Data Lineage Visualizer: Auto-Generate Flowcharts from a Spreadsheet Catalog

Stop guessing where your data flows: auto-generate lineage diagrams from a spreadsheet

Wasting time drawing data maps by hand? Struggling to prove where data came from for an audit or an AI model? In 2026, data teams must move faster: a simple tabular data catalog should automatically produce a readable flowchart that shows how data moves between systems, tables, and services. This guide gives you a step-by-step tutorial and a ready-to-run Google Apps Script that converts a spreadsheet catalog into a CSV or SVG flowchart you can embed, export, or version-control.

Why automatic data lineage matters now (2026)

Modern enterprises face three converging realities:

• AI-first initiatives need high data trust—model explainability depends on knowing provenance and transformations.
• Micro apps and low-code tools let non-developers build integrations; that increases the number of data touchpoints you must track.
• Regulation and security require auditable lineage for PII and sensitive attributes.

Recent studies in late 2025 and early 2026 highlight that weak data management and siloed catalogs remain the largest friction points for scaling enterprise AI and automation. That makes lightweight, automated lineage visualizers a high-impact, low-effort win for operations and small businesses.

What you'll build: simple, dependable lineage diagrams from a sheet

By the end of this tutorial you'll have:

• A recommended spreadsheet schema (columns and examples)
• A Google Apps Script that reads the catalog and outputs either:
• a CSV 'edges' file (for import into external diagramming tools), and
• a self-contained SVG file (stored to Drive and embeddable in docs).
• Automation tips: trigger generation on changes, enrich with APIs, or stream to Zapier.

1) Design a practical spreadsheet data catalog

Start with a single sheet named Catalog. Keep rows as canonical mapping records: each row represents a flow from a source field (or table) to a target field (or table), and optional transformation notes.

Minimum recommended columns

• ProcessID — unique ID for the ETL/Job (optional but useful)
• SourceSystem — e.g., Salesforce, MySQL, S3
• SourceTable — table or object name
• SourceField — optional field/column name
• TargetSystem
• TargetTable
• TargetField
• Transformation — short note: join, normalize, hash, enrich
• Sensitivity — e.g., Public, Confidential, PII (for color-coding)
• Owner — data owner or team contact

Sample rows

Example (one row):

ProcessID: ETL_1001 | SourceSystem: Shopify | SourceTable: orders | SourceField: customer_email | TargetSystem: Redshift | TargetTable: orders_enriched | TargetField: email_hash | Transformation: SHA256 | Sensitivity: PII

2) How the visualizer works (conceptual)

The script follows a straightforward pipeline:

1. Read all rows from the Catalog sheet.
2. Normalize system/table names and build unique node IDs (System:Table or System only).
3. Create an edge for each mapping row (source node → target node) with metadata (transformation, sensitivity).
4. Compute a simple layout (left-to-right by role or system category; vertical stacking to avoid overlap).
5. Render either a CSV of edges or an inline SVG with boxes and connectors. Save to Drive and/or write back to the spreadsheet.

3) Google Apps Script: read a catalog and generate an SVG

Below is a compact, production-ready Apps Script. It focuses on clarity and extensibility: you can add API enrichment, change the layout algorithm, or build a dot/mermaid exporter.

// Apps Script: Catalog > SVG Lineage Generator
function generateLineageSvg() {
  const ss = SpreadsheetApp.getActive();
  const sheet = ss.getSheetByName('Catalog');
  if (!sheet) throw new Error('Sheet named "Catalog" not found.');

  const data = sheet.getDataRange().getValues();
  const headers = data.shift().map(h => (h || '').toString().trim());

  // Column index helper
  const col = (name) => headers.indexOf(name);
  const rows = data.map(r => ({
    process: r[col('ProcessID')],
    sourceSystem: String(r[col('SourceSystem')] || '').trim(),
    sourceTable: String(r[col('SourceTable')] || '').trim(),
    targetSystem: String(r[col('TargetSystem')] || '').trim(),
    targetTable: String(r[col('TargetTable')] || '').trim(),
    transformation: String(r[col('Transformation')] || '').trim(),
    sensitivity: String(r[col('Sensitivity')] || 'Public').trim(),
  }));

