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Data Analyst Agent

The Data Analyst Agent is a sophisticated agent that answers data questions by orchestrating analyses across multiple resources and datasets. It intelligently coordinates file analysis, integration analysis, and resource discovery to provide comprehensive data insights.

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Quick Start

from erdo.actions import bot

# Invoke the data analyst agent
result = bot.invoke(
    bot_name="data analyst",
    parameters={
        "query": "What trends do you see in our sales data?",
        "resources": ["sales_data.csv", "customer_metrics.xlsx"]
    }
)

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Features

File Analysis

Automatically analyzes file contents, structure, and data quality

Integration Analysis

Examines integration configurations and data flow patterns

Resource Orchestration

Coordinates multiple data sources and analysis tools

Intelligent Caching

Optimizes performance with smart caching strategies

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Capabilities

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Data Processing

  • File Types: CSV, Excel, JSON, Parquet, and more
  • Data Quality: Validation, profiling, and anomaly detection
  • Statistical Analysis: Descriptive statistics, correlations, trends
  • Visualization: Charts, graphs, and interactive dashboards

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Integration Support

  • Database Connections: PostgreSQL, MySQL, MongoDB, BigQuery
  • APIs: REST, GraphQL, and custom integrations
  • Cloud Storage: S3, GCS, Azure Blob Storage
  • Real-time Data: Streaming and event-driven analysis

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Advanced Features

  • Memory Integration: Stores and retrieves analysis insights
  • Resource Discovery: Automatically finds relevant data sources
  • Conditional Execution: Smart workflow optimization
  • Error Recovery: Robust handling of data issues

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Configuration

# Simple data analysis
result = bot.invoke(
    bot_name="data analyst",
    parameters={
        "resources": ["data.csv"],
        "query": "Analyze sales trends"
    }
)

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Output Format

The Data Analyst Agent returns structured analysis results: 
{
  "analysis_summary": "Overall insights and key findings",
  "data_quality": {
    "completeness": 0.95,
    "accuracy": 0.98,
    "consistency": 0.92
  },
  "insights": [
    {
      "type": "trend",
      "description": "Sales increased 15% over last quarter",
      "confidence": 0.87
    }
  ],
  "visualizations": [
    {
      "type": "line_chart",
      "title": "Sales Trend Over Time",
      "data_url": "chart_data.json"
    }
  ],
  "recommendations": [
    "Focus marketing efforts on high-performing regions",
    "Investigate seasonal patterns in Q4"
  ]
}

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Use Cases

Generate comprehensive reports combining multiple data sources, track KPIs, and identify business opportunities.
Evaluate data completeness, accuracy, and consistency across datasets. Identify and flag potential data issues.
Detect patterns, seasonal trends, and anomalies in time-series data. Forecast future performance.
Analyze customer behavior, segmentation, and lifetime value. Generate actionable insights for marketing.
Perform revenue analysis, cost optimization, and financial forecasting with real-time data integration.

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Performance Optimization

  • Incremental Analysis: Only re-analyzes changed data
  • Resource Caching: Intelligent caching of analysis results
  • Parallel Processing: Concurrent analysis of multiple resources
  • Memory Management: Efficient handling of large datasets

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Best Practices

  • Ensure data quality before analysis - Use consistent naming conventions - Document data sources and transformations - Validate data types and formats

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Troubleshooting

For datasets over 100MB, consider: - Breaking data into smaller chunks - Using columnar formats (Parquet) - Implementing data sampling strategies
Common integration issues: - Check authentication credentials - Verify network connectivity - Review API rate limits - Validate data schema compatibility
Memory optimization techniques: - Use streaming analysis for large files - Implement data pagination - Clear intermediate results - Monitor memory usage patterns