Serverless BI Dashboards Backed by Databricks SQLTable

Question

Pulumi · Accepted Answer

In order to create a serverless BI (Business Intelligence) dashboard backed by Databricks using Pulumi, we need to accomplish a few core tasks:

1. **Provision the Data Store**: We'll create a Databricks SQL table that will act as the data store for our BI dashboards. The SQL tables in Databricks are logical structures for organizing data. You can think of them as a way to structure your data within Databricks' data engine.

2. **Set Up Dashboards**: We'll create a Databricks SQL dashboard which can display the data in a meaningful way.

3. **Integrate Visualizations**: For a BI dashboard, we may want to add visualizations like charts or graphs that help users interpret the data. We'll use Databricks SQL visualizations for this purpose.

4. **Establish Connectivity/Endpoint**: The endpoint allows connection to our Databricks instance to query data using SQL queries. We need an endpoint that our BI tools can use to fetch data for reports.

We'll use the `databricks` provider to create a Databricks SQL table and a Dashboard. The SQL Visualization and Widgets help in presenting the data in the dashboard, and the SQL Endpoint provides a serverless compute resource which can be used by the BI tools to run queries against the SQL Tables.

Here's a Pulumi program in Python that outlines these resources:

```python
import pulumi
import pulumi_databricks as databricks

# Step 1: Provision the Data Store (create a Databricks SQL Table)
# The SQL Table is where your data will be stored.
# You'd configure the schema based on the data you're planning to analyze.
sql_table = databricks.Table("sql-table",
    name="bi_dashboard_data",
    columns=[{
        "name": "id",
        "typeText": "INT",
        "position": 1,
    }, {
        "name": "metric",
        "typeText": "VARCHAR",
        "position": 2,
    }],
    # Connect the table to your data source or import data as needed.
    tableType="EXTERNAL",
    schemaName="default",
    catalogName="hive_metastore"
)

# Step 2: Set Up Dashboards (create Databricks SQL Dashboard)
# The Dashboard is a collection of visualizations, charts, and queries.
sql_dashboard = databricks.SqlDashboard("sql-dashboard",
    name="BusinessIntelligenceDashboard",
    # You will add widgets and visualizations in further configuration.
)

# Step 3: Integrate Visualizations (create Databricks SQL Visualization)
# Visualizations display data in a graphical format.
# You would create a chart or graph here to display your business metrics.
# You would also pass the proper queryId that you get from a created SqlQuery.
sql_visualization = databricks.SqlVisualization("sql-visualization",
    name="RevenueChart",
    type="BAR",
    options="{\"config1\":\"value1\"}",  # Visualizations have configuration options like type, axis information, etc.
    queryId="replace-with-your-query-id",  # The ID of the query to visualize
    visualizationId="RevenueChart-ID"
)

# Step 4: Establish Connectivity/Endpoint (create Databricks SQL Endpoint)
# SQL Endpoints allow you to connect to your Databricks workspace and run SQL queries.
# This is a serverless compute resource that will be used by your BI tools.
sql_endpoint = databricks.SqlEndpoint("sql-endpoint",
    name="BusinessIntelligenceEndpoint",
    channel="stable",
    clusterSize="Small",
    autoStopMins=120,  # Automatically stops the cluster when it's idle for the set minutes to save costs
)
```

In this program, we have defined the bare minimum necessary to have a backend for a BI dashboard. You would replace placeholder values like `replace-with-your-query-id` with actual values obtained when you deploy your queries in Databricks.

### Further Steps:

- **Data Population**: Your next steps would be to make sure your Databricks SQL table (`sql_table`) is populated with the data that you want to visualize.
- **Query Creation**: You will need to create the necessary SQL queries in Databricks that your visualizations will be based on. This is not shown in the above code but would be a necessary step in creating a full BI solution.
- **Linking Widgets**: Combine the visualizations creating widgets and link them to the dashboard for a full-fledged report.
- **Permissions and Access**: In a production setup, you would also manage permissions to ensure the right level of access to your data and dashboards.

Remember, after defining these resources, use the Pulumi CLI to deploy your infrastructure with `pulumi up`. It's assumed that you have already set up the Pulumi CLI, authenticated with Databricks, and selected the appropriate workspace.

This setup provides you with a strong starting point. You will further customize it with more specific visualizations, widgets, and data querying logic to meet your specific BI requirements.