Lumen Dashboards Skill

Lumen Dashboards Skill

Overview

Lumen is a declarative framework for creating data dashboards through YAML specifications. Build interactive data exploration dashboards without writing code - just configuration.

What is Lumen?

Lumen provides a declarative approach to building data dashboards:

• No-code dashboards: Define everything in YAML
• Data pipelines: Sources → Transforms → Views
• Interactive exploration: Built-in filters and cross-filtering
• Component library: Reusable sources, transforms, views
• Live updates: Auto-reload and real-time data

Lumen vs Panel vs Lumen AI

Use Lumen when:

• Building standard data exploration dashboards
• Working with non-programmers
• Want rapid prototyping with configuration
• Need reproducible dashboard specifications

Use Panel when:

• Need fine-grained control over components
• Building custom application logic
• Creating novel interactions

Use Lumen AI when:

• Users need ad-hoc exploration
• Questions vary unpredictably
• Enabling self-service analytics

Quick Start

Installation

pip install lumen

Your First Dashboard

File: dashboard.yaml

sources:
  data:
    type: file
    tables:
      penguins: https://datasets.holoviz.org/​penguins/​v1/​penguins.csv

pipelines:
  main:
    source: data
    table: penguins

    filters:
      - type: widget
        field: species

layouts:
  - title: Penguin Explorer
    views:
      - type: hvplot
        pipeline: main
        kind: scatter
        x: bill_length_mm
        y: bill_depth_mm
        by: species
        title: Bill Dimensions

Launch:

lumen serve dashboard.yaml --show

Core Concepts

1. Sources

Data sources provide tables for your dashboard.

Supported sources:

• File: CSV, Parquet, Excel, JSON
• Database: PostgreSQL, DuckDB, SQLite
• REST API: JSON endpoints
• Intake: Data catalogs

Quick example:

sources:
  mydata:
    type: file
    tables:
      sales: ./​data/​sales.csv

See: Data Sources Reference for comprehensive source configuration.

2. Pipelines

Pipelines define data flows: Source → Filters → Transforms → Views

Basic pipeline:

pipelines:
  sales_pipeline:
    source: mydata
    table: sales

    filters:
      - type: widget
        field: region

    transforms:
      - type: aggregate
        by: ['category']
        aggregate:
          total_sales: {revenue: sum}

Components:

• Filters: Interactive widgets for user input
• Transforms: Data manipulation (filter, aggregate, sort, SQL)
• Views: Visualizations and tables

3. Filters

Add interactive controls:

filters:
  # Dropdown select
  - type: widget
    field: category

  # Multi-select
  - type: widget
    field: region
    multiple: true

  # Date range
  - type: widget
    field: date
    widget: date_range_slider

  # Numeric slider
  - type: param
    parameter: min_revenue
    widget_type: FloatSlider
    start: 0
    end: 100000

4. Transforms

Process data in pipelines:

Common transforms:

• columns: Select specific columns
• query: Filter rows with pandas query
• aggregate: Group and aggregate
• sort: Sort data
• sql: Custom SQL queries

Example:

transforms:
  - type: columns
    columns: ['date', 'region', 'revenue']

  - type: query
    query: "revenue > 1000"

  - type: aggregate
    by: ['region']
    aggregate:
      total: {revenue: sum}
      avg: {revenue: mean}

See: Data Transforms Reference for all transform types.

5. Views

Visualize data:

View types:

• hvplot: Interactive plots (line, scatter, bar, etc.)
• table: Data tables
• indicator: KPI metrics
• vega: Vega-Lite specifications
• altair: Altair charts
• plotly: Plotly charts

Example:

views:
  - type: hvplot
    pipeline: main
    kind: line
    x: date
    y: revenue
    by: category

  - type: indicator
    pipeline: main
    field: total_revenue
    title: Total Sales
    format: '${value:,.0f}'

See: Views Reference for all view types and options.

6. Layouts

Arrange views on the page:

layouts:
  - title: Overview
    layout: [[0, 1, 2], [3], [4, 5]]  # Grid positions
    views:
      - type: indicator
        # View 0 config...

      - type: indicator
        # View 1 config...

      - type: hvplot
        # View 2 config...

