I want to know if it is possible to link multiple different google analytics properties in a single Bigquery project by separating the property datasets by properties.
I checked the acceptance message that the linking is going well, but I have no idea that the data will be saved in a dataset or different datasets.
Yes. It is possible to link multiple GA properties to a single GCP Project but in different BigQuery datasets. In case of Universal Analytics, the ID of each BQ dataset will be same as that of GA View ID. And in case of GA4 the ID of each BQ dataset will be analytics_<property_id>.
Related
How do I connect various different client's google analytics (GA4 & UA) to one instance of Big Query? I want to store the analytics reports on bigquery & then visualise it on a unified dashboard on Looker
You can set up the exports from Google Analytics to go to the same BigQuery project and transfer historical data to the same project as well.
Even if data is spread across multiple GCP projects, you can still query all from a single project. I would suggest you create a query that connects data from multiple sources together, you can then save it as a view and add it as a source in Looker, you can use it as a custom query in Looker or for best efficiency save the results of your query as a new reporting table that feeds into Looker.
I'm making a BigQuery dataset available to specific external users (meaning, they run their queries in the context of a private project) through dataset sharing. Is there any way to collect basic metrics regarding the use of the dataset, such as - which tables are being accessed?
This is doable through Google BigQuery audit logging. This tutorial provides step-by-step instructions for how to save the logs in a BQ dataset and query it for analysis.
I need to understand the below:
1.) How does one BigQuery connect to another BigQuery and apply some logic and create another BigQuery. For e.g if i have a ETL tool like Data Stage and we have some data been uploaded for us to consume in form of a BigQuery. So in DataStage or using any other technology how do i design the job so that the source is one BQ and the Target is another BQ.
2.) I want to achieve like my input will be a VIEW (BigQuery) and then need to run some logic on the BigQuery View and then load into another BigQuery view.
3.) What is the technology used to connected one BigQuery to another BigQuery is it https or any other technology.
Thanks
If you have a large amount of data to process (many GB), you should do the transformation of the data directly in the Big Query database. It would be very slow to extract all the data, run it through something locally, and send it back. You don't need any outside technology to make one view depend on another view, besides access to the relevant data.
The ideal job design will be an SQL query that Big Query can process. If you are trying to link tables/views across different projects then the source BQ table must be listed in fully-specified form projectName.datasetName.tableName in the FROM clauses of the SQL query. Project names are globally unique in Google Cloud.
Permissions to access the data must be set up correctly. BQ provides fine-grained control over who can access, and it is in the BQ documentation. You can also enable public access to all BQ users if that is appropriate.
Once you have that SQL query, you can create a new view by sending your SQL to Google BigQuery either through the command line (the bq tool), the web console, or an API.
1) You can use BigQuery Connector in DataStage to read and write to bigquery.
2) Bigquery use namespaces in the format project.dataset.table to access tables across projects. This allows you to manipulate your data in GCP as it were in the same database.
To manipulate your data you can use DML or standard SQL.
To execute your queries you can use the GCP Web console or client libraries such as python or java.
3) BigQuery is a RESTful web service and use HTTPS
I would like to improve the quality of existing data by using the Google BigQuery API to help validate the accuracy of existing data.
I dont see information on the types of data elements contained in the BigQuery and dont understand how to use an API if I just want to see what types of data are contained in there.
I tried looking for instructions and data elements in the Google Health Care API and Google BigQuery documentation and only saw how to set up a payment option.
I am a newbie at programming and wanted to do some preliminary research on these data sets prior to bringing them to our technical team.
I expect to see a list of relevant results based on a custom query.
You can see the data types supported
by Google BigQuery here and the conversion between different types here.
Also you can try out the BigQuery APIs in the OAuthPlayGround.
We are using the setup of BigQuery, Stitch and Branch.io. In Branch.io we generate deep links which are shared from our app. The data from Branch.io we the ETL with Stitch to our data warehouse at BigQuery.
We want to have several tables from Branch.io, one with data on clicks on deep links, and one with data on installs from deep links.
This works fine: We set up two webhooks in Branch.io and manage to ETL that data to BigQuery.
HOWEVER, we do not want this data to be in separate datasets. We only manage to get two separate datasets with one table each called "data".
We want one dataset, let's say "deep_links" with two tables, "installs_" and "clicks_". We would also like each of those two tables to be replicated with all data for each day. So that the data for today (2017-11-20) will end up in the tables "installs_20171120" and "clicks20171120".
Is this possible?