  // Build nodes and edges
  const nodes = {};
  const edges = [];
  function nodeId(system, table){
    const id = (system + ':' + (table || '')).replace(/\s+/g,'_');
    if (!nodes[id]) nodes[id] = {id, system, table};
    return id;
  }

  rows.forEach(r => {
    const src = nodeId(r.sourceSystem, r.sourceTable);
    const tgt = nodeId(r.targetSystem, r.targetTable);
    edges.push({src, tgt, t: r.transformation, s: r.sensitivity, p: r.process});
  });

  // Simple layout: group nodes by system side: sources left, targets right
  // Vertical stacking by occurrence order
  const leftX = 40, rightX = 760, boxW = 220, boxH = 36, vPad = 12;
  const nodeList = Object.values(nodes);

  // Assign side based on if node appears only as source, only as target, or both
  const appearsAs = {};
  edges.forEach(e => {appearsAs[e.src] = (appearsAs[e.src]||{}); appearsAs[e.src].asSrc = true; appearsAs[e.tgt] = (appearsAs[e.tgt]||{}); appearsAs[e.tgt].asTgt = true;});

  const leftNodes = [], rightNodes = [], middleNodes = [];
  nodeList.forEach(n => {
    const flag = appearsAs[n.id] || {};
    if (flag.asSrc && !flag.asTgt) leftNodes.push(n);
    else if (flag.asTgt && !flag.asSrc) rightNodes.push(n);
    else middleNodes.push(n);
  });

  // Vertical position assignment
  function place(list, x){
    return list.map((n,i) => {
      n.x = x; n.y = 40 + i*(boxH + vPad); return n;
    });
  }
  const placedLeft = place(leftNodes, leftX);
  const placedMiddle = place(middleNodes, (leftX + rightX)/2);
  const placedRight = place(rightNodes, rightX);

  const placed = [...placedLeft, ...placedMiddle, ...placedRight];
  const byId = {}; placed.forEach(n => byId[n.id] = n);

  // Color map for sensitivity
  const colorFor = s => ({'PII':'#ff6b6b','Confidential':'#ffb86b','Public':'#86e1b7'}[s] || '#d0d0ff');

  // Build SVG
  const width = 1024, height = Math.max(300, ...placed.map(n => n.y + boxH + 20));
  const xmlns = 'http://www.w3.org/2000/svg';
  let svg = [];
  svg.push(`');
  const svgContent = svg.join('\n');

  // Save SVG to Drive
  const blob = Utilities.newBlob(svgContent, 'image/svg+xml', 'data-lineage.svg');
  const file = DriveApp.createFile(blob);
  SpreadsheetApp.getUi().alert('SVG created: ' + file.getUrl());
}

function escapeXml(s){
  return (s||'').toString().replace(/&/g,'&').replace(//g,'>');
}

How it works: The script groups nodes into left (sources), middle (both), and right (targets), stacks them vertically, draws smooth cubic connectors, and color-codes edges by sensitivity. It saves the resulting SVG into Google Drive and alerts you with a link.

4) Exporting CSV for external diagram tools

If you prefer to use diagrams.net, Mermaid, or Graphviz, generate a simple edge CSV (source,target,label). Here's an Apps Script snippet that writes a CSV to a new sheet:

function exportEdgesCsv() {
  const ss = SpreadsheetApp.getActive();
  const sheet = ss.getSheetByName('Catalog');
  const data = sheet.getDataRange().getValues();
  const headers = data.shift().map(h => h.toString().trim());
  const col = (name) => headers.indexOf(name);
  const rows = data.map(r => [
    `${r[col('SourceSystem')]}:${r[col('SourceTable')]}`,
    `${r[col('TargetSystem')]}:${r[col('TargetTable')]}`,
    `${r[col('Transformation')] || ''}`
  ]);
  const out = [['source','target','label'], ...rows];
  const outSheet = ss.getSheetByName('EdgesCSV') || ss.insertSheet('EdgesCSV');
  outSheet.clear();
  outSheet.getRange(1,1,out.length,out[0].length).setValues(out);
}

5) Automate generation: triggers and integrations

Automation is where this visualizer becomes operational:

• Time-driven triggers — regenerate nightly or hourly to keep diagrams fresh for reporting.
• On-edit triggers — in Apps Script, use an installable onChange trigger to update on sheet edits (use carefully for rate limits).
• Zapier/Webhook — push a webhook from other systems (e.g., when a new ETL job is deployed) to a small cloud function that updates the sheet and triggers Apps Script via the Sheets API.
• APIs to enrich metadata — call your data catalog (Collibra, Alation) or cloud providers to add schema or sensitivity tags before generating SVGs.

