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Dashboard Assets

Business intelligence and data visualization

Dashboard assets represent business intelligence reports, visualizations, and analytics that help users understand and explore data. OpenMetadata models dashboards with a three-level hierarchy for BI platforms.

Hierarchy Overview

graph TD
    A[DashboardService<br/>Tableau, Looker, PowerBI] --> B1[Dashboard:<br/>Sales Performance]
    A --> B2[Dashboard:<br/>Customer Analytics]

    B1 --> C1[Chart:<br/>Revenue Trend]
    B1 --> C2[Chart:<br/>Regional Sales Map]
    B1 --> C3[Chart:<br/>Top Products]

    B2 --> C4[Chart:<br/>Customer Cohorts]
    B2 --> C5[Chart:<br/>Retention Rate]
    B2 --> C6[Chart:<br/>LTV Distribution]

    C1 -.->|queries| D1[Snowflake<br/>fact_orders]
    C2 -.->|queries| D2[Snowflake<br/>dim_geography]
    C4 -.->|queries| D3[Snowflake<br/>dim_customers]
    C5 -.->|queries| D4[Snowflake<br/>customer_metrics]

    B1 -.->|owned by| E1[Sales Team]
    B2 -.->|owned by| E2[Analytics Team]

    style A fill:#667eea,color:#fff
    style B1 fill:#f5576c,color:#fff
    style B2 fill:#f5576c,color:#fff
    style C1 fill:#fa709a,color:#fff
    style C2 fill:#fa709a,color:#fff
    style C3 fill:#fa709a,color:#fff
    style C4 fill:#fa709a,color:#fff
    style C5 fill:#fa709a,color:#fff
    style C6 fill:#fa709a,color:#fff
    style D1 fill:#4facfe,color:#fff
    style D2 fill:#4facfe,color:#fff
    style D3 fill:#4facfe,color:#fff
    style D4 fill:#4facfe,color:#fff
    style E1 fill:#00f2fe,color:#fff
    style E2 fill:#00f2fe,color:#fff

Why This Hierarchy?

Dashboard Service

Purpose: Represents the BI or analytics platform

A Dashboard Service is the platform that hosts dashboards and visualizations. It contains configuration for connecting to the BI tool and discovering reports.

Examples:

  • tableau-prod - Production Tableau Server
  • looker-analytics - Looker instance
  • powerbi-sales - Power BI workspace
  • superset-internal - Apache Superset for internal analytics

Why needed: Organizations use multiple BI platforms for different teams and use cases (Tableau for executive dashboards, Looker for self-service analytics, Superset for engineering). The service level organizes dashboards by platform.

Supported Platforms: Tableau, Looker, Power BI, Apache Superset, Metabase, Mode, Redash, QuickSight, Sisense, Google Data Studio

View Dashboard Service Specification →

Dashboard

Purpose: Represents a complete BI report or dashboard

A Dashboard is a collection of charts and visualizations that tell a story about the data. It has owners, tags, lineage to source tables, and contains multiple charts.

Examples:

  • Sales Performance Dashboard - Executive sales metrics
  • Customer Analytics - Customer behavior and segmentation
  • Operations KPIs - Operational metrics and health
  • Marketing Attribution - Marketing channel effectiveness

Key Metadata:

  • Charts: Individual visualizations within the dashboard
  • Data Sources: Tables and queries used
  • Lineage: Source tables → Dashboard
  • Owners: Team or users responsible
  • Tags: Department, sensitivity, business domain
  • URL: Link to live dashboard
  • Refresh Schedule: How often data updates

Why needed: Dashboards are the consumption layer of analytics. Tracking them enables: - Understanding which data powers which business decisions - Impact analysis (which dashboards break if table changes?) - Governance (who has access to sensitive dashboards?) - Discoverability (find relevant dashboards for your team)

View Dashboard Specification →

Chart

Purpose: Individual visualization within a dashboard

A Chart is a single visualization - bar chart, line chart, pie chart, table, etc. Charts have queries, data sources, and visual configurations.

Examples:

  • Monthly Revenue Trend - Line chart of revenue over time
  • Top 10 Products - Bar chart of product sales
  • Customer Segmentation - Pie chart of customer types
  • Orders Table - Tabular view of recent orders

Chart Types:

  • Bar/Column: Compare categories
  • Line: Show trends over time
  • Pie/Donut: Show composition
  • Table: Display raw data
  • Map: Geographic visualization
  • Scatter: Show correlations
  • Heatmap: Show intensity across dimensions

Why needed: Charts provide granular lineage. You can see exactly which columns from which tables feed each visualization, enabling precise impact analysis.

View Chart Specification →

Common Patterns

Pattern 1: Tableau Executive Dashboard

Tableau Service → Sales Performance Dashboard → Revenue Trend Chart
                                              → Regional Sales Map
                                              → Top Products Table

Executive dashboard with multiple visualizations from a single data source.

