Connect to Preset

Article
12/30/2024

Preset provides modern business intelligence for your entire organization. Preset provides a powerful, easy to use data exploration and visualization platform, powered by open source Apache Superset.

You can integrate your Databricks SQL warehouses (formerly Databricks SQL endpoints) and Azure Databricks clusters with Preset.

Connect to Preset manually

In this section, you connect an existing SQL warehouse or cluster in your Azure Databricks workspace to Preset.

Requirements

Before you integrate with Preset manually, you must have the following:

A cluster or SQL warehouse in your Azure Databricks workspace.
- Compute configuration reference.
- Create a SQL warehouse.
The connection details for your cluster or SQL warehouse, specifically the Server Hostname, Port, and HTTP Path values.
- Get connection details for an Azure Databricks compute resource.
An Azure Databricks personal access token. To create a personal access token, follow the steps in Azure Databricks personal access tokens for workspace users.

Note

As a security best practice, when you authenticate with automated tools, systems, scripts, and apps, Databricks recommends that you use personal access tokens belonging to service principals instead of workspace users. To create tokens for service principals, see Manage tokens for a service principal.

Steps to connect

To connect to Preset manually, do the following:

Create a new Preset account, or sign in to your existing Preset account.
Click + Workspace.
In the Add New Workspace dialog, enter a name for the workspace, select the workspace region that is nearest to you, and then click Save.
Open the workspace by clicking the workspace tile.
On the toolbar, click Catalog > Databases.
Click + Database.
In the Connect a database dialog, in the Supported Databases list, select one of the following:
- For a SQL warehouse, select Databricks SQL Warehouse.
- For a cluster, select Databricks Interactive Cluster.
For SQLAlchemy URI, enter the following value:

For a SQL warehouse:
```
databricks+pyodbc://token:{access token}@{server hostname}:{port}/{database name}
```
For a cluster:
```
databricks+pyhive://token:{access token}@{server hostname}:{port}/{database name}
```
Replace:
- {access token} with the Azure Databricks personal access token value from the requirements.
- {server hostname} with the Server Hostname value from the requirements.
- {port} with the Port value from the requirements.
- {database name} with the name of the target database in your Azure Databricks workspace.
For example, for a SQL warehouse:
```
databricks+pyodbc://token:dapi...@adb-1234567890123456.7.databricks.azure.cn:443/default
```
For example, for a cluster:
```
databricks+pyhive://token:dapi...@adb-1234567890123456.7.databricks.azure.cn:443/default
```
Click the Advanced tab, and expand Other.

For Engine Parameters, enter the following value:

For a SQL warehouse:

{"connect_args": {"http_path": "sql/1.0/warehouses/****", "driver_path": "/opt/simba/spark/lib/64/libsparkodbc_sb64.so"}}

For a cluster:

{"connect_args": {"http_path": "sql/protocolv1/o/****"}}

Replace sql/protocolv1/o/**** with the HTTP Path value from the requirements.

For example, for a SQL warehouse:

{"connect_args": {"http_path": "sql/1.0/warehouses/ab12345cd678e901", "driver_path": "/opt/simba/spark/lib/64/libsparkodbc_sb64.so"}}

For example, for a cluster:

{"connect_args": {"http_path": "sql/protocolv1/o/1234567890123456/1234-567890-buyer123"}}

Click the Basic tab, and then click Test Connection.

Note

For connection troubleshooting, see Database Connection Walkthrough for Databricks on the Preset website.
After the connection succeeds, click Connect.

Next steps

Explore one or more of the following resources on the Preset website:

Connect to Preset