googleapis · quirogas · Jul 6, 2026 · Jul 6, 2026 · Jul 6, 2026 · Jul 6, 2026
@@ -0,0 +1 @@
+../../../knowledge/agent_workflow.md
@@ -16,8 +16,8 @@ version: v0.22.0
 repo: googleapis/google-cloud-python
 sources:
   googleapis:
-    commit: e57bae6efbd075a925978a79bb9b997beb4ecc19
-    sha256: 762523e55a4cd9f57c7e5a952dd76ca6041c0e1dd405c14b1d6cfb165e4730b1
+    commit: 73a8001701e1d3a668dbfb72e9ab66c97d107ff6
+    sha256: 16b606051acfea9e3d2871a844e1106459294c540c5089fdcf7d306092e27813
 default:
   output: packages
   tag_format: '{name}-v{version}'
@@ -718,6 +718,7 @@ libraries:
     apis:
       - path: google/cloud/ces/v1
       - path: google/cloud/ces/v1beta
+    skip_generate: true
     python:
       default_version: v1
   - name: google-cloud-channel

@@ -1,5 +1,5 @@
 {
-    "api_description": "The BigLake API provides access to BigLake Metastore, a serverless, fully\nmanaged, and highly available metastore for open-source data that can be\nused for querying Apache Iceberg tables in BigQuery.",
+    "api_description": "The Lakehouse API (formerly BigLake API) provides access to a serverless,\nfully managed, and highly available metastore that provides a single\nsource of truth for your data lakehouse. It lets multiple\nengines—including Apache Spark, Google Managed Spark, Apache Flink, Trino\nand BigQuery—share tables and metadata for key open formats (Apache\nIceberg, Apache Hive), and query the same copy of data. Plus, through the\nLakehouse runtime catalog federation seamlessly unite your lakehouse\necosystem, letting Iceberg compatible engines on Google Cloud (BigQuery,\nGoogle Managed Spark) discover and analyze enterprise data across\nSnowflake, Databricks, and AWS Glue.",
     "api_id": "biglake.googleapis.com",
     "api_shortname": "biglake",
     "client_documentation": "https://cloud.google.com/python/docs/reference/google-cloud-biglake-hive/latest",
@@ -9,7 +9,7 @@
     "language": "python",
     "library_type": "GAPIC_AUTO",
     "name": "google-cloud-biglake-hive",
-    "name_pretty": "BigLake",
+    "name_pretty": "Lakehouse",
     "product_documentation": "https://cloud.google.com/bigquery/",
     "release_level": "preview",
     "repo": "googleapis/google-cloud-python"

@@ -1,11 +1,18 @@
-Python Client for BigLake
-=========================
+Python Client for Lakehouse
+===========================
 
 |preview| |pypi| |versions|
 
-`BigLake`_: The BigLake API provides access to BigLake Metastore, a serverless, fully
-managed, and highly available metastore for open-source data that can be
-used for querying Apache Iceberg tables in BigQuery.
+`Lakehouse`_: The Lakehouse API (formerly BigLake API) provides access to a serverless,
+fully managed, and highly available metastore that provides a single
+source of truth for your data lakehouse. It lets multiple
+engines—including Apache Spark, Google Managed Spark, Apache Flink, Trino
+and BigQuery—share tables and metadata for key open formats (Apache
+Iceberg, Apache Hive), and query the same copy of data. Plus, through the
+Lakehouse runtime catalog federation seamlessly unite your lakehouse
+ecosystem, letting Iceberg compatible engines on Google Cloud (BigQuery,
+Google Managed Spark) discover and analyze enterprise data across
+Snowflake, Databricks, and AWS Glue.
 
 - `Client Library Documentation`_
 - `Product Documentation`_
@@ -16,7 +23,7 @@ used for querying Apache Iceberg tables in BigQuery.
    :target: https://pypi.org/project/google-cloud-biglake-hive/
 .. |versions| image:: https://img.shields.io/pypi/pyversions/google-cloud-biglake-hive.svg
    :target: https://pypi.org/project/google-cloud-biglake-hive/
-.. _BigLake: https://cloud.google.com/bigquery/
+.. _Lakehouse: https://cloud.google.com/bigquery/
 .. _Client Library Documentation: https://cloud.google.com/python/docs/reference/google-cloud-biglake-hive/latest/summary_overview
 .. _Product Documentation:  https://cloud.google.com/bigquery/
 
@@ -27,12 +34,12 @@ In order to use this library, you first need to go through the following steps:
 
 1. `Select or create a Cloud Platform project.`_
 2. `Enable billing for your project.`_
-3. `Enable the BigLake.`_
+3. `Enable the Lakehouse.`_
 4. `Set up Authentication.`_
 
