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What is Apache Yetus?

Apache Yetus is a collection of libraries and tools aimed at streamlining the contribution and release processes of software projects. It provides a robust framework for automatically assessing new contributions against a variety of community-approved criteria, while also enabling the documentation of a well-defined interface that supports related downstream projects. Furthermore, it equips release managers with tools to generate release documentation by utilizing data from community issue trackers and source code repositories. The project's name, Yetus, is inspired by a term associated with the Cymbium genus of gastropods, reflecting the predominant use of shell and scripting languages in its codebase. The Yetus Precommit suite for building, patching, and continuous integration allows projects to establish formal criteria for patch acceptance and evaluate incoming contributions prior to a committer's review. Additionally, the Audience Annotations feature empowers developers to use Java Annotations to specify which components of their Java libraries are meant for public access, thereby improving clarity and usability. Overall, this framework ensures that both contributors and users have a comprehensive understanding of the project's standards and guidelines, promoting a collaborative and transparent development environment.

What is Apache Hive?

Apache Hive serves as a data warehousing framework that empowers users to access, manipulate, and oversee large datasets spread across distributed systems using a SQL-like language. It facilitates the structuring of pre-existing data stored in various formats. Users have the option to interact with Hive through a command line interface or a JDBC driver. As a project under the auspices of the Apache Software Foundation, Apache Hive is continually supported by a group of dedicated volunteers. Originally integrated into the Apache® Hadoop® ecosystem, it has matured into a fully-fledged top-level project with its own identity. We encourage individuals to delve deeper into the project and contribute their expertise. To perform SQL operations on distributed datasets, conventional SQL queries must be run through the MapReduce Java API. However, Hive streamlines this task by providing a SQL abstraction, allowing users to execute queries in the form of HiveQL, thus eliminating the need for low-level Java API implementations. This results in a much more user-friendly and efficient experience for those accustomed to SQL, leading to greater productivity when dealing with vast amounts of data. Moreover, the adaptability of Hive makes it a valuable tool for a diverse range of data processing tasks.

Media

Media

Integrations Supported

iTop VPN

Integrations Supported

Adobe Real-Time CDP
Algonomy
Apache Iceberg
Apache Spark
Bytebase
Coginiti
Data Virtuality
Dataiku
Mage Dynamic Data Masking
Predibase
Qlik Staige
RATH
Salesforce Data 360
SecuPi
StreamFlux
Truedat
Varada
WEBDEV
lakeFS

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

yetus.apache.org

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

hive.apache.org

Categories and Features

Software Testing

Not specified

Categories and Features

ETL

Not specified

Query Engines

Not specified

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