A command line application that helps converting raw data into highly-structured data in Parquet.
Project description
Parquest
Parquest is a project that aims to structure raw data into a structured format based on the Parquet file format. The name "Parquest" is a portmanteau of "Parquet" and "quest," symbolizing the journey of transforming raw data into a structured format.
Introduction
In today's data-driven world, dealing with large volumes of raw data can be challenging. The Parquest project provides a solution by leveraging the power of the Parquet file format to structure and organize raw data efficiently.
Features
- Data Structuring: Parquest enables you to convert raw data into a structured format based on the Parquet file format.
- Efficient Storage: The Parquet file format is designed for efficient storage and retrieval of structured data, making it ideal for big data applications.
- Columnar Storage: Parquest stores data in a columnar format, which allows for faster query performance and better compression ratios.
- Schema Evolution: Parquest supports schema evolution, allowing you to easily modify the structure of your data over time without breaking compatibility.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file parquest-0.0.15.tar.gz
.
File metadata
- Download URL: parquest-0.0.15.tar.gz
- Upload date:
- Size: 10.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/4.0.2 CPython/3.11.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | dc9cc11fd1e256dfc4ed5c9625d52cf60f89589c94caf9795273bf6a0960a489 |
|
MD5 | c304512a008af448cd9b357b30247920 |
|
BLAKE2b-256 | 8a2de79c2d9164ba8cb83148765a61f77f31ee6728ef55d5b8783591a7866c10 |
File details
Details for the file parquest-0.0.15-py3-none-any.whl
.
File metadata
- Download URL: parquest-0.0.15-py3-none-any.whl
- Upload date:
- Size: 12.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/4.0.2 CPython/3.11.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 677a41e5ec134c22bea8295d864410bf88cf8a74f9b55b8c9bf32243f7c8c166 |
|
MD5 | d13c51aeae7323a0cdc49d95ef7a36b0 |
|
BLAKE2b-256 | 3724a612ed3b047316427b94478214b438acd28bfdff95d841c9dc9e826e4339 |