> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/concepts/resources/lakehouse/supported-sources/lakehouse-on-s3.md).

# Lakehouse on S3

Create a Lakehouse Resource on an Amazon Simple Storage Service (S3) data source.

## Prerequisites

* Access to an S3 bucket with the right permissions.
* PostgreSQL database credentials for the metastore.
* A valid compute resource in your DataOS instance.
* The Lakehouse domain present in the instance.

## Procedure

{% stepper %}
{% step %}

#### Create the metastore secret

Create a secret manifest for the PostgreSQL metastore credentials:

```yaml
name: ${metastore-secret-name}
version: v2alpha
type: secret
description: PostgreSQL metastore secret for Lakehouse
secret:
  type: key-value
  data:
    username: ${postgres-username}
    password: ${postgres-password}
```

Apply the manifest:

```bash
dataos-ctl resource apply -f ${metastore-secret-manifest-path}
```

{% endstep %}

{% step %}

#### Create the storage secret

Create a secret manifest for the S3 storage credentials:

```yaml
name: ${storage-secret-name}
version: v2alpha
type: secret
description: S3 storage secret for Lakehouse
secret:
  type: key-value
  data:
    aws_access_key: ${aws-access-key}
    aws_secret_key: ${aws-secret-key}
    storage_type: s3
```

Apply the manifest:

```bash
dataos-ctl resource apply -f ${storage-secret-manifest-path}
```

{% endstep %}

{% step %}

#### Create the Lakehouse manifest

After you set up the secrets, create the Lakehouse manifest file. This file defines the Lakehouse configuration: compute resource, metastore, storage, and Spark Cluster settings.

```yaml
name: ${lakehouse-name}
version: v1alpha
type: lakehouse
description: Lakehouse on S3 storage
tags:
  - lakehouse
  - s3
spec:
  compute: ${compute-name}
  runAsUser: ${user-id}
  logLevel: INFO
  iceberg:
    metastore:
      type: iceberg-jdbc-catalog
      replicas: 1
      secret: ${tenant}:${metastore-secret-name}
      postgresql:
        host: ${postgres-host}
        port: "5432"
        database: ${database-name}
        schema: public
      hadoopConf:
        fs.s3a.connection.maximum: 1000
      resources:
        requests:
          cpu: "1"
          memory: 1000Mi
        limits:
          cpu: "1"
          memory: 2000Mi
    storage:
      type: s3
      s3:
        bucket: ${s3-bucket-name}
        relativePath: ${relative-path}
        scheme: s3a
        format: ICEBERG
      secret: ${tenant}:${storage-secret-name}
    sherpa:
      replicas: 1
      resources:
        requests:
          cpu: 200m
          memory: 512Mi
        limits:
          cpu: 500m
          memory: 1000Mi
    sparkCluster:
      server:
        requests:
          cpu: 200m
          memory: 512Mi
        limits:
          cpu: 500m
          memory: 1000Mi
      driver:
        coreLimit: "2048m"
        cores: 1
        memory: "2048m"
      executor:
        coreLimit: "2048m"
        cores: 1
        memory: "2048m"
        instances: 1
```

{% endstep %}

{% step %}

#### Apply the Lakehouse manifest

Apply the Lakehouse manifest to provision the Lakehouse. Replace all placeholders (`${}`) with actual values before applying.

```bash
dataos-ctl resource apply -f ${lakehouse-manifest-path}
```

{% endstep %}

{% step %}

#### Verify the Lakehouse creation

Check that the Lakehouse was created successfully:

```bash
dataos-ctl resource get -t lakehouse
```

For detailed status including internal service URLs:

```bash
dataos-ctl resource get -t lakehouse -n ${lakehouse-name} -d
```

{% endstep %}
{% endstepper %}


---

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```
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