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Description

The continuous integration tool known as Apache Gump was the inaugural project created by the Apache Software Foundation. Developed in Python, it offers comprehensive support for build tools like Apache Ant and Apache Maven (versions 1.x to 3.x). What sets Gump apart is its capability to build and compile software against the most recent development iterations of various projects. This functionality enables Gump to identify potentially breaking changes to software just hours after they are committed to the version control system. Upon detecting such changes, it promptly alerts the project team, providing access to more extensive reports online for further investigation. While you can install and operate Gump on your personal computer to manage your own projects, it is predominantly recognized for its role in building numerous Apache projects and their respective dependencies. To facilitate this, the Gump initiative maintains a dedicated server specifically for its operations, ensuring efficiency and reliability in continuous integration processes. Gump's commitment to early detection of issues greatly enhances the overall software development cycle.

Description

Apache Hive is a data warehouse solution that enables the efficient reading, writing, and management of substantial datasets stored across distributed systems using SQL. It allows users to apply structure to pre-existing data in storage. To facilitate user access, it comes equipped with a command line interface and a JDBC driver. As an open-source initiative, Apache Hive is maintained by dedicated volunteers at the Apache Software Foundation. Initially part of the Apache® Hadoop® ecosystem, it has since evolved into an independent top-level project. We invite you to explore the project further and share your knowledge to enhance its development. Users typically implement traditional SQL queries through the MapReduce Java API, which can complicate the execution of SQL applications on distributed data. However, Hive simplifies this process by offering a SQL abstraction that allows for the integration of SQL-like queries, known as HiveQL, into the underlying Java framework, eliminating the need to delve into the complexities of the low-level Java API. This makes working with large datasets more accessible and efficient for developers.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Adobe Real-Time CDP
Apache Impala
ClicData
Coginiti
DataGrip
Dataiku
DigDash
Foundational
IBM API Connect
IRI CoSort
IRI Voracity
OpenText Structured Data Manager
Progress DataDirect
RazorSQL
SQL
Salesforce Data 360
Syntho
Timbr.ai
Yandex Data Proc

Integrations

Adobe Real-Time CDP
Apache Impala
ClicData
Coginiti
DataGrip
Dataiku
DigDash
Foundational
IBM API Connect
IRI CoSort
IRI Voracity
OpenText Structured Data Manager
Progress DataDirect
RazorSQL
SQL
Salesforce Data 360
Syntho
Timbr.ai
Yandex Data Proc

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Apache Software Foundation

Founded

1999

Country

United States

Website

gump.apache.org

Vendor Details

Company Name

Apache Software Foundation

Founded

1999

Country

United States

Website

hive.apache.org

Product Features

Continuous Integration

Build Log
Change Management
Configuration Management
Continuous Delivery
Continuous Deployment
Debugging
Permission Management
Quality Assurance Management
Testing Management

Product Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

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