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Cloud SDK update: google-cloud-bigquery 2.31.2 (2023-09-05) - Bug fixes
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The weekly Cloud SDK client library update includes changes to google-cloud-bigquery version 2.31.2 (2023-09-05). This update fixes bugs including masking the TableReference data structure (#2855) and a bug related to SearchStats IndexUnusedReasons (#2825).
New GA4 API fields in Google Analytics 4
Google Analytics 4 has introduced new data sources that retrieve their fields directly from the GA4 API. Previously, GA4 data sources were based on a fixed schema with a predefined list of fields. To see the new GA4 API fields in an existing data source, simply refresh the fields. This update is backward compatible with the previous version of the connector. Refreshing a Google Analytics 4 data source preserves the existing schema and simply adds new API fields to the data source. Learn how to connect to Google Analytics.
Cloud SDK updates for BigQuery client libraries
This weekly update concerns the Cloud SDK client libraries. For google-cloud-bigquery in Java, version 2.37.0 adds support for table resource tags and the universe domain. For google-cloud-bigquery in Python, version 3.17.1 fixes bugs and version 3.17.0 adds support for universe resolution. In addition, it is now possible to use tags on BigQuery tables to grant or deny conditional access with identity and access management (IAM) policies.
Cloud SDK updates: google-cloud-bigquery v3.14.0rc0
This weekly update concerns updates to client libraries in the Cloud SDK. For the google-cloud-bigquery Python library, version 3.14.0rc0 brings several features, including the addition of properties such as job_id, location, project and query_id on RowIterator, the addition of job_timeout_ms to job configuration classes, support for dataset.max_time_travel_hours and Dataset.isCaseInsensitive, as well as support for data_governance_type. Bug fixes have also been made, notably for load_table_from_dataframe and query delays. Performance improvements have also been made.
Cloud SDK updates: google-cloud-bigquery 3.14.1, 3.14.0, administrative resource graphs previewed
This weekly update concerns updates to client libraries in the Cloud SDK. For the google-cloud-bigquery library in Python, version 3.14.1 brings bug fixes, including the addition of a missing handler for deserializing JSON values. Version 3.14.0 adds new features such as the ability to return a result row iterator directly, additional properties for the row iterator, support for Python 3.12, and more. Performance enhancements and bug fixes have also been made. Finally, administrative resource graphs for operational health are now available for preview.
Cloud SDK updates and BigQuery traffic charges
This weekly update concerns updates to client libraries in the Cloud SDK. For google-cloud-bigquery, version 2.31.1 was released on August 9, 2023. Dependencies have been updated, including com.google.api.grpc:proto-google-cloud-bigqueryconnection-v1 to version 2.25.0, com.google.cloud:google-cloud-datacatalog-bom to version 1.29.0, com.google.cloud:google-cloud-shared-dependencies version 3.14.0, org.graalvm.buildtools:junit-platform-native version 0.9.24, org.graalvm.buildtools:native-maven-plugin version 0.9.24, github/codeql-action version 2.21.1 and jmh.version version 1.37. From September 15, 2023, charges will apply for network outbound traffic from one BigQuery Google Cloud region to another Google Cloud region on the same continent and between different continents. For more information, see the BigQuery network outbound traffic charges announcement.
Cloud SDK and BigQuery updates available in Berlin
This weekly update concerns updates to the Cloud SDK client libraries. Changes include improvements to schema comparison stability, the ability to request compressed rows in read responses, the addition of data governance types for routines, support for range types in schemas, support for non-incremental definition and expiry on materialized views, support for resource tags for tables, and exposure of the query identifier on the row iterator. Documentation has also been updated. In addition, BigQuery is now available in the Berlin region (europe-west10).
BigQuery migration for Apache Hive: pre-release evaluation available
The BigQuery Migration Assessment is now available in pre-release for Apache Hive. You can use this feature to assess the complexity of migrating data from your Apache Hive data warehouse to BigQuery.
Cloud SDK updates: BigQuery, DLP and encryption
This weekly update concerns client library updates in the Cloud SDK. Version 2.33.2 of google-cloud-bigquery brings bug fixes and dependency updates. It is now possible to use DLP functions to support encryption and decryption between BigQuery and DLP, using AES-SIV. This feature is in pre-release.
