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Dynamic Visualization

1. Introduction​

Dynamic Visualization is an AI-powered data analysis tool inside DPL Suite that allows users to:

  1. Connect to multiple data sources
  2. Automatically generate charts and insights
  3. Perform analysis using natural language
  4. Build dashboards without manual coding
  5. It uses AI models (like GPT/Claude) to understand your data and suggest meaningful visualizations.

2. Enabling Dynamic Visualization​

Before using the tool, the Dynamic Visualization service must be running. This is a backend component that powers the UI and AI processing.

Steps:

  1. Open DPL Suite
  2. Go to Settings
  3. Click Configuration → Component
  4. Locate Dynamic Visualization

What to check​

  1. Status should be Running
  2. If not → Click Start

Without this step, the visualization app will not open

dynamic visualization

3. Launching the Application​

Dynamic Visualization is accessed through the Automation layer, which acts as the execution environment for tools.

Steps:

  1. Go to Automation tab
  2. Click Dynamic Visualization

This opens the Data Formulator interface

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4. Selecting AI Model (Important Step)​

The tool depends on an AI model to:

  1. Generate charts
  2. Understand your data
  3. Answer questions

Without configuring a model → App won’t function properly

Steps​

a. Click Select Models (top-right)

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b. Fill the fields:

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c. Click + (Add)

d. Click Use

Recommendation​

Use:

  1. GPT-5 → Best for complex analysis
  2. Claude Sonnet → Good for structured reasoning

5. Understanding the Dashboard​

This is your main working area, where you:

  1. Load data
  2. Generate visualizations
  3. Explore insights

Key Sections​

Data Input Options:

  1. EXTRACT → From images/documents
  2. UPLOAD FILE → CSV/JSON
  3. CLIPBOARD → Paste data
  4. DATABASE → Connect live data

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Demo Section​

Preloaded examples to practice visualization

6. Loading Data​

Data is the foundation of visualization. You must load data before any chart or insight can be generated.

Steps:

  1. Click Data (top menu)
  2. Choose one option:

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7. Connecting to Database​

Database connection allows you to:

  1. Work with real-time data
  2. Perform live analytics
  3. Avoid manual file uploads

Supported Databases​

  1. MySQL
  2. MSSQL
  3. PostgreSQL
  4. MongoDB
  5. BigQuery
  6. Azure Blob
  7. S3
  8. DPL Connector

8. Using DPL Connector​

DPL Connector is used when your data is inside a DPL Pack.

Steps:

a. Select DPL Connector

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b. Enter:

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c. Click Connect

What Happens Next​

  1. System reads pack
  2. Tables are loaded
  3. Data becomes available for visualization

9. Visualization Workflow​

Once data is loaded, AI starts working automatically.

Process:

  1. Data schema is detected
  2. AI analyzes patterns
  3. Suggested charts appear

You can:

  1. Ask questions (e.g., “Show monthly trend”)
  2. Generate: Bar charts, Line charts, Trends, KPIs

10. Session Controls​

Located at top-right

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11. Example Use Cases​

  1. Business dashboards
  2. Energy / IoT monitoring
  3. Anomaly detection
  4. Sales analysis
  5. AI-assisted reporting

12. Best Practices​

  1. Use clean, structured data
  2. Avoid missing/null-heavy columns
  3. Use meaningful column names
  4. Choose a strong AI model
  5. Start with sample data for testing