"title"=>"April 04, 2024",
"summary"=>nil,
"content"=>"Anthos clusters on AWS\n
Announcement
\nYou can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:
\n\n\nAnthos clusters on Azure\nAnnouncement
\nYou can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:
\n\n\nBigQuery\nFeature
\nThe allow_non_incremental_definition option and max_staleness option for materialized views are now generally available (GA). The allow_non_incremental_definition option supports an expanded range of SQL queries to create materialized views, and the max_staleness option provides consistently high performance with controlled costs when processing large, frequently changing datasets.
Feature
\nYou can now perform\nmodel monitoring in BigQuery ML. The following model monitoring functions are now in\npreview:
\n\n- \n
ML.DESCRIBE_DATA:\ncompute descriptive statistics for a set of training or serving data. \nML.VALIDATE_DATA_SKEW:\ncompute the statistics for a set of serving data, and then compare them to\nthe statistics for the data used to train a BigQuery ML model in order to\nidentify anomalous differences between the two data sets. \nML.VALIDATE_DATA_DRIFT:\ncompute and compare the statistics for two sets of serving data in order to\nidentify anomalous differences between the two data sets. \nML.TFDV_DESCRIBE:\ncompute fine-grained descriptive statistics for a set of training or\nserving data. This function provides the same behavior as the\nTensorFlowtfdv.generate_statistics_from_csvAPI. \nML.TFDV_VALIDATE:\ncompute and compare the statistics for training and serving data, or two\nsets of serving data, in order to identify anomalous differences between\nthe two data sets. This function provides the same behavior as the\nTensorFlowvalidate_statisticsAPI. \n
Feature
\nBigQuery data clean rooms with analysis rules and enhanced usage metrics are now generally available (GA). Data clean rooms provide a security-enhanced and privacy-preserving environment for multiple parties to share and augment data without moving or revealing the underlying data.
\nFeature
\nJoin restrictions, list overlap, differential privacy with privacy budgeting, and aggregation thresholding are now generally available (GA) and enforceable in BigQuery data clean rooms using analysis rules.
\nCloud Data Fusion\nChanged
\nCloud Data Fusion is available in the africa-south1 region. For more information, see Pricing.
Feature
\nGenerally available: Simplify block storage management for Compute Engine instances with Hyperdisk Storage Pools. A Hyperdisk Storage Pool is a pre-purchased collection of disk capacity, throughput, and IOPS which you can then provision to your applications as needed. By managing disks in aggregate, you can save costs while achieving expected capacity and performance growth. For more information, see About Hyperdisk Storage Pools.
\nDialogflow\nFeature
\nVertex AI Conversation: You can now create a data store in one language that is connected to an agent that uses different languages.
\nGoogle Kubernetes Engine\nSecurity
\nA Denial-of-Service (DoS) vulnerability (CVE-2023-45288) was recently discovered in multiple implementations of the HTTP/2 protocol, including the golang HTTP server used by Kubernetes. The vulnerability could lead to a DoS of the Google Kubernetes Engine (GKE) control plane.
\n\nFor more information, see the GCP-2024-022 security bulletin.
\n\n ","author"=>nil,
"link"=>"https://cloud.google.com/release-notes#April_04_2024",
"published_date"=>Thu, 04 Apr 2024 07:00:00.000000000 UTC +00:00,
"image_url"=>nil,
"feed_url"=>"https://cloud.google.com/release-notes#April_04_2024",
"language"=>nil,
"active"=>true,
"ricc_source"=>"feedjira::v1",
"created_at"=>Fri, 05 Apr 2024 08:04:28.578334000 UTC +00:00,
"updated_at"=>Tue, 14 May 2024 05:25:13.611174000 UTC +00:00,
"newspaper"=>"GCP latest releases",
"macro_region"=>"Technology"}