Deneb Visuals

Deneb Visuals in Power BI (PBIR)

Use pbir for every report mutation. Read PBIR metadata only for diagnosis. If pbir is unavailable or lacks an operation, stop and report the gap; never edit report JSON directly.

Deneb is a certified custom visual for Power BI that enables Vega and Vega-Lite declarative visualization specs directly inside reports. Author specs using this skill.

Provider Policy

Prefer Vega-Lite for new Deneb visuals unless specific Vega-only features are required (signals, event streams, custom projections, force/voronoi layouts). Vega-Lite is more concise, easier to maintain, and covers most chart types. For advanced Vega features, see references/vega-patterns.md and the Vega documentation.

Visual Identity

• visualType: deneb7E15AEF80B9E4D4F8E12924291ECE89A
• Bundled runtime: Vega 6.2.0 / Vega-Lite 6.4.1 (since Deneb 1.8; use v6.json schema URLs)
• Data role: Single dataset role (all fields go into one "Values" well)
• Default row limit: 10,000 rows (override via dataLimit.override)
• Provider: vegaLite (default) or vega (when Vega-specific features needed)
• Render modes: svg (default, sharp text) or canvas (better for large datasets)

Custom Visual Registration (Required)

Copying a working Deneb visual with pbir cp carries its registration. For a new visual, inspect publicCustomVisuals with pbir get and update the complete list through pbir set --json, preserving any existing ids. Never edit report.json directly.

Workflow: Creating a Deneb Visual

Step 1: Add the Visual

Create the visual and bindings through pbir:

bashCopy code
pbir add visual deneb7E15AEF80B9E4D4F8E12924291ECE89A \
  "Report.Report/Page.Page" --name RevenueByCategoryDeneb
pbir visuals bind "Report.Report/Page.Page/RevenueByCategoryDeneb.Visual" \
  --add "dataset:Sales.Category" --type Column
pbir visuals bind "Report.Report/Page.Page/RevenueByCategoryDeneb.Visual" \
  --add "dataset:Sales.Revenue" --type Measure

All fields bind to the single dataset role. Use Table.Column for columns and Table.Measure for measures. Field names in bindings must match those used in the Vega/Vega-Lite spec.

Step 2: Write the Spec

Create a Vega-Lite (or Vega) JSON spec file. Key difference:

• Vega-Lite: "data": {"name": "dataset"} (object)
• Vega: "data": [{"name": "dataset"}] (array)

jsonCopy code
{
  "$schema": "https://vega.github.io/schema/vega-lite/v6.json",
  "data": {"name": "dataset"},
  "mark": {"type": "bar", "tooltip": true},
  "encoding": {
    "y": {"field": "Category", "type": "nominal"},
    "x": {"field": "Value", "type": "quantitative"}
  }
}

See examples/spec/ for complete spec files (Vega and Vega-Lite) and examples/visual/ for full PBIR visual.json files. Field names in the spec must match the nativeQueryRef (display name) from the field bindings.

Step 3: Inject the Spec

bashCopy code
pbir visuals deneb "Report.Report/Page.Page/RevenueByCategoryDeneb.Visual" \
  --spec-file chart.vl.json --provider vegaLite

The CLI handles PBIR encoding. Keep ordinary Vega or Vega-Lite JSON in the spec file.

Step 3b: Review

Before presenting the spec to the user, dispatch the deneb-reviewer agent to validate syntax and provide design feedback.

Step 4: Validate

bashCopy code
pbir visuals bind "Report.Report/Page.Page/RevenueByCategoryDeneb.Visual" --show
pbir validate "Report.Report" --all

Spec Authoring Rules

Data Binding

• Vega: "data": [{"name": "dataset"}] (array form)
• Vega-Lite: "data": {"name": "dataset"} (object form)
• Fields reference display names. Special characters (., [, ], \, ") become _
• Spaces are NOT replaced -- field names keep their spaces (e.g., "Order Lines")

Field Name Escaping in Expressions (Critical)

Escaping depends on whether the spec is standalone or injected into a PBIR visual.json:

Standalone spec files (in examples/spec/): use double quotes with JSON escaping:

jsonCopy code
{"calculate": "datum[\"Order Lines\"] - datum[\"Order Lines (PY)\"]", "as": "diff"}

Inside PBIR visual.json (in examples/visual/): the entire spec is a single-quoted DAX literal string. Field names with spaces use doubled single quotes (''):

Copy code
datum[''Order Lines''] - datum[''Order Lines (PY)'']

Single quotes that are NOT part of field name escaping (e.g., string literals in filter expressions like datum.Series == 'Actuals') work as-is because they don't conflict with the outer single-quote wrapper.

Responsive Sizing (Vega)

Use Deneb's built-in signals for responsive container sizing:

jsonCopy code
"width": {"signal": "pbiContainerWidth - 25"},
"height": {"signal": "pbiContainerHeight - 27"}

The offsets account for padding. For absolute positioning of text marks, use {"signal": "width"} instead of hardcoded pixel values.

Config (Separate from Spec)

Always provide a config file for consistent styling. See the Standard Config section in references/vega-patterns.md. Key settings: autosize: fit, view.stroke: transparent, font: Segoe UI.

