visuals

Use quando o usuário quiser incorporar gráficos, tabelas de dados, ou outras representações visuais de dados em seu projeto. Use VegaVisual e DataGrid…

npx skills add https://github.com/microsoft/fabric-apps-analytic-templates --skill visuals

Visuals

Types of visuals

There are 2 different types of visuals that can be used in a project:

  1. Charts and Graphs: These are used to represent data in a visual format, such as bar charts, line charts, pie charts, etc. These are built using vega-lite, see references/vega-lite-visual.md for more details.
  2. Data Grids: These are used to display tabular data in a structured format, allowing for sorting, filtering, and pagination. See references/data-grid-visual.md for more details.

Packages & Imports

The visual components are provided by three packages. Always use these package imports when creating visuals.

PackagePrimary exportsExample import
@microsoft/fabric-visualsVegaVisual, types: VisualizationSpec, VegaLiteConfig, VegaVisualPropsimport { VegaVisual } from "@microsoft/fabric-visuals"
@microsoft/fabric-datagridDataGrid, types: GridColumnDef, Row, CellValue, DataGridProps, DataGridTheme, SortConfigimport { DataGrid } from "@microsoft/fabric-datagrid"
@microsoft/fabric-visuals-coreisDataTable, convertDataTableToRows, design tokensimport { isDataTable } from "@microsoft/fabric-visuals-core"

The DataTable type (used by both components) is defined in @microsoft/fabric-visuals-core and re-exported by the visual packages' type definitions.

Data Format

The chart and data grid components share a unified data prop of type DataTable (from @microsoft/fabric-visuals-core). This structured format carries column metadata (displayName, format, semanticType) that the components use for axis titles, grid headers, number formatting, and tooltips.

Using DataTable: Pass a DataTable via the data prop. The visual uses its column metadata for formatting, axis titles, and tooltips.

Static/inline data: For static data, transformed data, or plain arrays, put data: { values: [...] } directly in the Vega-Lite spec and omit the data prop.

Multiple tables in one visual: the data prop also accepts a Record<string, DataTable> for specs that bind separate layers to more than one dataset by name such as layered overlays, reference lines, and axis spines. See references/multi-data-input.md.

import { VegaVisual, useCssTheme } from "@microsoft/fabric-visuals";
import { DataGrid } from "@microsoft/fabric-datagrid";
import type { DataTable } from "@microsoft/fabric-visuals-core";

// useCssTheme() reads --color-* vars from the page and updates automatically
// when the theme changes (e.g. dark-mode toggle adds/removes the .dark class).
const theme = useCssTheme();

// Charts — pass a DataTable and Vega-Lite spec
<VegaVisual spec={vegaLiteSpec} data={dataTable} theme={theme} />

// Grids — displayName becomes column headers, format applies to cells
<DataGrid data={dataTable} theme={theme} />

// Static/inline data — no data prop needed
const inlineSpec = {
  data: { values: [{ x: 1, y: 2 }, { x: 3, y: 4 }] },
  mark: "point",
  encoding: { ... },
};

<VegaVisual spec={inlineSpec} theme={theme} />

For the DataTable schema and ColumnDef fields, see references/data-table.md.

VisualContainer defaults for every visual

VegaVisual and DataGrid wrap themselves in a VisualContainer by default. The container draws the chrome — border, padding, header (title/subtitle) — plus its built-in actions, all enabled by default. Do not add a wrapper of your own unless absolutely required.

<VegaVisual
    spec={vegaLiteSpec}
    data={dataTable}
    theme={theme}
    header={{ title: "Revenue by Region", subtitle: "Last 12 months" }}
/>

<DataGrid data={dataTable} theme={theme} header={{ title: "Top Products" }} />

Anything the default container doesn't cover — custom actions, programmatic capture, the same chrome around a non-visual etc: references/visual-container.md.

Formatting & Theme

  • Formatting rules: Number formatting, color palettes, chart-specific encoding rules, highlighting guidelines, and a default theme. See references/formatting.md.

Custom visuals

Always use the above mentioned ways to create visual when possible. If the user's request doesn't allow creation using the above methods, ask the user if they are ok with using another library for creating the visual. If they are ok with it, use the library to create the visual. If they are not ok with it, then build that visual from scratch using HTML, CSS, and JS/TS. Make sure to ask the user for any specific requirements they have for the visual, such as colors, labels, etc.

Interactivity

Both VegaVisual and DataGrid expose an onInteraction prop that emits structured, predicate-based events when the user clicks a data point or row.

Always use the onInteraction prop on VegaVisual and DataGrid to surface user selections. The component only emits the selection; the host app decides what it does — e.g. coordinating other visuals or queries on the page.

A visual renders a layered subset by binding two named datasets to two layers (data={{ all, highlighted }}) — see references/multi-data-input.md. The component renders whatever tables it is handed.

import type { InteractionEvent } from "@microsoft/fabric-visuals-core";

function handleInteraction(source: string, events: InteractionEvent[]) {
    for (const event of events) {
        if (event.action === "select") {
            // event.selections describes the clicked data as predicates
        } else if (event.action === "clear") {
            // user deselected (re-clicked same item or clicked empty space)
        }
    }
}

<VegaVisual spec={spec} data={dataTable} theme={theme}
    onInteraction={(events) => handleInteraction("salesChart", events)} />
<DataGrid data={dataTable} theme={theme}
    onInteraction={(events) => handleInteraction("detailTable", events)} />

Key concepts:

  • Clicking a different data point emits a new select (replaces the prior selection — no preceding clear).
  • Re-clicking the same item or background space in a vega-lite visual emits clear.
  • Predicates include all fields from the datum; consumers filter to the ones that are relevant to them.

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development