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This guide shows you how to analyze transportation data using natural language queries without SQL. You'll work with the dremio_samples.nyc_citibikes.citibikes table that contains over 115 million real bike-sharing records from New York City, one of the world's busiest transportation networks.

Step 1: Explore Available Data​

Understanding your dataset structure is crucial before diving into analysis. Start by getting an overview of the bike-sharing data to understand what insights are possible.

On the Home page, enter the following prompt into Dremio's AI agent chat box:

Give me an overview of the nyc_citibikes.citibikes dataset

The AI agent uses capabilities like semantic search to find relevant datasets. Review the response to understand the schema, field definitions, and what types of analysis are possible with bike-sharing data.

Step 2: Compare User Behavior​

Ask the AI agent to compare how subscribers and casual riders use the service:

Give me the total number of rides and the average trip duration grouped by user type across the dataset.

The AI agent generates and runs SQL using Dremio's query engine. Review the results to see how different user types interact with the service.

Step 3: Analyze Peak Demand Patterns​

Ask the AI agent to reveal hourly demand patterns:

Analyze hourly ride patterns to find when demand peaks. Show a line chart of total rides per hour of the day, separated by user type, and include a short report highlighting the busiest hours for each group and recommendations for bike availability.

The AI agent creates a chart showing distinct patterns and provides a written analysis with recommendations.

Step 4: Identify Key Factors​

Ask the AI agent to run a comprehensive comparative analysis:

Run comparative analysis on seasonal, daily, hourly, and bike type patterns to identify which factor has the most significant impact on ride behavior. Then create a detailed visualization of the most influential factor.

The AI agent compares multiple variables, identifies the most influential factor, and provides actionable recommendations.

Step 5: Try Your Own Analysis​

Now that you understand how to analyze transportation data with the AI agent, try exploring other bike-sharing questions.

You can also analyze your own data using Dremio's AI agent.

Summary​

You completed a data analysis using natural language queries with Dremio's AI agent. You explored dataset structure, compared user patterns, and identified key factors in minutes.

Next Steps​

Now that you have experienced Dremio's AI agent with sample data, you can connect your own data sources and build production use cases. The most common next step is to connect an object storage source like Amazon S3 or Azure Data Lake Storage. See Add Your Data for connection instructions.

Troubleshoot​

  • If a prompt does not work as expected, try simplifying the request or verifying that you are referencing the correct dataset (nyc_citibikes.citibikes). Break complex questions into smaller steps.
  • If the AI agent cannot find the dataset, verify that sample data is enabled in your project. Contact your administrator if sample data is not available in your environment.
  • If query results are unexpected, review the generated SQL by clicking the query in the Jobs page. You can modify and re-run the SQL directly.