Theme Integration

Use Power BI theme colors instead of hardcoded hex values:

Interactivity

Enable interactivity via the vega objects in visual.json:

Cross-Filtering

When enableSelection is true, handle __selected__ ("on", "off", "neutral") in encode blocks. Selection modes: simple (auto-resolves, up to 250 data points) or advanced (Vega only; required for brush/lasso/region selection, supports up to 2500 via options.limit, exposes pbiCrossFilterApply and pbiCrossFilterClear signals). See references/vega-patterns.md for the simple pattern and references/advanced-patterns.md for the advanced signal API.

Cross-Highlighting

Use layered marks -- background at reduced opacity, foreground shows <field>__highlight values. See references/vega-patterns.md for details.

Special Runtime Fields

Deneb injects runtime fields into each dataset row. See references/capabilities.md for the full table.

Key fields: __row__ (zero-based row index, replaces removed __identity__), __selected__ (selection state), <field>__highlight + <field>__highlightStatus + <field>__highlightComparator (cross-highlighting), <field>__formatted (pre-formatted value string), <field>__format (Power BI format string).

Breaking change in 1.9: __identity__ and __key__ were removed. Replace any datum.__identity__ with datum.__row__.

Best Practices

1. Use Vega-Lite for new visuals unless Vega-specific features are needed (signals, events, force layouts)
2. Always use autosize: fit in config for responsive Power BI sizing
3. Use pbiContainerWidth/pbiContainerHeight signals for responsive Vega specs
4. Use theme colors (pbiColor, pbiColorNominal) instead of hex values
5. Use enter/update/hover encode blocks for clean state management (Vega only)
6. Enable tooltips with "tooltip": {"signal": "datum"} on marks
7. Performance -- aggregate in DAX first, prefer renderMode: canvas for many marks, and only then raise dataLimit.override. See references/advanced-patterns.md for the full lever order
8. Test field names -- verify nativeQueryRef matches spec field references
9. Avoid external data -- AppSource certification prevents loading external URLs
10. Escaping depends on context -- double quotes in standalone specs, doubled single quotes in PBIR visual.json (see escaping rules above)

When to Use Deneb

Deneb is the preferred choice for advanced custom visuals that need interactivity (cross-filtering, tooltips, hover effects) and go beyond what native Power BI visuals offer. Use Deneb when you need:

• Custom chart types not available natively (bullet charts, beeswarms, sankeys, etc.)
• Fine-grained control over visual encoding, animation, and interactivity
• Vector-based rendering (crisp at any size)

Use SVG measures instead for simple inline graphics in tables/cards (sparklines, data bars, progress bars) where interactivity is not needed. Use Python/R instead for statistical visualizations (distribution analysis, regression, correlation) where the focus is analytical rigor over interactivity.

References

• references/community-examples.md -- 170+ community templates organized by chart type, with author citations and direct links
• references/vega-patterns.md -- Vega chart patterns (bar, line, scatter, donut, stacked, heatmap, area, lollipop, bullet, KPI card), standard config, transforms and scales reference
• references/vega-lite-patterns.md -- Vega-Lite chart patterns (for editing existing Vega-Lite visuals only)
• references/pbir-structure.md -- PBIR JSON structure (literal encoding, query state, interactivity example)
• references/capabilities.md -- Full Deneb object properties reference and template format (usermeta schema)
• references/advanced-patterns.md -- Advanced cross-filtering signals (Vega pbiCrossFilterApply/pbiCrossFilterClear), performance engineering lever order, and community template round-trip from the terminal
• examples/visual/bullet-chart.json -- PBIR visual.json: faceted bullet chart with conditional indicators and cross-filtering (Vega-Lite)
• examples/visual/kpi-card.json -- PBIR visual.json: KPI card with layered text and conditional % change coloring (Vega-Lite)
• examples/visual/trend-line.json -- PBIR visual.json: dual-series line chart with fold transform and color/legend mapping (Vega-Lite)
• examples/visual/ytd-comparison.json -- PBIR visual.json: YTD vs target with dashed lines, endpoint labels, number formatting, and rank-based filtering (Vega-Lite)
• examples/spec/vega/ -- Standalone Vega spec files (bar-chart, line-chart) -- ready to inject into visual.json after escaping
• examples/spec/vega-lite/ -- Standalone Vega-Lite spec files (bullet-chart, kpi-card) -- ready to inject after escaping
• examples/standard-config.json -- Standard config for all Deneb specs

Fetching Docs

To retrieve current Power BI custom visual docs, use microsoft_docs_search + microsoft_docs_fetch (MCP) if available, otherwise mslearn search + mslearn fetch (CLI). Search based on the user's request and run multiple searches as needed to ensure sufficient context before proceeding. Note: Vega/Vega-Lite docs live at vega.github.io (not MS Learn) -- use WebFetch for those.

Related Skills

• pbir-format (pbip plugin) -- PBIR JSON format reference
• pbi-report-design -- Layout and design best practices
• r-visuals -- R Script visuals (ggplot2)
• python-visuals -- Python Script visuals (matplotlib)
• svg-visuals -- SVG via DAX measures (lightweight inline graphics)