DuckDB Data Explorer

Overview

This skill enables comprehensive local data exploration using DuckDB, supporting automated data profiling, quality analysis, and transformation workflows for CSV, Parquet, and JSON files. It provides reusable scripts for common data tasks, reference patterns for complex queries, and HTML report generation for interactive data visualization.

Quick Start

Basic Data Profiling

To quickly analyze a data file and generate a quality report:

1. Use scripts/data_profiler.py to profile the file:

   python scripts/data_profiler.py data.csv --output profile.json
   

2. Generate an HTML report:

   python scripts/html_report_generator.py profile.json report.html
   

3. Open the HTML report to view data quality metrics, null analysis, and sample data.

JSON Data Transformation

For complex JSON handling and transformation:

1. Analyze JSON structure:

   python scripts/json_transformer.py structure data.json
   

2. Transform JSON data:

   python scripts/json_transformer.py transform "*.json" "SELECT json_extract(data, '$.user.name') as name FROM json_data" --output transformed.parquet
   

Core Capabilities

1. Data Profiling and Quality Analysis

Use scripts/data_profiler.py for automated data quality assessment:

• Null analysis: Percentage of null values per column
• Data type detection: Automatic type inference and validation
• Uniqueness analysis: Distinct value counts and percentages
• Sample data: First 10 rows for quick inspection
• Summary statistics: Row counts, column counts, file type detection

When to use: Initial data exploration, data quality assessment, before data cleaning or transformation.

2. Complex JSON Handling

Use scripts/json_transformer.py for advanced JSON operations:

• Structure analysis: Understand nested JSON schemas
• Pattern matching: Extract specific fields from complex JSON
• Array operations: Flatten and transform JSON arrays
• Multi-file processing: Handle glob patterns like *.json
• Export options: Output to Parquet or JSON formats

When to use: Working with nested JSON data, API responses, log files, or document databases.

3. Interactive HTML Reports

Use scripts/html_report_generator.py to create visual data exploration reports:

• Summary dashboards: Key metrics at a glance
• Column analysis: Detailed null percentage bars with color coding
• Sample data viewer: Scrollable data tables with copy functionality
• Quality indicators: Visual quality scores for each column
• Responsive design: Works on desktop and mobile devices

When to use: Sharing data insights, creating documentation, or interactive data exploration.

4. DuckDB Query Patterns

Reference references/duckdb_patterns.md for common query patterns:

• File reading: CSV, Parquet, JSON ingestion patterns
• Data quality: Null analysis, type validation, duplicate detection
• Transformation: String operations, date handling, JSON extraction
• Performance: Optimization tips and best practices

When to use: Writing custom DuckDB queries, optimizing performance, learning DuckDB syntax.

5. JSON Function Reference

Reference references/json_functions.md for comprehensive JSON function documentation:

• Extraction functions: json_extract, json_extract_string, etc.
• Array operations: json_array_length, json_contains, etc.
• Structure analysis: json_structure, json_type, etc.
• Advanced patterns: Nested object handling, conditional operations

When to use: Complex JSON transformations, API data processing, nested data extraction.

6. Data Quality Framework

Reference references/data_quality_checks.md for comprehensive quality assessment:

• Null analysis: Multi-dimensional null value reporting
• Type validation: Consistency checking and conflict detection
• Outlier detection: Statistical methods for anomaly identification
• Duplicate analysis: Record and column-level duplication detection
• Quality scoring: Automated quality assessment metrics

When to use: Data quality audits, data cleaning workflows, data validation pipelines.

Workflow Examples

Example 1: Initial Data Exploration

# Profile a new dataset
python scripts/data_profiler.py sales_data.csv --output sales_profile.json

# Generate interactive report
python scripts/html_report_generator.py sales_profile.json sales_report.html

# Open report for exploration
open sales_report.html

Example 2: JSON Data Transformation

# Analyze JSON structure
python scripts/json_transformer.py structure api_responses.json

# Transform and flatten JSON data
python scripts/json_transformer.py transform "logs/*.json" \
  "SELECT 
    json_extract(data, '$.timestamp') as timestamp,
    json_extract(data, '$.user.id') as user_id,
    json_extract(data, '$.event.type') as event_type
   FROM json_data" \
  --output cleaned_logs.parquet

Example 3: Data Quality Assessment

# Profile data for quality issues
python scripts/data_profiler.py customer_data.parquet --output quality_profile.json

# Generate detailed quality report
python scripts/html_report_generator.py quality_profile.json quality_report.html

# Use reference queries for deeper analysis
duckdb :memory: "SELECT * FROM read_parquet('customer_data.parquet') LIMIT 10"

File Type Support

CSV Files

• Auto-detection of delimiters, headers, and data types
• Custom configuration support for non-standard formats
• Multiple file ingestion with glob patterns

Parquet Files

• Columnar format optimization
• Schema preservation and type inference
• Efficient handling of large datasets

JSON Files

• Auto-detection of JSON structure
• Support for nested objects and arrays
• Multi-file processing with glob patterns
• Complex JSON function support

Integration Patterns

With PostgreSQL Export

When exporting to PostgreSQL for analysis:

1. Profile before export: Use data profiler to understand data quality
2. Transform to Parquet: Clean and transform data locally first
3. Export to PostgreSQL: Use DuckDB's PostgreSQL extension or export Parquet
4. Validate export: Compare profiles before and after export

Batch Processing

For processing multiple files:

# Process all CSV files in directory
for file in data/*.csv; do
  python scripts/data_profiler.py "$file" --output "profiles/$(basename "$file" .csv).json"
  python scripts/html_report_generator.py "profiles/$(basename "$file" .csv).json" "reports/$(basename "$file" .csv).html"
done

Resources

scripts/

Executable Python scripts for data operations:

• data_profiler.py: Automated data quality analysis and profiling
• json_transformer.py: Complex JSON handling and transformation utilities
• html_report_generator.py: Interactive HTML report generation

references/

Comprehensive documentation for DuckDB operations:

• duckdb_patterns.md: Common query patterns and best practices
• json_functions.md: Complete JSON function reference with examples
• data_quality_checks.md: Data quality assessment frameworks and queries

assets/

Templates and resources for output generation:

• report_template.html: Interactive HTML template for data exploration reports

Best Practices

1. Start with profiling: Always profile data before transformation
2. Use HTML reports: Generate interactive reports for better insights
3. Leverage patterns: Use reference patterns for common operations
4. Validate transformations: Profile data before and after transformations
5. Handle nulls explicitly: Use DuckDB's null handling functions
6. Optimize queries: Use column pruning and early filtering
7. Document processes: Keep track of transformations for reproducibility