> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-sync-clickhouse-operator-docs-7e82242.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Lakehouse runtime catalog (BigLake Metastore)
> In this guide, we will walk you through the steps to query your data in Google Cloud Storage using ClickHouse and the Lakehouse runtime Catalog (BigLake Metastore).
export const BetaBadge = () => {
return ;
};
ClickHouse supports integration with multiple catalogs (Unity, Glue, Polaris, etc.). This guide will walk you through the steps to query your Iceberg tables in [Lakehouse runtime catalog aka BigLake Metastore](https://docs.cloud.google.com/biglake/docs/) via ClickHouse.
As this feature is beta, you will need to enable it using:
`SET allow_database_iceberg = 1;`
Prerequisites
Before creating a connection from ClickHouse to Lakehouse runtime catalog (BigLake Metastore), ensure you have:
* A **Google Cloud project** with Lakehouse runtime catalog 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 Lakehouse runtime catalog and ClickHouse
With the OAuth credentials in place, create a database in ClickHouse that uses the [DataLakeCatalog](/core/reference/engines/database-engines/datalake) database engine:
```sql theme={null}
SET allow_database_iceberg = 1;
CREATE DATABASE lakehouse_runtime_catalog
ENGINE = DataLakeCatalog('https://biglake.googleapis.com/iceberg/v1/restcatalog')
SETTINGS
catalog_type = 'biglake',
google_adc_client_id = '',
google_adc_client_secret = '',
google_adc_refresh_token = '',
google_adc_quota_project_id = '',
warehouse = 'gs:///';
```
Querying Lakehouse runtime catalog tables using ClickHouse
Once the connection is created, you can query tables registered in the Lakehouse runtime catalog.
```sql theme={null}
USE LAKEHOUSE_RUNTIME_CATALOG;
SHOW TABLES;
```
Example output:
```response theme={null}
┌─name──────────────────────────────────────┐
│lakehouse_runtime_catalog.my_iceberg_table │
└───────────────────────────────────────────┘
```
```sql theme={null}
SELECT count(*) FROM `lakehouse_runtime_catalog.my_iceberg_table`;
```
**Backticks required**
Backticks are required because ClickHouse doesn't support more than one namespace.
To inspect the table definition:
```sql theme={null}
SHOW CREATE TABLE `lakehouse_runtime_catalog.my_iceberg_table`;
```
Loading data from Lakehouse into ClickHouse
To load data from a Lakehouse runtime catalog table into a local ClickHouse table for faster repeated queries, create a MergeTree table and insert from the catalog:
```sql theme={null}
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 lakehouse_runtime_catalog.`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.