Charting Intelligence & Data-to-Viz Pipeline Engineering

Enable any agent to (1) instantly identify the exact chart needed from raw data, (2) generate the precise path of queries/transforms to materialize that chart, and (3) evaluate and choose the optimal charting library/stack based on performance, scale, and interactivity requirements.

This is not "just call a library" — it is full-stack visualization strategy.

1. Core Decision Framework — Choosing the Chart That Fits the Data AND the Story

Before any code runs, answer these questions in order:

What is the goal of the viewer?

How many variables and what types?

Audience & Context Check

Rule of Thumb Table

2. The Data Pipeline Engine

Most databases do NOT have the exact aggregation ready. Auto-generate the full pipeline:

Step A — Inventory

• Scan schema or sample 100 rows — detect column types, null rates, cardinality
• Flag missing aggregations (e.g., "no daily_sales_by_region view exists")

Step B — Required Transformations

Auto-generate SQL or pandas code for:

• Joins needed?
• GROUP BY + SUM/AVG/COUNT?
• Window functions for running totals or YoY?
• Binning (e.g., age into decades)?
• Pivot/unpivot?
• Outlier flagging or imputation?

Step C — Materialization Strategy

Step D — Validation

• Run a tiny sample query first — confirm the shape matches the chosen chart type
• If not, loop back and adjust aggregation

Example

User says "show monthly revenue by product category":

"I need: LEFT JOIN orders -> products -> categories; GROUP BY month, category; SUM(revenue). No view exists -> I will create temp table or run inline. Chart type: Stacked Area. Library recommendation below."

3. Library Selection Matrix

Always output the performance trade-off and recommended stack.

Optimization Rules (apply automatically)

• Downsample for overview, show full detail on zoom (ECharts built-in)
• Use Canvas instead of SVG above ~5k elements
• Pre-aggregate at DB level whenever possible (biggest single win)
• Lazy load charts below the fold
• Bundle size: tree-shake everything except the one chart type you need
• GPU vs CPU: if >100k points and user needs pan/zoom, force WebGL path

4. Full Workflow

1. Parse intent — identify required chart type from user request
2. Schema scan — detect column types, cardinality, row estimates
3. Decision framework — output chart recommendation + rationale
4. Generate transforms — exact SQL/pandas/transform code needed
5. Choose library — select by performance tier based on row estimate
6. Emit deliverables:
   • Chart spec (JSON for the library or React component)
   • SQL/transform script
   • Performance warning or confirmation
   • Accessibility note + alt-text template

5. Advanced Capabilities

• "Show me what I should be charting but aren't" — auto-correlation scan + suggested visuals
• "Optimize this dashboard for 10x speed" — rewrite query + switch library
• "Make this mobile-first" — auto-switch to horizontal bars + simplify
• Color-blind & accessibility mode — toggle patterns, high contrast
• Export — SVG/PNG/PDF with embedded data table