Pattern 2: Looker Self-Service Analytics

Looker Service → Customer Analytics Dashboard → Customer Cohorts Chart
                                               → Retention Rate Chart
                                               → LTV Distribution Chart

Self-service dashboard with drill-down capabilities.

Pattern 3: Power BI Operational KPIs

Power BI Service → Operations Dashboard → Real-Time Orders Chart
                                        → Inventory Levels Chart
                                        → Fulfillment Rate Chart

Real-time operational dashboard with live data connections.

Real-World Example

Here's how a sales team uses dashboards to track performance:

graph LR
    A[Snowflake<br/>fact_orders] --> D1[Tableau<br/>Sales Dashboard]
    B[Snowflake<br/>dim_customers] --> D1
    C[Snowflake<br/>dim_products] --> D1

    D1 --> E1[Revenue Trend<br/>Chart]
    D1 --> E2[Regional Sales<br/>Chart]
    D1 --> E3[Top Products<br/>Chart]

    D1 -.->|Owner| F[Sales Team]
    D1 -.->|Tags| G[Sales, Executive]
    D1 -.->|Refresh| H[Every 1 hour]

    style A fill:#0061f2,color:#fff
    style B fill:#0061f2,color:#fff
    style C fill:#0061f2,color:#fff
    style D1 fill:#f5576c,color:#fff
    style E1 fill:#fa709a,color:#fff
    style E2 fill:#fa709a,color:#fff
    style E3 fill:#fa709a,color:#fff

Flow: 1. Data Sources: Three Snowflake tables (fact and dimension tables) 2. Dashboard: Tableau Sales Dashboard combining all three sources 3. Charts: - Revenue trend over time (from fact_orders) - Regional breakdown (from fact_orders + dim_customers) - Top products (from fact_orders + dim_products) 4. Metadata: Owned by Sales Team, tagged for executives, refreshes hourly

Benefits:

  • Lineage: See which tables power which charts
  • Impact Analysis: Know which dashboards break if fact_orders schema changes
  • Ownership: Know who to contact for dashboard questions
  • Discoverability: Sales team can find all sales-related dashboards

Dashboard Lineage

Dashboards create lineage from data tables to business insights:

graph LR
    A[MySQL orders] --> P1[ETL Pipeline]
    P1 --> B[Snowflake fact_orders]

    B --> D1[Sales Dashboard]
    B --> D2[Executive Dashboard]
    C[Snowflake dim_products] --> D1
    C --> D2

    D1 --> R1[Revenue Chart]
    D1 --> R2[Products Chart]

    style P1 fill:#f5576c,color:#fff
    style D1 fill:#6900c7,color:#fff
    style D2 fill:#6900c7,color:#fff
    style R1 fill:#fa709a,color:#fff
    style R2 fill:#fa709a,color:#fff

Column-Level Lineage: Track which specific columns are used in which charts (e.g., orders.total_amount → Revenue Chart Y-axis).

Dashboard Data Models

Some BI tools have intermediate data models:

Looker LookML Models

Looker Service → E-commerce Model → Orders View
                                  → Customers View
               → Sales Dashboard → Uses Orders View & Customers View

Looker's semantic layer (LookML) defines reusable data models.

Power BI Datasets

Power BI Service → Sales Dataset → fact_sales Table
                                 → dim_date Table
                 → Sales Dashboard → Uses Sales Dataset

Power BI datasets are reusable data models shared across dashboards.

Embedded Analytics

Track dashboards embedded in applications:

Tableau Service → Customer Portal Dashboard → Embedded in: app.company.com/portal
                                             → Public Access: Yes
                                             → Row-Level Security: customer_id

Embedded dashboards require special security and access controls.

Entity Specifications

Entity Description Specification
Dashboard Service BI platform View Spec
Dashboard Report or dashboard View Spec
Chart Individual visualization View Spec

Each specification includes: - Complete field reference - JSON Schema definition - RDF/OWL ontology representation - JSON-LD context and examples - Platform-specific integrations

Supported BI Platforms

OpenMetadata supports metadata extraction from:

  • Tableau - Enterprise BI and visualization
  • Looker - Modern BI with semantic modeling
  • Power BI - Microsoft's BI platform
  • Apache Superset - Open-source data exploration
  • Metabase - Simple BI for everyone
  • Mode - Collaborative analytics
  • Redash - SQL-based dashboards
  • Amazon QuickSight - Cloud-native BI
  • Google Data Studio - Free BI from Google
  • Sisense - Embedded analytics platform
  • Qlik - Associative analytics
  • MicroStrategy - Enterprise analytics

Next Steps

  1. Explore specifications - Click through each entity above
  2. See lineage examples - Check out lineage from tables to dashboards
  3. BI integration - Learn how to connect your BI platform