Practical tip

For high-frequency updates, write only deltas to a staging sheet and batch process them to avoid hitting Google API quotas.

6) Advanced strategies and 2026 trends

Things to add as your lineage needs grow:

• Field-level lineage — extend the script to create nodes for fields (sourceField → targetField). Use zoom levels in the SVG (click to open detailed maps).
• Interactive HTML + SVG — embed the SVG inside an HTML service in Apps Script to get tooltips, click-to-open Docs, and live searches.
• Enrichment via AI — in 2026, many teams are using small AI models to classify transformations (e.g., detecting hashing, masking). Call an AI endpoint to auto-label transformations and flag risky ones.
• Governance hooks — add validations that block a deployment if new flows expose PII without a transformation or owner.
• Integration with workflows — create a Zapier action: when an approved mapping is added, send a message to Slack and regenerate the public SVG used in runbooks.

7) Example: small e-commerce case study

One of our customers—an online retailer—reduced time-to-audit from 3 days to 2 hours by switching to a spreadsheet-led lineage visualizer. They used the simple schema above plus a nightly Apps Script run. Benefits included:

• Faster incident triage: the SVG highlighted the ETL job that produced malformed emails.
• Simpler onboarding for BI: analysts could visually trace where SKU enrichment happened.
• Compliance-ready exports: they attached the SVG to audit evidence packages.

8) Troubleshooting & limitations

Expect these common issues and how to solve them:

• Overlapping nodes — increase vertical spacing or add a smarter layout (force-directed or grid clustering). For complex graphs, export to Graphviz (dot) for better automatic layout.
• Too many edges — aggregate edges by table (instead of field) or create filtered views (per-system or per-process) and generate per-view SVGs.
• SVG size/performance — large SVGs may be slow; consider paginated or tiled diagrams, or create PNG thumbnails for dashboards.
• Security — do not publish PII values in the diagram. Use tags and mask details. Keep the Drive file access controlled.

9) Alternatives: Excel and cloud-native tools

If you use Excel on Microsoft 365, you can implement the same logic with Office Scripts or a small Power Automate flow to produce SVG via an Azure function or to push an edge CSV into Power BI or Visio. For teams using central catalogs (Alation, Collibra), use their APIs to export mapping tables into your spreadsheet and let the script generate visuals for lightweight sharing.

10) Keep it trustworthy: governance & auditability

Automated visualizations help, but governance ensures they are trusted. Follow these best practices:

• Record a source-of-truth: your sheet should be an exportable artifact with timestamps and an auditable change log (use sheet-level version history or append-only audit rows).
• Assign data owners and require an owner field for every mapping row.
• Flag transformations that remove or mask identifiers and require a justification or ticket ID.

"Weak data management and low trust limit AI adoption." — Recent industry reports in 2025–26 emphasize lineage as a foundational capability for enterprise AI.

Next steps: practical checklist

1. Create a Catalog sheet using the recommended schema.
2. Paste the Apps Script in Extensions > Apps Script and run generateLineageSvg().
3. Set up a time-driven trigger for nightly generation.
4. Create an EdgesCSV sheet if you want to import into other tools.
5. Add a Zapier/Webhook to notify stakeholders when the SVG changes.

Wrapping up: why this approach pays off in 2026

As organizations scale AI, micro apps, and low-code automations, the number of hidden data paths grows rapidly. A lightweight, spreadsheet-driven lineage visualizer gives operations and small business owners a practical way to document, audit, and communicate data movement without heavy tooling. You get transparency, faster audits, and fewer breaks in your data supply chain—without overhauling your stack.

Actionable takeaway: Start with a simple catalog, run the Apps Script, and automate the generation. In two hours you'll have a living diagram that helps you triage incidents, onboard analysts, and satisfy auditors.

Call to action

Ready to implement this in your environment? Download the full Apps Script, spreadsheet template, and a premium SVG layout pack from spreadsheet.top/templates. If you want a customized diagram layout (field-level, clickable tooltips, or integration into your CI/CD), reply with your catalog sample and we'll provide a tailored solution.

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