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<strong class="release-note-product-title">Anthos clusters on AWS</strong>
<h3>Announcement</h3>
<p>You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:</p>

<ul>
<li><a href="https://cloud.google.com/anthos/clusters/docs/multi-cloud/aws/reference/supported-versions#1287-gke1700">1.28.7-gke.1700</a></li>
<li><a href="https://cloud.google.com/anthos/clusters/docs/multi-cloud/aws/reference/supported-versions#12711-gke1600">1.27.11-gke.1600</a></li>
<li><a href="https://cloud.google.com/anthos/clusters/docs/multi-cloud/aws/reference/supported-versions#12614-gke1500">1.26.14-gke.1500</a></li>
</ul>
<strong class="release-note-product-title">Anthos clusters on Azure</strong>
<h3>Announcement</h3>
<p>You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:</p>

<ul>
<li><a href="https://cloud.google.com/anthos/clusters/docs/multi-cloud/azure/reference/supported-versions#1287-gke1700">1.28.7-gke.1700</a></li>
<li><a href="https://cloud.google.com/anthos/clusters/docs/multi-cloud/azure/reference/supported-versions#12711-gke1600">1.27.11-gke.1600</a></li>
<li><a href="https://cloud.google.com/anthos/clusters/docs/multi-cloud/azure/reference/supported-versions#12614-gke1500">1.26.14-gke.1500</a></li>
</ul>
<strong class="release-note-product-title">BigQuery</strong>
<h3>Feature</h3>
<p>The <a href="https://cloud.google.com/bigquery/docs/materialized-views-create#non-incremental"><code>allow_non_incremental_definition</code> option</a> and <a href="https://cloud.google.com/bigquery/docs/materialized-views-create#max_staleness"><code>max_staleness</code> option</a> for materialized views are now <a href="https://cloud.google.com/products/#product-launch-stages">generally available (GA)</a>.  The <code>allow_non_incremental_definition</code> option supports an expanded range of SQL queries to create materialized views, and the <code>max_staleness</code> option provides consistently high performance with controlled costs when processing large, frequently changing datasets.</p>
<h3>Feature</h3>
<p>You can now perform
<a href="https://cloud.google.com/bigquery/docs/model-monitoring-overview">model monitoring</a> in BigQuery ML. The following model monitoring functions are now in
<a href="https://cloud.google.com/products/#product-launch-stages">preview</a>:</p>

<ul>
<li><a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-describe-data"><code>ML.DESCRIBE_DATA</code></a>:
compute descriptive statistics for a set of training or serving data.</li>
<li><a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-validate-data-skew"><code>ML.VALIDATE_DATA_SKEW</code></a>:
compute the statistics for a set of serving data, and then compare them to
the statistics for the data used to train a BigQuery ML model in order to
identify anomalous differences between the two data sets.</li>
<li><a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-validate-data-drift"><code>ML.VALIDATE_DATA_DRIFT</code></a>:
compute and compare the statistics for two sets of serving data in order to
identify anomalous differences between the two data sets.</li>
<li><a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-tfdv-describe"><code>ML.TFDV_DESCRIBE</code></a>:
compute fine-grained descriptive statistics for a set of training or
serving data. This function provides the same behavior as the
<a href="https://www.tensorflow.org/tfx/data_validation/api_docs/python/tfdv/generate_statistics_from_csv">TensorFlow <code>tfdv.generate_statistics_from_csv</code> API</a>.</li>
<li><a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-tfdv-validate"><code>ML.TFDV_VALIDATE</code></a>:
compute and compare the statistics for training and serving data, or two
sets of serving data, in order to identify anomalous differences between
the two data sets. This function provides the same behavior as the
<a href="https://www.tensorflow.org/tfx/data_validation/api_docs/python/tfdv/validate_statistics">TensorFlow <code>validate_statistics</code> API</a>.</li>
</ul>
<h3>Feature</h3>
<p><a href="https://cloud.google.com/bigquery/docs/data-clean-rooms">BigQuery data clean rooms</a> with analysis rules and enhanced usage metrics are now <a href="https://cloud.google.com/products/#product-launch-stages">generally available (GA)</a>. 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. </p>
<h3>Feature</h3>
<p><a href="https://cloud.google.com/bigquery/docs/analysis-rules#join_restriction_rules">Join restrictions</a>, <a href="https://cloud.google.com/bigquery/docs/analysis-rules#list_overlap_rules">list overlap</a>, <a href="https://cloud.google.com/bigquery/docs/analysis-rules#dp_analysis_rules">differential privacy with privacy budgeting</a>, and <a href="https://cloud.google.com/bigquery/docs/analysis-rules#agg_analysis_rules">aggregation thresholding</a> are now <a href="https://cloud.google.com/products/#product-launch-stages">generally available (GA)</a> and enforceable in BigQuery data clean rooms using analysis rules.</p>
<strong class="release-note-product-title">Cloud Data Fusion</strong>
<h3>Changed</h3>
<p>Cloud Data Fusion is available in the <code>africa-south1</code> region. For more information, see <a href="https://cloud.google.com/data-fusion/pricing">Pricing</a>.</p>
<strong class="release-note-product-title">Compute Engine</strong>
<h3>Feature</h3>
<p><strong>Generally available</strong>: 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 <a href="https://cloud.google.com/compute/docs/disks/storage-pools">About Hyperdisk Storage Pools</a>.</p>
<strong class="release-note-product-title">Dialogflow</strong>
<h3>Feature</h3>
<p>Vertex AI Conversation: You can now create a <a href="https://cloud.google.com/dialogflow/vertex/docs/concept/data-store#languages">data store in one language</a> that is connected to an agent that uses different languages.</p>
<strong class="release-note-product-title">Google Kubernetes Engine</strong>
<h3>Security</h3>
<p>A 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.</p>

