Parquet & Arrow Viewer Professional Columnar Data Analysis

Advanced Parquet and Arrow file viewer powered by unified Rust-based ParquetViewer API. View, analyze, and explore columnar data files with exceptional performance. Direct JSON output, streamlined architecture, and native macOS integration for data engineering and analytics workflows.

🦀 Rust-Based Backend

📊 Unified API

⚡ Fast JSON Output

📥 Download DevHelper 👀 See Features

DevHelper Parquet Viewer Screenshot showing columnar data analysis interface

Advanced Data Viewing Features

Everything you need for professional columnar data file analysis and exploration.

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Unified Rust Backend

Single Rust-based ParquetViewer API handles both Parquet and Arrow files with exceptional performance, replacing multiple dependencies for streamlined architecture.

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Dual Format Support

Native support for both Apache Parquet and Arrow files with automatic format detection and optimized rendering for different columnar data structures.

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Fast JSON Output

Direct JSON output from ParquetViewer provides faster loading times and simplified data processing without intermediate conversions or complex parsing.

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Data Exploration

Comprehensive data exploration with column inspection, data type analysis, null value detection, and statistical summaries for thorough data understanding.

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Schema Analysis

Detailed schema inspection showing column names, data types, nested structures, and metadata for comprehensive data structure understanding.

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Simplified Architecture

Streamlined implementation without DuckDB-swift and arrow-swift dependencies, focusing on core file viewing functionality with improved reliability.

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Large File Support

Efficient handling of large Parquet and Arrow files with memory-optimized loading, pagination support, and responsive performance for big data analysis.

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Easy Data Export

Export data views to JSON format, copy specific columns or rows, and integrate with other data analysis tools and workflows seamlessly.

How to Use Parquet Viewer

Analyze columnar data files efficiently in just a few simple steps.

Load Data File

Open your Parquet or Arrow file using the file selector. DevHelper automatically detects the format and prepares the data for viewing.

Explore Schema

Review the data schema, column types, and structure. Understand nested data, array fields, and complex data types in your columnar files.

Analyze Data

Browse through data rows, inspect column values, and analyze data patterns. Use built-in tools to understand data distribution and quality.

Export Results

Export data to JSON format or copy specific sections for use in other analysis tools, reports, or data processing pipelines.

Perfect for These Development Tasks

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Data Engineering

Inspect ETL pipeline outputs, validate data transformations, and debug columnar data processing workflows in big data applications.

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Analytics & BI

Preview analytics data, validate business intelligence datasets, and explore data warehouse outputs before downstream processing.

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Data Science

Explore machine learning datasets, validate feature engineering outputs, and inspect training data in Parquet format for ML workflows.

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Data Quality Assurance

Validate data integrity, check for null values, verify schema compliance, and ensure data quality in columnar storage formats.

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Cloud Data Analysis

Inspect data from cloud storage systems, validate S3 data lakes, and analyze data warehouse exports in columnar formats.

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Performance Optimization

Analyze data compression ratios, inspect column organization, and optimize Parquet file structures for better query performance.

Frequently Asked Questions

What file formats are supported?

DevHelper supports both Apache Parquet (.parquet) and Apache Arrow (.arrow, .feather) file formats with automatic format detection and optimized rendering for each format type.

How large files can the viewer handle?

The Rust-based ParquetViewer backend efficiently handles large files with memory-optimized loading. Performance depends on available system memory, but files in the gigabytes range are typically well-supported.

What happened to the SQL editor feature?

DevHelper removed the SQL editor to focus on core file viewing functionality. The simplified architecture using unified ParquetViewer API provides better performance and reliability for data exploration.

Can I export data to other formats?

Currently, DevHelper exports data to JSON format directly from the ParquetViewer API. This JSON output can be easily imported into other data analysis tools and processing workflows.

Does it support nested and complex data types?

Yes! DevHelper properly displays nested structures, arrays, maps, and other complex Parquet and Arrow data types with hierarchical visualization and detailed schema inspection.

How does the unified API improve performance?

The single Rust-based ParquetViewer backend eliminates multiple dependency layers, reduces memory overhead, and provides direct JSON output, resulting in faster loading times and improved stability.