 .. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project
 .. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project
-.. _Enable the BigLake.:  https://cloud.google.com/bigquery/
+.. _Enable the Lakehouse.:  https://cloud.google.com/bigquery/
 .. _Set up Authentication.: https://googleapis.dev/python/google-api-core/latest/auth.html
 
 Installation
@@ -100,14 +107,14 @@ Windows
 Next Steps
 ~~~~~~~~~~
 
--  Read the `Client Library Documentation`_ for BigLake
+-  Read the `Client Library Documentation`_ for Lakehouse
    to see other available methods on the client.
--  Read the `BigLake Product documentation`_ to learn
+-  Read the `Lakehouse Product documentation`_ to learn
    more about the product and see How-to Guides.
 -  View this `README`_ to see the full list of Cloud
    APIs that we cover.
 
-.. _BigLake Product documentation:  https://cloud.google.com/bigquery/
+.. _Lakehouse Product documentation:  https://cloud.google.com/bigquery/
 .. _README: https://github.com/googleapis/google-cloud-python/blob/main/README.rst
 
 Logging

@@ -1,11 +1,18 @@
-Python Client for BigLake
-=========================
+Python Client for Lakehouse
+===========================
 
 |preview| |pypi| |versions|
 
-`BigLake`_: The BigLake API provides access to BigLake Metastore, a serverless, fully
-managed, and highly available metastore for open-source data that can be
-used for querying Apache Iceberg tables in BigQuery.
+`Lakehouse`_: The Lakehouse API (formerly BigLake API) provides access to a serverless,
+fully managed, and highly available metastore that provides a single
+source of truth for your data lakehouse. It lets multiple
+engines—including Apache Spark, Google Managed Spark, Apache Flink, Trino
+and BigQuery—share tables and metadata for key open formats (Apache
+Iceberg, Apache Hive), and query the same copy of data. Plus, through the
+Lakehouse runtime catalog federation seamlessly unite your lakehouse
+ecosystem, letting Iceberg compatible engines on Google Cloud (BigQuery,
+Google Managed Spark) discover and analyze enterprise data across
+Snowflake, Databricks, and AWS Glue.
 
 - `Client Library Documentation`_
 - `Product Documentation`_
@@ -16,7 +23,7 @@ used for querying Apache Iceberg tables in BigQuery.
    :target: https://pypi.org/project/google-cloud-biglake-hive/
 .. |versions| image:: https://img.shields.io/pypi/pyversions/google-cloud-biglake-hive.svg
    :target: https://pypi.org/project/google-cloud-biglake-hive/
-.. _BigLake: https://cloud.google.com/bigquery/
+.. _Lakehouse: https://cloud.google.com/bigquery/
 .. _Client Library Documentation: https://cloud.google.com/python/docs/reference/google-cloud-biglake-hive/latest/summary_overview
 .. _Product Documentation:  https://cloud.google.com/bigquery/
 
@@ -27,12 +34,12 @@ In order to use this library, you first need to go through the following steps:
 
 1. `Select or create a Cloud Platform project.`_
 2. `Enable billing for your project.`_
-3. `Enable the BigLake.`_
+3. `Enable the Lakehouse.`_
 4. `Set up Authentication.`_
 
 .. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project
 .. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project
-.. _Enable the BigLake.:  https://cloud.google.com/bigquery/
+.. _Enable the Lakehouse.:  https://cloud.google.com/bigquery/
 .. _Set up Authentication.: https://googleapis.dev/python/google-api-core/latest/auth.html
 
 Installation
@@ -100,14 +107,14 @@ Windows
 Next Steps
 ~~~~~~~~~~
 
--  Read the `Client Library Documentation`_ for BigLake
+-  Read the `Client Library Documentation`_ for Lakehouse
    to see other available methods on the client.
--  Read the `BigLake Product documentation`_ to learn
+-  Read the `Lakehouse Product documentation`_ to learn
    more about the product and see How-to Guides.
 -  View this `README`_ to see the full list of Cloud
    APIs that we cover.
 
-.. _BigLake Product documentation:  https://cloud.google.com/bigquery/
+.. _Lakehouse Product documentation:  https://cloud.google.com/bigquery/
 .. _README: https://github.com/googleapis/google-cloud-python/blob/main/README.rst
 
 Logging

@@ -5,14 +5,14 @@ reverted. Instead, if you want to place additional content, create an
 pick up on the content and merge the content.
 ]: #
 
-# BigLake API
+# Lakehouse API
 
-Overview of the APIs available for BigLake API.
+Overview of the APIs available for Lakehouse API.
 