BigQuery and Analytics Hub (GA) updates
The update includes the quantitative LIKE operator in preview, as well as several JSON functions now available (GA). BigQuery now supports the ability to deny access to principals via deny policies for certain IAM authorizations. In addition, there have been updates to the client library for Node.js. Finally, Analytics Hub now supports the use of routines in linked datasets, in preview.
Server tagging update: Google Analytics sends data to regional centers.
The update concerns server-side tagging: the Google Analytics: GA4 tag in server containers now sends data to regional data centers based on the user's location.
BigQuery ML update: time-based search and IAM access control
The update introduces time search functions for BigQuery ML, which allow you to specify a reference date when retrieving features for model training or inference execution, in order to avoid data leakage. The ML.FEATURES_AT_TIME and ML.ENTITY_FEATURES_AT_TIME functions enable features to be retrieved from multiple points in time for one or more entities. In addition, it is now possible to use IAM conditions to control access to BigQuery resources. This feature is in preview version.
BigQuery ML update: time-based search and IAM access control
The update introduces time search functions for BigQuery ML, which allow you to specify a reference date when retrieving features for model training or inference execution, in order to avoid data leakage. The ML.FEATURES_AT_TIME and ML.ENTITY_FEATURES_AT_TIME functions enable features to be retrieved from multiple points in time for one or more entities. In addition, it is now possible to use IAM conditions to control access to BigQuery resources. This feature is in preview version.
Custom data masking and GA entity resolution in BigQuery
Custom data masking functionality is now available in General Availability (GA). You can define custom masking routines for custom masking capabilities such as salt-based hashing. This feature is available in the Enterprise Plus edition. BigQuery now offers entity resolution. This feature enables users to match records between datasets even in the absence of a common identifier. It uses an identity provider for this process; BigQuery supports LiveRamp and provides a framework for other identity providers to offer similar services. This functionality is available in general availability (GA).
Data masking with BigQuery: creation of custom routines in pre-release.
BigQuery now lets you create your own masking routines for your data. You can use the REGEX_REPLACE scalar function to create custom masking rules to obscure your sensitive data. This feature is currently in pre-release.
Linking Google Analytics with Search Ads 360 and Display & Video 360
Google Analytics has added the ability to link your sub-properties and grouping properties with Search Ads 360 and Display & Video 360. Linking works in exactly the same way as for regular properties. Once linked, Google Analytics will send the same data in the same way as for regular properties. With this feature, customers can now share a subset of data from a sub-property, or a superset of cross-property data to Search Ads 360 or Display & Video 360 to target subsets or supersets of their audience. Learn how to link Google Analytics with Search Ads 360 and Display & Video 360.
Import web and app conversions from Google Analytics 4 into Google Ads
Google Ads now offers the ability to import web and app conversions from Google Analytics 4 into Google Ads. This makes it possible to access Google Analytics data in Google Ads and optimize bids to improve performance. This recommendation can appear at the top of the "Ad overview" page, on the home page or on the "Information center" page. To find out more about these recommendations, please consult the documentation.
BigQuery-Looker Studio integration: enhanced performance and new features
BigQuery's native integration with Looker Studio enables new monitoring features for Looker Studio queries, improves query performance and supports many BigQuery features. This feature is in preview. You can subscribe to Looker Studio Pro directly using our self-service upgrade tool. Looker Studio Pro gives you all the Looker Studio features you already know, plus enhanced enterprise capabilities and access to Google Cloud Customer Care support. Find out more about Looker Studio Pro.
Administrative query inspector for BigQuery: monitoring job slots and performance.
As a BigQuery administrator, you can now use the Administrative Query Inspector to monitor your organization's slot utilization and BigQuery job performance over time. This feature is now available to all users.
Google Analytics improves results accuracy and reduces data sampling
With this update, Google Analytics now chooses the table that provides the most accurate results for each query, reducing the likelihood of seeing the "(other)" line and data sampling in reports and crawls. As a result, it is now possible to see the "(other)" line in drilldowns or data sampling in reports, as Analytics chooses the table based on your query to provide the most accurate results and reduce the impact of data sampling and cardinality limits. Find out more about how data is stored and displayed.
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