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Dashboard Copilot

Transform your BI dashboards into intelligent AI subagents with a single command. This guide walks you through deploying Superset with PostgreSQL, configuring Datus, and using the bootstrap-bi command to automatically generate context and subagents from your dashboard.

Why Dashboard Copilot?

Traditional BI dashboards are static - they show predefined charts and metrics, but users cannot ask follow-up questions or explore data beyond what's been pre-built. Datus Dashboard Copilot transforms these static dashboards into dynamic analysis copilots that can:

  • Answer ad-hoc questions using the same data and business logic as your dashboard
  • Perform root cause analysis when metrics change unexpectedly
  • Generate new SQL queries that stay consistent with your dashboard's semantic model
  • Provide attribution analysis to explain what's driving metric changes

With one command, Datus extracts all the context from your existing dashboard - the SQL queries, table relationships, metrics definitions, and business logic - and creates AI subagents that understand your data as well as your dashboard does.

The bootstrap process automatically generates two specialized subagents: a GenSQL subagent that enables self-service data retrieval through SQL tools, and a GenReport subagent that provides analysis, drill-down, and attribution reports through metric tools.

Dashboard to Agent Architecture

Prerequisites

Before you begin, ensure you have:

  • Docker Desktop installed and running
  • Kubernetes CLI (kubectl)
  • Helm package manager
  • Python 3.12 with Datus installed

Step 1: Deploy Superset + PostgreSQL

First, install the required infrastructure tools.

Install Dependencies

brew install --cask docker
brew install helm kubectl minikube

# Install Docker
curl -fsSL https://get.docker.com | sh

# Install kubectl
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
chmod +x kubectl && sudo mv kubectl /usr/local/bin/

# Install Helm
curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

# Install minikube
curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube

# Install with Chocolatey
choco install docker-desktop
choco install kubernetes-helm
choco install kubernetes-cli
choco install minikube

Start Minikube

minikube start --driver=docker

Deploy Superset

Add the Superset Helm repository and deploy:

# Add Superset Helm repo
helm repo add superset https://apache.github.io/superset
helm repo update

# Deploy Superset with example configuration
helm upgrade --install superset superset/superset -n default -f ./examples-values.yaml

Download examples-values.yaml

Custom Values

You can customize the deployment by modifying examples-values.yaml. See the Superset Helm Chart documentation for available options.

Wait for Pods

Monitor the deployment until all pods are running:

kubectl get pods -n default -w

Wait until you see all pods in Running state before proceeding.

Set Up Port Forwarding

Expose Superset and PostgreSQL services locally:

# Forward Superset UI (port 8088)
kubectl port-forward -n default service/superset 8088:8088 &

# Forward PostgreSQL (port 15432)
kubectl port-forward -n default svc/superset-postgresql 15432:5432 &

You can now access Superset at http://localhost:8088 with default credentials admin/admin.

Step 2: Configure Datus

Configure Datus to connect to both the PostgreSQL database and Superset dashboard.

Install Required Extensions

pip install datus-postgresql datus-semantic-metricflow

Update agent.yml

Add the following configuration to your ~/.datus/agent.yml:

agent:
  namespace:
    superset:
      type: postgresql
      host: 127.0.0.1
      port: 15432
      username: superset
      password: superset
      database: examples
  dashboard:
    superset:
      username: admin
      password: admin
      extra:
        provider: db

Configuration Sections

  • namespace: Defines the database connection for SQL execution
  • dashboard: Defines the BI platform credentials for dashboard access

Step 3: Bootstrap from Dashboard

Now use the bootstrap-bi command to automatically generate context and subagents from your Superset dashboard. We'll use the World Bank's Data dashboard as an example.

Run Bootstrap Command

datus-agent bootstrap-bi --namespace demo

Interactive Flow

The command will guide you through an interactive process:

Select BI platform (superset): superset
Dashboard URL: http://localhost:8088/superset/dashboard/world_health/?native_filters_key=4X5gjZkIbnU
API base URL (e.g. https://host) (http://localhost:8088): http://localhost:8088

The system will display the dashboard information and extracted charts:

Extracted SQL from Dashboard Charts

Select the charts and tables you want to include. The bootstrap process will then automatically:

1. Build Metadata and Reference SQL

The system analyzes each chart's SQL query and generates comprehensive documentation:

Reference SQL Generation

2. Generate Semantic Model

Datus creates a semantic model with measures, dimensions, and relationships:

Semantic Model Generation

3. Extract Metrics

The system identifies and validates metrics from the dashboard queries:

Metrics Extraction

Output

After the bootstrap completes, you'll have ready-to-use subagents:

Subagent `superset_world_bank_s` saved.
Subagent `superset_world_bank_s` bootstrapped.
Attribution Sub-Agent `superset_world_bank_s_attribution` saved.

Step 4: Use Attribution Analysis

The bootstrapped attribution subagent enables powerful metric analysis capabilities.

Example Query

Datus> /superset_world_bank_s_attribution compare 2014 and 2004, find the reason of population growth

The agent performs multi-step analysis using the metrics and dimensions from your dashboard:

Attribution Analysis Process

Attribution Analysis Capabilities

The attribution subagent provides:

  • Automatic Dimension Importance Ranking - Identifies which dimensions most influence metric changes
  • Delta Contribution Calculation - Quantifies each factor's contribution to the overall change
  • Root Cause Identification - Pinpoints specific values driving metric movement

Sample Output

The analysis generates a comprehensive report with key findings:

Attribution Analysis Result

The report includes:

  • Overall Growth Metrics - Comparison of total population, growth rate, and rural population percentage
  • Top Regional Contributors - Which regions drove the most population increase
  • Top Country Contributors - Individual country contributions to the change
  • Conclusion - Summary of key insights explaining the metric movement

Generated Subagents Explained

The bootstrap-bi command creates two types of subagents:

GenSQL Subagent

The primary subagent (e.g., superset_world_bank_s) provides:

  • SQL generation within the dashboard's semantic scope
  • Context-aware queries using extracted metadata
  • Reference SQL patterns from dashboard charts

Example usage: 

/superset_world_bank_s show top 10 countries by life expectancy in 2020

GenReport Subagent (Attribution)

The attribution subagent (e.g., superset_world_bank_s_attribution) provides:

  • Metric comparison and trend analysis
  • Root cause analysis for metric changes
  • Dimension-level attribution reports

Example usage: 

/superset_world_bank_s_attribution why did healthcare spending increase between 2010 and 2020?

Next Steps

Now that you have your dashboard-powered subagents, explore more: