and IsTargetingLocation filters but nothing seems to work.
I see no errors in Storage Transfer service logs
The CampaignStat returns almost correct data but there is no GEO info there and I need the country breakdown. Not Target Location but GEO (location of presence)
I expect the numbers returned by my query to match the numbers that I see via Google Ads UI
Related
We are using the Google Ads transfer in BigQuery to ingest our Google Ads data. One thing I have noticed when querying the results is that all of the metrics are exactly 156x of the values we would expect in the Google Ads UI (cost, clicks, etc.)
We have tested multiple transfers and each time we have this same issue. The transfer process seems pretty straight forward, but am I missing something? Has anyone else noticed a similar issue or have any ideas of what to look at to adjust in the data transfer?
For which tables do you notice this behavior?
The dimension tables such as Customer, Campaign, AdGroup are exported every day and so are partitioned by day.
This could cause your duplication?!
You only need the latest partition/day.
So this is for example how I get the latest account / customer data:
SELECT
-- main reason I cast all the id's to string is because BI reporting tool will not see it as a metric but as a dimension field
CAST(customer_id AS STRING) AS account_id, --globally unique, see also: https://developers.google.com/google-ads/api/docs/concepts/api-structure
customer_descriptive_name,
customer_auto_tagging_enabled,
customer_currency_code,
customer_manager,
customer_test_account,
customer_time_zone,
_DATA_DATE AS date, --source table is paritioned on date
_LATEST_DATE,
CASE WHEN _DATA_DATE = _LATEST_DATE THEN TRUE ELSE FALSE END is_most_recent_record
FROM
`YOURPROJECTID.google_ads.ads_Customer_YOURID`
WHERE
_DATA_DATE = _LATEST_DATE
I have a Google Ads account which has a single Smart Campaign and multiple usual campaigns. Also I've set up a data transfer to Google BigQuery. When I try to compare BigQuery data using the query
SELECT sum(Cost) FROM `project.dataset.AccountBasicStats_XXXXXX` where Date between '2021-12-01' and '2021-12-31'
the query result shows a less cost than I see in the Google Ads interface for the same time period. The difference is equal to the spend of my smart campaign. To check this, I've tried the queries:
SELECT * FROM `project.dataset.CampaignBasicStats_XXXXXX` where Date between '2021-12-01' and '2021-12-31' AND CampaignId = {ID of my smart campaign}
SELECT * FROM `project.dataset.CampaignStats_XXXXXX` where Date between '2021-12-01' and '2021-12-31' AND CampaignId = {ID of my smart campaign}
The both give me no results.
Is it true that BigQuery data transfer discards the data of smart campaigns? What are other ways to get statistics for them?
BigQuery Data Transfer service for Google Ads is based on an old API, AdWords API v201809. This version of the API does not support Google Ads features introduced in 2019 or later, and Smart Campaigns is among that.
To get around this limitation, you could consider a different Data Transfer - for instance, SuperMetrics and FiveTran have Data Transfers for Google Ads too. Note they are paid, and you should make sure that they do support Smart Campaigns.
Google is said to be working on a Data Transfer using the newer Google Ads API.
I have Google Analytics integrated to Bigquery and I'm trying to write a query to fetch Active Users that should match with the number on GA Portal.
Here's the query I've written;
SELECT
date(date) as date,
EXACT_COUNT_DISTINCT(fullVisitorId) as daily_active_users,
FROM TABLE_DATE_RANGE([<project_id>:<dataset>.ga_sessions_],
TIMESTAMP('2018-01-01'),
TIMESTAMP(CURRENT_DATE()))
group by date
order by date desc
The numbers I get in response are somehow related to the ones Google Analytics shows me, but they aren't a 100% accurate.
The numebers I get in return are slightely higher than the ones on the portal and I assume I need to put a where clause to filter a property GA might be filtering on the portal.
Your query looks fine to me. Assuming that you're looking at the same GA view as the one linked to BigQuery, I think that the problem could be sampling.
Even if the GA UI says that "This report is based on 100% of sessions.", try to export it as an Unsampled Report and check the numbers (in my experience, the users metric sometimes doesn't match between unsampled reports and default reports without sampling).
I started to test Google AdWords transfers for Big Query (https://cloud.google.com/bigquery/docs/adwords-transfer).
I have few questions for which I cannot find answers anywhere.
Is it possible to e.g. edit which columns are downloaded from AdWords to Big Query? E.g. Keyword report has only ad group ID column but not ad group text name.
Or is it possible to decide which tables=reports are downloaded? The transfer creates around 60 tables and I need just 5...
DZ
According to here, AdWords data transfer
store your AdWords data into a Dataset. So, the inputs are in terms of Adwords customer IDs (minimum one customer ID) and the output is a collection of Datasets.
I think, you need a modified version of PubSub to store special columns or tables in BigQuery.
I am using Alteryx to extract weather data for a handful of cities and it works great. I'd like to expand this to able to download data for all weather stations in the UK. At the moment I am specifying which cities I want, e.g. London / Manchester.
Is there a way of specifying in the api call to download all stations in 'GB' or 'UK'?
Ideally I'd like to do this in one call rather than listing all locations which will be very laborious
Get a list of stations or cities that you want to retrieve weather data from. I found some good sources from openweather here: http://bulk.openweathermap.org/sample/
Then build a url request using the list of id's above that retrieves specific weather information. Using an id for the weather station in Cairns, id=2172797, the url ends up looking like:
http://api.openweathermap.org/data/2.5/weather?id=2172797&appid=843798874aac0ef138e6f77c72f3af80
Note that this url will return an error because this isn't a real appid. If you replace the appid with your own, this url will give you data for that station.
Putting this process into Alteryx lets you put the list of station id's together with the url and the appid to make many calls into openweather and then process all of the data together. I could not find information from the API on rate limits, so be conscious of how many requests you are posting to the service.
There is an example of this process here: https://www.dropbox.com/s/yoppbx3bw0p4rug/Get%20individual%20stations.yxzp?dl=0
Keep in mind that you have to update the Appid in the text input tool within this sample as well.