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What is JAMS?

JAMS functions as an all-encompassing tool for automating workloads and scheduling jobs, crucial for managing workflows that drive business operations. This robust software is adept at automating a wide range of IT tasks, from simple batch jobs to complex workflows that span different platforms and incorporate scripts. By integrating seamlessly with various enterprise technologies, JAMS facilitates the efficient execution of jobs without human intervention, prioritizing resource allocation to ensure tasks are performed in a predetermined sequence, at scheduled times, or triggered by specific events. The centralized console offered by JAMS enables users to easily define, manage, and monitor vital batch processes. Whether handling basic command line executions or coordinating intricate multi-step operations involving ERPs, databases, and business intelligence applications, JAMS is tailored to meet the scheduling needs of organizations. Furthermore, the software enhances the migration of tasks from platforms such as Windows Task Scheduler, SQL Agent, or Cron by providing built-in conversion tools, ensuring a smooth transition with minimal disruption. Ultimately, JAMS plays a pivotal role in helping businesses streamline their job scheduling processes, thereby improving overall operational efficiency and effectiveness. By adopting JAMS, organizations can focus more on strategic initiatives while relying on automated processes to handle routine tasks.

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
Azure Marketplace
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
Azure Marketplace
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

Fortra

Date Founded

1982

Company Location

United States

Company Website

www.jamsscheduler.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

Workload Automation

Alerts / Notifications
Audit Trails
Business Process Management
Cross-Platform Scheduling
Dashboard
Data Analysis / Visualization
Event-Triggers
Load Balancing
Resource-Based Scheduling
Robotic Process Automation (RPA)
Workflow Foundation Support

Categories and Features

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