<p>For more information, see the <a href="https://cloud.google.com/anthos/clusters/docs/security-bulletins#gcp-2024-022">GCP-2024-022 security bulletin</a>.</p>

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--- !ruby/object:Feedjira::Parser::AtomEntry
title_type:
title: April 04, 2024
entry_id: tag:google.com,2016:gcp-release-notes#April_04_2024
updated: &1 2024-04-04 07:00:00.000000000 Z
links:
- https://cloud.google.com/release-notes#April_04_2024
content: "<strong class=\"release-note-product-title\">Anthos clusters on AWS</strong>\n<h3>Announcement</h3>\n<p>You
  can now launch clusters with the following Kubernetes versions. Click on the following
  links to see the release notes associated with these patches:</p>\n\n<ul>\n<li><a
  href=\"https://cloud.google.com/anthos/clusters/docs/multi-cloud/aws/reference/supported-versions#1287-gke1700\">1.28.7-gke.1700</a></li>\n<li><a
  href=\"https://cloud.google.com/anthos/clusters/docs/multi-cloud/aws/reference/supported-versions#12711-gke1600\">1.27.11-gke.1600</a></li>\n<li><a
  href=\"https://cloud.google.com/anthos/clusters/docs/multi-cloud/aws/reference/supported-versions#12614-gke1500\">1.26.14-gke.1500</a></li>\n</ul>\n<strong
  class=\"release-note-product-title\">Anthos clusters on Azure</strong>\n<h3>Announcement</h3>\n<p>You
  can now launch clusters with the following Kubernetes versions. Click on the following
  links to see the release notes associated with these patches:</p>\n\n<ul>\n<li><a
  href=\"https://cloud.google.com/anthos/clusters/docs/multi-cloud/azure/reference/supported-versions#1287-gke1700\">1.28.7-gke.1700</a></li>\n<li><a
  href=\"https://cloud.google.com/anthos/clusters/docs/multi-cloud/azure/reference/supported-versions#12711-gke1600\">1.27.11-gke.1600</a></li>\n<li><a
  href=\"https://cloud.google.com/anthos/clusters/docs/multi-cloud/azure/reference/supported-versions#12614-gke1500\">1.26.14-gke.1500</a></li>\n</ul>\n<strong
  class=\"release-note-product-title\">BigQuery</strong>\n<h3>Feature</h3>\n<p>The
  <a href=\"https://cloud.google.com/bigquery/docs/materialized-views-create#non-incremental\"><code>allow_non_incremental_definition</code>
  option</a> and <a href=\"https://cloud.google.com/bigquery/docs/materialized-views-create#max_staleness\"><code>max_staleness</code>
  option</a> for materialized views are now <a href=\"https://cloud.google.com/products/#product-launch-stages\">generally
  available (GA)</a>.  The <code>allow_non_incremental_definition</code> option supports
  an expanded range of SQL queries to create materialized views, and the <code>max_staleness</code>
  option provides consistently high performance with controlled costs when processing
  large, frequently changing datasets.</p>\n<h3>Feature</h3>\n<p>You can now perform\n<a
  href=\"https://cloud.google.com/bigquery/docs/model-monitoring-overview\">model
  monitoring</a> in BigQuery ML. The following model monitoring functions are now
  in\n<a href=\"https://cloud.google.com/products/#product-launch-stages\">preview</a>:</p>\n\n<ul>\n<li><a
  href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-describe-data\"><code>ML.DESCRIBE_DATA</code></a>:\ncompute
  descriptive statistics for a set of training or serving data.</li>\n<li><a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-validate-data-skew\"><code>ML.VALIDATE_DATA_SKEW</code></a>:\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.</li>\n<li><a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-validate-data-drift\"><code>ML.VALIDATE_DATA_DRIFT</code></a>:\ncompute
