Create BigLake Metastore Catalog Integration docs

docs/use-cases/data_lake/index.md

@@ -4,7 +4,7 @@ pagination_prev: null
 pagination_next: null
 slug: /use-cases/data-lake
 title: 'Data Lakehouse'
-keywords: ['data lake', 'lakehouse', 'iceberg', 'delta lake', 'hudi', 'paimon', 'glue', 'unity', 'rest', 'OneLake']
+keywords: ['data lake', 'lakehouse', 'iceberg', 'delta lake', 'hudi', 'paimon', 'glue', 'unity', 'rest', 'OneLake', 'BigLake']
 doc_type: 'landing-page'
 ---
 
@@ -41,6 +41,7 @@ Supported catalogs include:
 | Catalog | Guide |
 |---------|-------|
 | AWS Glue | [Glue Catalog guide](/use-cases/data-lake/glue-catalog) |
+| BigLake Metastore | [BigLake Metastore guide](/use-cases/data-lake/biglake-catalog) |
 | Databricks Unity Catalog | [Unity Catalog guide](/use-cases/data-lake/unity-catalog) |
 | Iceberg REST Catalog | [REST Catalog guide](/use-cases/data-lake/rest-catalog) |
 | Lakekeeper | [Lakekeeper Catalog guide](/use-cases/data-lake/lakekeeper-catalog) |

docs/use-cases/data_lake/reference/biglake_catalog.md

@@ -0,0 +1,103 @@
+---
+slug: /use-cases/data-lake/biglake-catalog
+sidebar_label: 'BigLake Metastore'
+title: 'BigLake Metastore'
+pagination_prev: null
+pagination_next: null
+description: 'In this guide, we will walk you through the steps to query
+ your data in Google Cloud Storage using ClickHouse and the BigLake Metastore.'
+keywords: ['BigLake', 'GCS', 'Data Lake', 'Iceberg', 'Google Cloud']
+show_related_blogs: true
+doc_type: 'guide'
+---
+
+import BetaBadge from '@theme/badges/BetaBadge';
+
+<BetaBadge/>
+
+ClickHouse supports integration with multiple catalogs (Unity, Glue, Polaris, etc.). This guide will walk you through the steps to query your Iceberg tables in [BigLake Metastore](https://cloud.google.com/biglake/docs) via ClickHouse.
+
+:::note
+As this feature is beta, you will need to enable it using:
+`SET allow_database_iceberg = 1;`
+:::
+
+## Prerequisites {#prerequisites}
+
+Before creating a connection from ClickHouse to BigLake Metastore, ensure you have:
+
+- A **Google Cloud project** with BigLake Metastore enabled
+- **Application Default credentials** (Oauth client ID and client secret) for an application, created via [Google Cloud Console](https://docs.cloud.google.com/docs/authentication/provide-credentials-adc)
+- A **refresh token** obtained by completing the OAuth flow with the appropriate scopes (e.g. `https://www.googleapis.com/auth/bigquery` and storage scope for GCS)
+- A **warehouse** path: a GCS bucket (and optional prefix) where your tables are stored, e.g. `gs://your-bucket` or `gs://your-bucket/prefix`
+
+## Creating a connection between BigLake Metastore and ClickHouse {#creating-a-connection}
+
+With the OAuth credentials in place, create a database in ClickHouse that uses the [DataLakeCatalog](/engines/database-engines/datalakecatalog) database engine:
+
+```sql
+SET allow_database_iceberg = 1;
+
+CREATE DATABASE biglake_metastore
+ENGINE = DataLakeCatalog('https://biglake.googleapis.com/iceberg/v1/restcatalog')
+SETTINGS
+    catalog_type = 'biglake',
+    google_adc_client_id = '<client-id>',
+    google_adc_client_secret = '<client-secret>',
+    google_adc_refresh_token = '<refresh-token>',
+    google_adc_quota_project_id = '<gcp-project-id>',
+    warehouse = 'gs://<bucket_name>/<optional-prefix>';
+```
+
+## Querying BigLake Metastore tables using ClickHouse {#querying-biglake-metastore-tables}
+
+Once the connection is created, you can query tables registered in the BigLake Metastore.
+
+```sql
+USE biglake_metastore;
+
+SHOW TABLES;
+```
+
+Example output:
+
+```response
+┌─name─────────────────────┐
+│icebench.my_iceberg_table │   
+└──────────────────────────┘
+```
+
+```sql
+SELECT count(*) FROM `icebench.my_iceberg_table`;
+```
+
+:::note Backticks required
+Backticks are required because ClickHouse doesn't support more than one namespace.
+:::
+
+To inspect the table definition:
+
+```sql
+SHOW CREATE TABLE `icebench.my_iceberg_table`;
+```
+
+## Loading data from BigLake into ClickHouse {#loading-data-into-clickhouse}
+
+To load data from a BigLake Metastore table into a local ClickHouse table for faster repeated queries, create a MergeTree table and insert from the catalog:
+
+```sql
+CREATE TABLE clickhouse_table
+(
+    `id` Int64,
+    `event_time` DateTime64(3),
+    `user_id` String,
+    `payload` String
+)
+ENGINE = MergeTree
+ORDER BY (event_time, id);
+
+INSERT INTO local_events
+SELECT * FROM biglake_metastore.`icebench.my_iceberg_table`;
+```
+
+After the initial load, query `clickhouse_table` for lower latency. Re-run the `INSERT INTO ... SELECT` to refresh data from BigLake when needed.