Layout types:

• Grid: [[0, 1], [2, 3]]
• Tabs: Multiple layouts become tabs
• Responsive: Adapts to screen size

See: Layouts Reference for advanced layout patterns.

Common Patterns

Pattern 1: KPI Dashboard

sources:
  metrics:
    type: file
    tables:
      data: ./​metrics.csv

pipelines:
  kpis:
    source: metrics
    table: data
    transforms:
      - type: aggregate
        aggregate:
          total_revenue: {revenue: sum}
          total_orders: {orders: sum}
          avg_order_value: {revenue: mean}

layouts:
  - title: KPIs
    layout: [[0, 1, 2]]
    views:
      - type: indicator
        pipeline: kpis
        field: total_revenue
        format: '${value:,.0f}'

      - type: indicator
        pipeline: kpis
        field: total_orders
        format: '{value:,.0f}'

      - type: indicator
        pipeline: kpis
        field: avg_order_value
        format: '${value:.2f}'

Pattern 2: Filtered Exploration

pipelines:
  explorer:
    source: mydata
    table: sales

    filters:
      - type: widget
        field: region
        label: Region

      - type: widget
        field: category
        label: Category
        multiple: true

      - type: widget
        field: date
        widget: date_range_slider

    views:
      - type: hvplot
        kind: scatter
        x: price
        y: quantity
        by: category

      - type: table
        page_size: 20

Pattern 3: Multi-Source Dashboard

sources:
  sales_db:
    type: postgres
    connection_string: postgresql://localhost/​sales
    tables: [orders, customers]

  inventory_file:
    type: file
    tables:
      stock: ./​inventory.csv

pipelines:
  sales_pipeline:
    source: sales_db
    table: orders

  inventory_pipeline:
    source: inventory_file
    table: stock

Pattern 4: Cross-Filtering

pipelines:
  main:
    source: data
    table: sales

    filters:
      - type: widget
        field: region

layouts:
  - title: Analysis
    views:
      # Clicking bar filters other views
      - type: hvplot
        pipeline: main
        kind: bar
        x: category
        y: revenue
        selection_group: category_filter

      # Responds to selection above
      - type: hvplot
        pipeline: main
        kind: scatter
        x: price
        y: quantity
        selection_group: category_filter

Pattern 5: SQL Transform

transforms:
  - type: sql
    query: |
      SELECT
        region,
        category,
        SUM(revenue) as total_revenue,
        COUNT(*) as order_count,
        AVG(revenue) as avg_order_value
      FROM table
      WHERE date >= '2024-01-01'
      GROUP BY region, category
      HAVING total_revenue > 10000
      ORDER BY total_revenue DESC

Python API

While Lumen is designed for YAML, you can also use Python:

from lumen.sources import FileSource
from lumen.pipeline import Pipeline
from lumen.views import hvPlotView
from lumen.dashboard import Dashboard
import panel as pn

# Create source
source = FileSource(tables={'sales': './​data/​sales.csv'})

# Create pipeline
pipeline = Pipeline(source=source, table='sales')

# Create view
view = hvPlotView(
    pipeline=pipeline,
    kind='scatter',
    x='price',
    y='quantity'
)

# Create dashboard
dashboard = Dashboard(
    pipelines={'main': pipeline},
    layouts=[view]
)

# Serve
dashboard.servable()

See: Python API Reference for detailed API usage.

Configuration

Global Config

config:
  title: My Dashboard
  theme: dark  # or 'default', 'material'
  sizing_mode: stretch_width
  logo: ./​logo.png
  favicon: ./​favicon.ico
  layout: column  # or 'grid', 'tabs'

Themes

config:
  theme: material
  theme_json:
    palette:
      primary: '#00aa41'
      secondary: '#616161'

Authentication

# Serve with auth
lumen serve dashboard.yaml \
  --oauth-provider=generic \
  --oauth-key=${OAUTH_KEY} \
  --oauth-secret=${OAUTH_SECRET}

Deployment

Development

# Local with auto-reload
lumen serve dashboard.yaml --autoreload --show

# Specific port
lumen serve dashboard.yaml --port 5007

Production

# Production server
panel serve dashboard.yaml \
  --port 80 \
  --num-procs 4 \
  --allow-websocket-origin=analytics.company.com

Docker

FROM python:3.11-slim
WORKDIR /​app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY dashboard.yaml data/ ./
CMD ["lumen", "serve", "dashboard.yaml", "--port", "5006", "--address", "0.0.0.0"]

See: Deployment Guide for production deployment best practices.

