Viewing Apache Parquet Files on Windows

Apache Parquet is a columnar storage file format primarily used in big data processing frameworks like Apache Hadoop and Apache Spark. The benefit of Parquet files is their efficiency in terms of storage and retrieval speed, especially for query engines, offering significant improvements over row-based storage formats like CSV. Unlike simple text formats, Parquet files are binary, and require specific tools or software to view their contents. In this article, we'll explore various methods you can use to view Parquet files on a Windows system.

Tools and Methods to View Parquet Files

1. Apache Drill
   Apache Drill is a powerful tool that can query a variety of data stores, including Parquet files. To use Drill:
   • Installation: Download Apache Drill's Windows version from the Apache website and proceed with the installation.
   • Running Drill: Start the Drill server by opening a command prompt and entering:
   • Querying Parquet files: Use Drill's SQL interface to query Parquet files:
   • Pros & Cons:
     • Pros: Ability to perform complex SQL queries.
     • Cons: Requires setup and familiarity with SQL.
   • Installation: Install Spark along with a JDK.
   • Running Spark Shell: In your command window navigate to the Spark home directory and run:
   • Loading Parquet Files:
   • Pros & Cons:
     • Pros: Suitable for various big data analyses.
     • Cons: Heavyweight for simple viewing tasks, Spark installation is considerably large.
   • Installation: Download the prebuilt binaries or compile from source.
   • Usage: To convert a Parquet file to JSON just run:
   • Pros & Cons:
     • Pros: Lightweight.
     • Cons: Limited to command-line users.
   • Such tools might include web applications like `plumsail.com` or `json2parquet.com`.
   • Pros & Cons:
     • Pros: No installation required; simple interface.
     • Cons: Privacy concerns; requires internet access.
   • Installation:
   • Usage:
   • Pros & Cons:
     • Pros: Python flexibility; allows data manipulation.
     • Cons: Requires familiarity with Python.

• Convert to CSV/JSON: If viewing tools are limited, consider converting the Parquet file to a more common format like CSV or JSON using tools like Spark or Parquet-mr.
• Compatibility Considerations: Ensure your tools support the specific Parquet version used to create the file.