SQL Data Visualization

Problem it solves

SQL queries often stop at result tables: the fields are correct, but stakeholders still ask for trends, shares, anomalies, and root causes. Pasting raw tables into a report is not clear enough, and writing chart code from scratch can interrupt the analysis flow. SQL Dataviz turns existing SQL results into deliverable charts and reduces the manual bridge between querying and visualization.

How it works

• Input and output: takes SQL query results or structured data and emits base64 PNGs that can be embedded in Markdown, email, web pages, and reports.
• Chart coverage: covers common analysis scenarios, including trend comparison, part-whole views, distributions, metric monitoring, statistical analysis, geographic heat maps, and hierarchical shares.
• Interactive output: uses Plotly to generate self-contained HTML with hover, zoom, and pan, with themes such as powerbi, dark, seaborn, and ggplot2.
• Workflow fit: pairs with sql-master for query-to-chart work and with sql-report-generator for chart-to-report work.
• Degraded mode: without dependencies, it can still provide chart selection, data-format guidance, design principles, and color suggestions, but it cannot render PNG, interactive HTML, or dashboards.

Boundaries and caveats

• It requires a Python environment; when dependencies are unavailable, only advisory and guidance features remain.
• For very large datasets, aggregate, sample, or use high-density scatter first instead of rendering raw rows directly.
• Match chart type to the business question: trends use line charts, shares use stacked or donut charts, root causes use decomposition or key-influencer views, and target achievement uses KPI or gauge visuals.
• Styles can be customized through ChartConfig, but axis labels, units, tags, and empty-value handling should still be verified before delivery.