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What is ROC Maestro?

ROC Software presents ROC Maestro, an intuitive and powerful solution designed for job scheduling across diverse platforms such as UNIX®, Linux®, and Windows®. This innovative tool enhances the management of job scheduling, thereby expanding your operational potential. ROC Maestro's compact architecture ensures that you won’t encounter challenges related to resource constraints, as it does not require a dedicated server or large external databases for effective cross-platform job scheduling. Furthermore, its sophisticated graphical user interface guarantees user-friendliness. By adopting ROC Maestro, you can protect your existing IT investments while facilitating smooth automated batch processing and saving precious time and resources in managing administration tasks. This platform simplifies task automation and monitoring through reusable calendars, events, cross-platform dependencies, and detailed reporting capabilities. Additionally, ROC Maestro grants you centralized control and visibility, enabling you to navigate the intricacies of various platforms and applications with ease. As a result, overseeing your job schedules transforms into a more efficient and streamlined operation, ultimately enhancing your overall productivity.

What is Apache Hadoop YARN?

The fundamental principle of YARN centers on distributing resource management and job scheduling/monitoring through the use of separate daemons for each task. It features a centralized ResourceManager (RM) paired with unique ApplicationMasters (AM) for every application, which can either be a single job or a Directed Acyclic Graph (DAG) of jobs. In tandem, the ResourceManager and NodeManager establish the computational infrastructure required for data processing. The ResourceManager acts as the primary authority, overseeing resource allocation for all applications within the framework. In contrast, the NodeManager serves as a local agent on each machine, managing containers, monitoring their resource consumption—including CPU, memory, disk, and network usage—and communicating this data back to the ResourceManager/Scheduler. Furthermore, the ApplicationMaster operates as a dedicated library for each application, tasked with negotiating resource distribution with the ResourceManager while coordinating with the NodeManagers to efficiently execute and monitor tasks. This clear division of roles significantly boosts the efficiency and scalability of the resource management system, ultimately facilitating better performance in large-scale computing environments. Such an architecture allows for more dynamic resource allocation and the ability to handle diverse workloads effectively.

Media

Media

Integrations Supported

ActiveBatch Workload Automation
Apache Knox
Apache PredictionIO
Apache Ranger
Cloudera Data Platform
DX Unified Infrastructure Management
Hue
IronCore Labs
RunCode
Sematext Cloud
Terminals
Velotix
WINDEV

Integrations Supported

ActiveBatch Workload Automation
Apache Knox
Apache PredictionIO
Apache Ranger
Cloudera Data Platform
DX Unified Infrastructure Management
Hue
IronCore Labs
RunCode
Sematext Cloud
Terminals
Velotix
WINDEV

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

ROC Software

Date Founded

1992

Company Location

United States

Company Website

www.rocsoftware.com

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

Uniited States

Company Website

hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html

Categories and Features

Categories and Features

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