  and compare the statistics for two sets of serving data in order to\nidentify anomalous
  differences between the two data sets.</li>\n<li><a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-tfdv-describe\"><code>ML.TFDV_DESCRIBE</code></a>:\ncompute
  fine-grained descriptive statistics for a set of training or\nserving data. This
  function provides the same behavior as the\n<a href=\"https://www.tensorflow.org/tfx/data_validation/api_docs/python/tfdv/generate_statistics_from_csv\">TensorFlow
  <code>tfdv.generate_statistics_from_csv</code> API</a>.</li>\n<li><a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-tfdv-validate\"><code>ML.TFDV_VALIDATE</code></a>:\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\n<a href=\"https://www.tensorflow.org/tfx/data_validation/api_docs/python/tfdv/validate_statistics\">TensorFlow
  <code>validate_statistics</code> API</a>.</li>\n</ul>\n<h3>Feature</h3>\n<p><a href=\"https://cloud.google.com/bigquery/docs/data-clean-rooms\">BigQuery
  data clean rooms</a> with analysis rules and enhanced usage metrics are now <a href=\"https://cloud.google.com/products/#product-launch-stages\">generally
  available (GA)</a>. 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. </p>\n<h3>Feature</h3>\n<p><a href=\"https://cloud.google.com/bigquery/docs/analysis-rules#join_restriction_rules\">Join
  restrictions</a>, <a href=\"https://cloud.google.com/bigquery/docs/analysis-rules#list_overlap_rules\">list
  overlap</a>, <a href=\"https://cloud.google.com/bigquery/docs/analysis-rules#dp_analysis_rules\">differential
  privacy with privacy budgeting</a>, and <a href=\"https://cloud.google.com/bigquery/docs/analysis-rules#agg_analysis_rules\">aggregation
  thresholding</a> are now <a href=\"https://cloud.google.com/products/#product-launch-stages\">generally
  available (GA)</a> and enforceable in BigQuery data clean rooms using analysis rules.</p>\n<strong
  class=\"release-note-product-title\">Cloud Data Fusion</strong>\n<h3>Changed</h3>\n<p>Cloud
  Data Fusion is available in the <code>africa-south1</code> region. For more information,
  see <a href=\"https://cloud.google.com/data-fusion/pricing\">Pricing</a>.</p>\n<strong
  class=\"release-note-product-title\">Compute Engine</strong>\n<h3>Feature</h3>\n<p><strong>Generally
  available</strong>: 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 <a href=\"https://cloud.google.com/compute/docs/disks/storage-pools\">About
  Hyperdisk Storage Pools</a>.</p>\n<strong class=\"release-note-product-title\">Dialogflow</strong>\n<h3>Feature</h3>\n<p>Vertex
  AI Conversation: You can now create a <a href=\"https://cloud.google.com/dialogflow/vertex/docs/concept/data-store#languages\">data
  store in one language</a> that is connected to an agent that uses different languages.</p>\n<strong
  class=\"release-note-product-title\">Google Kubernetes Engine</strong>\n<h3>Security</h3>\n<p>A
  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.</p>\n\n<p>For more information, see the <a href=\"https://cloud.google.com/anthos/clusters/docs/security-bulletins#gcp-2024-022\">GCP-2024-022
  security bulletin</a>.</p>\n\n    "
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url: https://cloud.google.com/release-notes#April_04_2024

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