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Description

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.

Description

All components of a URL, including scheme, user, password, host, port, path, query, and fragment, can be accessed through their respective properties. Every manipulation of a URL results in a newly generated URL object, and the strings provided to the constructor or modification functions are automatically encoded to yield a canonical format. While standard properties return percent-decoded values, the raw_ variants should be used to obtain encoded strings. A human-readable version of the URL can be accessed using the .human_repr() method. Binary wheels for yarl are available on PyPI for operating systems such as Linux, Windows, and MacOS. In cases where you wish to install yarl on different systems like Alpine Linux—which does not comply with manylinux standards due to the absence of glibc—you will need to compile the library from the source using the provided tarball. This process necessitates having a C compiler and the necessary Python headers installed on your machine. It is important to remember that the uncompiled, pure-Python version is significantly slower. Nevertheless, PyPy consistently employs a pure-Python implementation, thus remaining unaffected by performance variations. Additionally, this means that regardless of the environment, PyPy users can expect consistent behavior from the library.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

APERIO DataWise Yes 
Amazon Data Firehose Yes 
Amazon SageMaker Data Wrangler Yes 
Apache DataFusion Yes 
Autymate Yes 
CSViewer Yes 
Data Sentinel Yes 
IBM Db2 Event Store Yes 
Indexima Data Hub Yes 
Mage Platform Yes 
Meltano Yes 
PuppyGraph Yes 
Python No 
QuerySurge Yes 
Semarchy xDI Yes 
Sliq Yes 
Streamkap Yes 
Tad Yes 
Timbr.ai Yes 
Warp 10 Yes 

Integrations

APERIO DataWise No 
Amazon Data Firehose No 
Amazon SageMaker Data Wrangler No 
Apache DataFusion No 
Autymate No 
CSViewer No 
Data Sentinel No 
IBM Db2 Event Store No 
Indexima Data Hub No 
Mage Platform No 
Meltano No 
PuppyGraph No 
Python Yes 
QuerySurge No 
Semarchy xDI No 
Sliq No 
Streamkap No 
Tad No 
Timbr.ai No 
Warp 10 No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

parquet.apache.org

Vendor Details

Company Name

Python Software Foundation

Country

United States

Website

pypi.org/project/yarl/

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Product Features

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