 ## All entries
 
 Classes, methods and properties & attributes for
-BigLake API.
+Lakehouse API.
 
 [classes](https://cloud.google.com/python/docs/reference/google-cloud-biglake-hive/latest/summary_class.html)
 

@@ -73,7 +73,7 @@ class HiveCatalog(proto.Message):
 
     Attributes:
         name (str):
-            Output only. The resource name. Format:
+            Identifier. The resource name. Format:
             projects/{project_id_or_number}/catalogs/{catalog_id}
         description (str):
             Optional. Stores the catalog description.
@@ -86,6 +86,11 @@ class HiveCatalog(proto.Message):
         replicas (MutableSequence[google.cloud.biglake_hive_v1beta.types.HiveCatalog.Replica]):
             Output only. The replicas for the catalog
             metadata.
+        create_time (google.protobuf.timestamp_pb2.Timestamp):
+            Output only. The creation time of the
+            catalog.
+        update_time (google.protobuf.timestamp_pb2.Timestamp):
+            Output only. The update time of the catalog.
     """
 
     class Replica(proto.Message):
@@ -151,6 +156,16 @@ class State(proto.Enum):
         number=4,
         message=Replica,
     )
+    create_time: timestamp_pb2.Timestamp = proto.Field(
+        proto.MESSAGE,
+        number=5,
+        message=timestamp_pb2.Timestamp,
+    )
+    update_time: timestamp_pb2.Timestamp = proto.Field(
+        proto.MESSAGE,
+        number=6,
+        message=timestamp_pb2.Timestamp,
+    )
 
 
 class CreateHiveCatalogRequest(proto.Message):
@@ -322,7 +337,7 @@ class HiveDatabase(proto.Message):
 
     Attributes:
         name (str):
-            Output only. The resource name. Format:
+            Identifier. The resource name. Format:
             projects/{project_id_or_number}/catalogs/{catalog_id}/databases/{database_id}
         description (str):
             Optional. Stores the database description.
@@ -335,6 +350,11 @@ class HiveDatabase(proto.Message):
         parameters (MutableMapping[str, str]):
             Optional. Stores the properties associated
             with the database. The maximum size is 2 MiB.
+        create_time (google.protobuf.timestamp_pb2.Timestamp):
+            Output only. The creation time of the
+            database.
+        update_time (google.protobuf.timestamp_pb2.Timestamp):
+            Output only. The update time of the database.
     """
 
     name: str = proto.Field(
@@ -354,6 +374,16 @@ class HiveDatabase(proto.Message):
         proto.STRING,
         number=4,
     )
+    create_time: timestamp_pb2.Timestamp = proto.Field(
+        proto.MESSAGE,
+        number=5,
+        message=timestamp_pb2.Timestamp,
+    )
+    update_time: timestamp_pb2.Timestamp = proto.Field(
+        proto.MESSAGE,
+        number=6,
+        message=timestamp_pb2.Timestamp,
+    )
 
 
 class CreateHiveDatabaseRequest(proto.Message):
@@ -506,7 +536,7 @@ class HiveTable(proto.Message):
 
     Attributes:
         name (str):
-            Output only. The resource name. Format:
+            Identifier. The resource name. Format:
             projects/{project_id_or_number}/catalogs/{catalog_id}/databases/{database_id}/tables/{table_id}
         description (str):
             Optional. Description of the table. The
@@ -520,9 +550,17 @@ class HiveTable(proto.Message):
         parameters (MutableMapping[str, str]):
             Optional. Stores the properties associated
             with the table. The maximum size is 4MiB.
+        view_original_text (str):
+            Optional. The original view text. Empty for
+            non-view. The maximum size is 16MiB.
+        view_expanded_text (str):
+            Optional. The expanded view text. Empty for
+            non-view. The maximum size is 16MiB.
         table_type (str):
             Output only. The type of the table. This is
             EXTERNAL for BigLake hive tables.
+        update_time (google.protobuf.timestamp_pb2.Timestamp):
+            Output only. The update time of the table.
     """
 
     name: str = proto.Field(
@@ -553,10 +591,23 @@ class HiveTable(proto.Message):
         proto.STRING,
         number=8,
     )
+    view_original_text: str = proto.Field(
+        proto.STRING,
+        number=9,
+    )
+    view_expanded_text: str = proto.Field(
+        proto.STRING,
+        number=10,
+    )
     table_type: str = proto.Field(
         proto.STRING,
         number=11,
     )
+    update_time: timestamp_pb2.Timestamp = proto.Field(
+        proto.MESSAGE,
+        number=12,
+        message=timestamp_pb2.Timestamp,
+    )
 
 
 class FieldSchema(proto.Message):