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Connect Your Data

Let’s start by uploading sample stock market data to the Workshop. This process takes less than 2 minutes and sets the foundation for everything else.
Download the sample data: stocks.csv. This contains stock market data with prices, volumes, and financial metrics for major tech companies.

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Create Your Project

First, let’s create a dedicated project for your data analytics.
1

Access the Infactory Workshop

Navigate to the Infactory Workshop:
https://workshop.infactory.ai
You’ll see the Infactory Workshop homepage with your projects.
2

Start a New Project

Click the “New Project” button in the top right corner.
3

Name Your Project

In the dialog that appears:
  1. Project Name: Enter Stock Market Analytics
  2. Leave other settings as default
Choosing a descriptive name like “Stock Market Analytics” or “Financial Analysis” helps Infactory’s AI understand your data context and generate better query suggestions.
4

Create project

Click “Create project” to initialize your workspace.
Success! You now have a dedicated project workspace. You’ll see your empty project dashboard.

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Connect Your Data Source

Now let’s add your stock market data to the project.
CSV upload interface with configuration fields
1

Add a Data Source

Click the ”+” button in the Connect panel on the left sidebar to add a new data source.
2

Select Connection Type

In the “Add Data Source” dialog:
  1. Select “Upload Files” from the connection types
  2. Click the upload area or drag and drop stocks.csv
Once you select the file, the interface immediately shows:
  • File: stocks.csv (8.5 KB)
  • Data Source Name: Auto-populated as StocksCsv
  • Description: Empty text field with placeholder
CSV upload interface with configuration fields

The upload interface shows your file with auto-populated name and description fields

Configure your data source before uploading:
  1. Data Source Name: Replace the auto-generated name with: 
    Stock Market Historical Data
  2. Description: Add this detailed description: 
    Historical stock market data for major tech companies including daily 
prices (open, close, high, low), trading volumes, market capitalization, 
P/E ratios, and EPS data. Contains ticker symbols, dates, price changes, 
and 52-week high/low information for market analysis and trend tracking.
Write descriptions as if explaining the data to a colleague who has never seen it. Include:
  • What type of data it contains (stock prices, financial metrics, etc.)
  • Key columns and their purpose (prices, volumes, ratios)
  • Time period or scope (if applicable)
  • Any important relationships between columns
This helps the AI understand your data context for better query suggestions and more accurate results.
  1. Click “Upload 1 File” to proceed.
You can upload multiple files at once using the “Add More Files” button if you have related datasets.
3

Schema Generation

After clicking “Upload 1 File”, Infactory automatically generates a schema for your data:
Generated dataline schema showing column definitions

AI-generated schema showing column definitions and relationships

The AI analyzes your data and creates:
  • Column definitions with appropriate data types
  • Display names that are human-readable
  • Descriptions for each field based on content analysis
  • Relationships between different columns
The schema generation uses AI to understand your data context. Because you provided a descriptive name and description earlier, the AI can better identify column purposes and relationships.
Example schema elements generated:
TICKER = Column(
    type_t=TypeT.STRING,
    col_kind_t=ColumnKindT.KEY,
    datasource_name="ticker",
    display_name="Ticker Symbol",
    description="Stock ticker symbol identifier"
)

CLOSE = Column(
    type_t=TypeT.FLOAT,
    col_kind_t=ColumnKindT.VARIABLE,
    datasource_name="close",
    display_name="Closing Price",
    description="Daily closing price of the stock"
)
Wait for the schema generation to complete (typically 10-15 seconds).
4

Success! Schema Complete

Once the schema generation is complete, you’ll see:
  • ✅ “Ready”
  • 📊 Schema with 16 columns generated
  • 🔍 Ready to build query programs
Congratulations! Your stock market data is now connected and ready for analysis.
The system has:
  • Analyzed all 16 columns
  • Generated appropriate data types and descriptions
  • Indexed your data for fast querying
  • Prepared the AI to understand your data context

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What Just Happened?

Behind the scenes, the Workshop:
  1. Parsed your CSV file and detected data types
  2. Generated a schema using AI to understand each column’s purpose
  3. Indexed the data for fast querying
  4. Used your descriptions to enhance the schema with business context
  5. Prepared it for AI-powered analysis
  6. Made it queryable through natural language
The AI schema generation:
  • Automatically identified column types (STRING, FLOAT, etc.)
  • Determined which columns are keys vs. variables
  • Created human-readable display names
  • Generated contextual descriptions for each field
The descriptive name and detailed description you provided help the AI:
  • Better understand your data’s business context
  • Generate more accurate column descriptions
  • Create more relevant query suggestions later
  • Build better API endpoint names when deploying

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Troubleshooting

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What’s Next?

Your data is connected and ready! Now let’s create Infactory Intelligent Queries to analyze your stock market data.

Continue to Build Queries

Learn how to create Infactory Intelligent Queries using natural language →
Time Check: You should be about 2 minutes into the tutorial. Everything working smoothly? Great! If not, reach out to [email protected] for immediate help.