Add lakehouse connector

.github/workflows/ci.yml

@@ -350,6 +350,7 @@ jobs:
             !:trino-ignite,
             !:trino-jdbc,
             !:trino-kafka,
+            !:trino-lakehouse,
             !:trino-main,
             !:trino-mariadb,
             !:trino-memory,
@@ -470,6 +471,7 @@ jobs:
             - { modules: plugin/trino-iceberg, profile: minio-and-avro }
             - { modules: plugin/trino-ignite }
             - { modules: plugin/trino-kafka }
+            - { modules: plugin/trino-lakehouse }
             - { modules: plugin/trino-mariadb }
             - { modules: plugin/trino-mongodb }
             - { modules: plugin/trino-mysql }

core/trino-server/src/main/provisio/trino.xml

@@ -148,6 +148,15 @@
         </artifact>
     </artifactSet>
 
+    <artifactSet to="plugin/lakehouse">
+        <artifact id="${project.groupId}:trino-lakehouse:zip:${project.version}">
+            <unpack />
+        </artifact>
+        <artifact id="${project.groupId}:trino-hdfs:zip:${project.version}">
+            <unpack useRoot="true" />
+        </artifact>
+    </artifactSet>
+
     <artifactSet to="plugin/loki">
         <artifact id="${project.groupId}:trino-loki:zip:${project.version}">
             <unpack />

docs/src/main/sphinx/connector.md

@@ -25,6 +25,7 @@ Iceberg         <connector/iceberg>
 Ignite          <connector/ignite>
 JMX             <connector/jmx>
 Kafka           <connector/kafka>
+Lakehouse       <connector/lakehouse>
 Loki            <connector/loki>
 MariaDB         <connector/mariadb>
 Memory          <connector/memory>

docs/src/main/sphinx/connector/lakehouse.md

@@ -0,0 +1,105 @@
+# Lakehouse connector
+
+The Lakehouse connector provides a unified way to interact with data stored
+in various table formats across different storage systems and metastore services.
+This single connector allows you to query and write data seamlessly, regardless of
+whether it's in Iceberg, Delta Lake, or Hudi table formats, or traditional Hive tables.
+
+This connector offers flexible connectivity to popular metastore services including
+AWS Glue and Hive Metastore. For data storage, it supports a wide range of options
+including cloud storage services such as AWS S3, S3-compatible storage,
+Google Cloud Storage (GCS), and Azure Blob Storage, as well as HDFS installations.
+
+The connector combines the features of the
+[Hive](/connector/hive), [Iceberg](/connector/iceberg),
+[Delta Lake](/connector/delta-lake), and [Hudi](/connector/hudi)
+connectors into a single connector. The configuration properties,
+session properties, table properties, and beahvior come from the underlying
+connectors. Please refer to the documentation for the underlying connectors
+for the table formats that you are using.
+
+## General configuration
+
+To configure the Lakehouse connector, create a catalog properties file
+`etc/catalog/example.properties` with the following content, replacing the
+properties as appropriate:
+
+```text
+connector.name=lakehouse
+```
+
+You must configure a [AWS Glue or a Hive metastore](/object-storage/metastores).
+The `hive.metastore` property will also configure the Iceberg catalog.
+Do not specify `iceberg.catalog.type`.
+
+You must select and configure one of the
+[supported file systems](lakehouse-file-system-configuration).
+
+## Configuration properties
+
+The following configuration properties are available:
+
+:::{list-table}
+:widths: 30, 58, 12
+:header-rows: 1
+
+* - Property name
+  - Description
+  - Default
+* - `lakehouse.table-type`
+  - The default table type for newly created tables when the `format`
+    table property is not specified. Possible values:
+    * `HIVE`
+    * `ICEBERG`
+    * `DELTA`
+  - `ICEBERG`
+:::
+
+(lakehouse-file-system-configuration)=
+## File system access configuration
+
+The connector supports accessing the following file systems:
+
+* [](/object-storage/file-system-azure)
+* [](/object-storage/file-system-gcs)
+* [](/object-storage/file-system-s3)
+* [](/object-storage/file-system-hdfs)
+
+You must enable and configure the specific file system access. 
+
+## Examples
+
+Create an Iceberg table:
+
+```sql
+CREATE TABLE iceberg_table (
+  c1 INTEGER,
+  c2 DATE,
+  c3 DOUBLE
+)
+WITH (
+  type = 'ICEBERG'
+  format = 'PARQUET',
+  partitioning = ARRAY['c1', 'c2'],
+  sorted_by = ARRAY['c3']
+);
+```
+
+Create a Hive table:
+
+```sql
+CREATE TABLE hive_page_views (
+  view_time TIMESTAMP,
+  user_id BIGINT,
+  page_url VARCHAR,
+  ds DATE,
+  country VARCHAR
+)
+WITH (
+  type = 'HIVE',
+  format = 'ORC',
+  partitioned_by = ARRAY['ds', 'country'],
+  bucketed_by = ARRAY['user_id'],
+  bucket_count = 50
+)
+```