Average Ratings 0 Ratings
Average Ratings 1 Rating
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
No
API Access
Has API
No
Integrations
Apache Iceberg
No
Apache Ranger
No
Astro by Astronomer
No
CelerData Cloud
No
DataClarity Unlimited Analytics
No
Datameer
No
Hackolade
No
Hue
No
IBM App Connect
No
Nucleon Database Master
No
Integrations
Apache Iceberg
Yes
Apache Ranger
Yes
Astro by Astronomer
Yes
CelerData Cloud
Yes
DataClarity Unlimited Analytics
Yes
Datameer
Yes
Hackolade
Yes
Hue
Yes
IBM App Connect
Yes
Nucleon Database Master
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
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
No
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
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
No
Change Management
Yes
Configuration Management
No
Continuous Delivery
No
Continuous Deployment
No
Debugging
No
Permission Management
No
Quality Assurance Management
No
Testing Management
No
Product Features
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No