Hello, ParquetReader Users!

We are excited to announce that ParquetReader now supports ORC files! ORC (Optimized Row Columnar) is a powerful, high-performance format commonly used in big data environments. With this new addition, ParquetReader is more versatile than ever, allowing you to seamlessly handle Parquet, Feather, Avro and now ORC formats with ease.

What is ORC?

ORC, or Optimized Row Columnar format, is a columnar storage file format designed for efficient big data processing. Developed by the Apache Hive community, ORC provides high compression, faster reads, and better query optimization, making it a preferred choice for Hadoop, Spark, and other big data frameworks.

Compared to other formats, ORC is known for its superior performance in analytic workloads. It reduces storage overhead while allowing for quick data retrieval, thanks to features like lightweight indexes, bloom filters, and optimized column compression.

Visit https://orc.apache.org to learn more.

Why Add ORC Support?

Our goal with ParquetReader is to make working with structured data as easy and efficient as possible. Many users in the data engineering and analytics community rely on ORC alongside Parquet. By adding ORC support, we’re making it easier for you to read and convert your data seamlessly within the same tool.

What’s New with ORC Support?

With this update, you can:

- Open and Inspect ORC Files: Easily view your ORC data just like Parquet and Feather files.

- Convert ORC to CSV or JSON

- Extract Schema and Metadata: Quickly extract and analyze ORC file schemas for better data insights.

Your Feedback Matters!

We love hearing from you! Let us know how ORC support is working for you and what improvements you’d like to see in future updates. Your input helps us make ParquetReader the best tool for your data workflows.

Thank You for Your Support!

Warm regards, The ParquetReader Team