Best Practices

1. Source Organization

# ✅ Good: Descriptive names
sources:
  sales_database:
    type: postgres
    tables: [orders, customers]

  inventory_files:
    type: file
    tables:
      stock: ./​inventory.csv

# ❌ Bad: Generic names
sources:
  db1:
    type: postgres
  file1:
    type: file

2. Pipeline Reusability

# Define reusable pipelines
pipelines:
  base_sales:
    source: data
    table: sales
    filters:
      - type: widget
        field: region

  summary_sales:
    pipeline: base_sales  # Extends base_sales
    transforms:
      - type: aggregate
        by: ['category']
        aggregate:
          total: {revenue: sum}

3. Performance

# Limit data size for large tables
sources:
  bigdata:
    type: postgres
    tables:
      events: "SELECT * FROM events WHERE date >= '2024-01-01' LIMIT 100000"

4. User Experience

# Provide clear labels and formatting
filters:
  - type: widget
    field: region
    label: "Sales Region"  # Clear label

views:
  - type: indicator
    field: revenue
    title: "Total Revenue"
    format: '${value:,.0f}'  # Formatted display

Troubleshooting

Dashboard Won't Load

# Check YAML syntax
python -c "import yaml; yaml.safe_load(open('dashboard.yaml'))"

# Run with debug logging
lumen serve dashboard.yaml --log-level=debug

Data Not Showing

• Verify data source path/​connection
• Check table names match YAML config
• Ensure columns referenced exist in data

Performance Issues

• Limit query results (use SQL WHERE clauses)
• Reduce number of rows displayed
• Use aggregation before visualization

See: Troubleshooting Guide for common issues.

Progressive Learning Path

Level 1: Basics

1. Create simple file-based dashboard
2. Add filters
3. Create basic views

Resources:

• Quick Start (this doc)
• Data Sources Reference

Level 2: Transforms

1. Filter and aggregate data
2. Use SQL transforms
3. Chain multiple transforms

Resources:

• Data Transforms Reference

Level 3: Advanced Layouts

1. Multi-page dashboards
2. Cross-filtering
3. Custom themes

Resources:

• Layouts Reference
• Views Reference

Level 4: Production

1. Database integration
2. Authentication
3. Deployment

Resources:

• Deployment Guide

Additional Resources

Documentation

• Data Sources Reference - All source types and configuration
• Data Transforms Reference - Complete transform reference
• Views Reference - All visualization types
• Layouts Reference - Layout patterns and organization
• Python API Reference - Programmatic dashboard creation
• Deployment Guide - Production deployment
• Examples - Complete dashboard examples
• Troubleshooting Guide - Common issues

External Links

• Lumen Documentation
• Lumen Gallery
• GitHub Repository
• Community Discourse

Use Cases

Business Intelligence

• Executive dashboards
• Sales analytics
• Financial reporting
• Operational metrics

Data Exploration

• Dataset overview
• Interactive filtering
• Drill-down analysis
• Comparative views

Real-Time Monitoring

• Live data feeds
• Alert dashboards
• System metrics
• Performance tracking

Reporting

• Scheduled reports
• Standardized views
• Shareable dashboards
• Embedded analytics

Summary

Lumen enables rapid dashboard development through declarative YAML specifications.

Strengths:

• No Python code required
• Fast development cycle
• Reproducible specifications
• Built-in interactivity
• Standard dashboard patterns

Ideal for:

• Fixed dashboard layouts
• Standard data patterns
• Non-programmer dashboard creators
• Rapid prototyping

Consider alternatives when:

• Need custom application logic → Panel Dashboards
• Need ad-hoc exploration → Lumen AI
• Building novel interactions → Panel Dashboards

Related Skills

• Lumen AI - Conversational data exploration
• Panel Dashboards - Custom Python dashboards
• Plotting Fundamentals - Quick plotting with hvPlot