The highlights of the updates on BigQuery, Looker Studio, Google Analytics (GA) & Google Tag Manager (GTM).
In this blog post, I want to summarize the new releases from the Google tools that we use daily in datadice. Therefore, I want to give an overview of the new features of BigQuery, Dataform, Looker Studio, Google Analytics, and Google Tag Manager. Furthermore, I will focus on the releases that I consider to be the most important ones, and I will also name some other changes that were made.
If you want to take a closer look, here you can find the Release Notes from BigQuery, Dataform, Looker Studio, Google Analytics & Google Tag Manager.
BigQuery
Autonomous embeddings in Tables
These embeddings are located in an additional column. This column is based on another source column. If the source column changes, based on a model from Vertex AI, the embedded column changes accordingly.
You can create such a table with an embedding by doing the following:
CREATE TABLE embeddings_test.movies (
title STRING,
description STRING,
genre STRING,
description_embedding STRUCT<result ARRAY<FLOAT64>, status STRING>
GENERATED ALWAYS AS (AI.EMBED(
description,
connection_id => ''eu.vertex_ai_eu_connection'',
endpoint => ''desc-embedding-005''
))
STORED OPTIONS( asynchronous = TRUE )
);
Then fill the table with some data. Just the 3 columns title, description and genre need to be filled, the description_embedding gets created based on these.
Additionally, a vector index can be used to speed up the system, but we will skip this part. Then you can ask the AI questions about your data, and you get a distance for every table entry. The smaller the distance, the better the match.
SELECT base.title, base.description, distance
FROM AI.SEARCH(TABLE embeddings_test.movies, ''description'', "The movie contains horror parts")
ORDER BY 3
Further information can be found here.
New Transfers
Several new Data Transfers are now available to ingest data into your BigQuery project, including:
- PostgreSQL
- Microsoft SQL Server
- MySQL
- Oracle
- Klaviyo
- HubSpot
The configuration for the relational databases (PostgreSQL, Microsoft SQL Server, MySQL, and Oracle) works similarly. You will need to provide the host, port number, database name, username, and password. There are also multiple TLS options available for securing the connection.
Once the connection is established, you can browse the database to select the required tables or add them manually.
Once the connection is established, you can browse the database to select the required tables or add them manually.
For the SaaS Connections:
- HubSpot: Requires an Access Token.
- Klaviyo: Requires a Private API Key.
For both services, you can select specific objects to transfer.
Additionally, it is now possible to ingest data from blob storage (Amazon S3, Azure Blob Storage, Google Cloud Storage) into Iceberg tables in BigQuery.
Dataform
Private Workspaces
There is a new kind of Dataform workspaces available: Private Workspaces. If this option is active, only the creator of the Workspace sees the Workspace. Just this account has access to the code changes, compilation errors, and logs for the compilations and invocations from the workspace.
So far, there is no button to activate this option, probably because the feature is in preview.
Instructions on how to set it up already, can be found here.
actAs Permission
Strict act-as mode enforcement is approaching. Please be prepared: The Service Account executing the scripts requires the iam.serviceAccounts.actAs permission. Google will enforce this requirement in the coming weeks.
You can monitor Cloud Logging, which now provides improved details if errors occur due to the Strict act-as mode. Instructions on how to verify your Service Account setup can be found here.
Looker Studio
Changes for Scheduled Reports
For a long time, you have been able to send your dashboard pages to certain accounts, for example via email.
So far, the shortest period of sending the dashboard data was daily. Now it is even possible to send the dashboard hourly. This feature is just available for Pro users.
If you update your data on an hourly basis, this new feature will be very useful - for example, if you want to track performance during an active sale.
A second update can be found in the schedule under "Advanced options." The new option, "Expand tables to show all rows (PDF)," ensures that all table rows are displayed, up to a limit of 2,000 rows. Please use this option with caution: depending on your active filters, it may result in a PDF with a very large number of pages.
Google Analytics
Analytics Advisor
This new Analytics Advisor is at the end … Gemini for your Google Analytics property.
So you can ask any question there regarding your Google Analytics data, and you get the corresponding answer. It can create tables and charts as well, and can link to further reports if it fits.
This kind of question-answer game was already available in some parts before in Google Analytics, but now it is always available and unified in the Analytics Advisor.
You can access this advisor on any page in the top right corner.
New audience templates
There are two new audience templates available:
- High-Value Purchasers: Includes the top X percentile of LTV users
- Disengaged Purchasers: Contains users who have not purchased in the last X days
The new audiences can be used in Google Analytics and be exported to Google Ads as well, to use especially in customer lifecycle goals.
Google Tag Manager
New built-in variables
There are 3 new built-in variables available in GTM:
- Analytics Client ID
- Analytics Session ID
- Analytics Session Number
They contain the current format of these 3 values, which Google Analytics uses to identify the current user and session. If multiple Measurement IDs are active on the website, all different values per ID are saved.
Rebuilding this was always highly unstable and connected with a lot of effort.
One Use Case would be: If you track data using the Measurement Protocol, we were never able to fully replicate the GA4 session logic. These new variables might solve the issue, but we have not tested them yet.
Furthermore, you can push this data to another system to compare the data with GA4.
And there is one new Analytics Storage variable type available. Here you can define which value you want and from which Measurement ID to get the single value.
Further Links
This post is part of the Google Data Analytics series from datadice and explains to you every month the newest features in BigQuery, Data Studio, Google Analytics, and Google Tag Manager.
Check out our LinkedIn account to get insights into our daily working life and get important updates about BigQuery, Looker Studio, and marketing analytics.
We also started with our own YouTube channel. We talk about important DWH, BigQuery, Looker Studio, and many more topics. Check out the channel here.
If you want to learn more about how to use Google Data Studio and take it to the next level in combination with BigQuery, check our Udemy course here.
If you are looking for help to set up a modern and cost-efficient data warehouse or analytical dashboards, send us an email to hello@datadice.io, and we will schedule a call.
