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Description
YARN's core concept revolves around the division of resource management and job scheduling/monitoring into distinct daemons, aiming for a centralized ResourceManager (RM) alongside individual ApplicationMasters (AM) for each application. Each application can be defined as either a standalone job or a directed acyclic graph (DAG) of jobs. Together, the ResourceManager and NodeManager create the data-computation framework, with the ResourceManager serving as the primary authority that allocates resources across all applications in the environment. Meanwhile, the NodeManager acts as the local agent on each machine, overseeing containers and tracking their resource consumption, including CPU, memory, disk, and network usage, while also relaying this information back to the ResourceManager or Scheduler. The ApplicationMaster functions as a specialized library specific to its application, responsible for negotiating resources with the ResourceManager and coordinating with the NodeManager(s) to efficiently execute and oversee the execution of tasks, ensuring optimal resource utilization and job performance throughout the process. This separation allows for more scalable and efficient management in complex computing environments.
Description
Batch facilitates the execution of applications across workstations and clusters, making it simple to enable your executable files and scripts for cloud scalability. It operates a queue system designed to handle tasks you wish to run, effectively executing your applications as needed. To leverage Batch effectively, consider the data that must be uploaded to the cloud for processing, how that data should be allocated across various tasks, the necessary parameters for each job, and the commands required to initiate the processes. Visualize this as an assembly line where different applications interact seamlessly. With Batch, you can efficiently share data across different stages and oversee the entire execution process. It operates on a demand-driven basis rather than adhering to a fixed schedule, allowing customers to run their cloud jobs whenever necessary. Additionally, it's vital to manage user access to Batch and regulate resource utilization while ensuring compliance with requirements like data encryption. Comprehensive monitoring features are in place to provide insight into the system's status and to help quickly identify any issues that may arise, ensuring smooth operation and optimal performance. Furthermore, the flexibility in resource scaling allows for efficient handling of varying workloads, making Batch an essential tool for cloud-enabled applications.
API Access
Has API
API Access
Has API
Integrations
ActiveBatch Workload Automation
Apache Knox
Apache PredictionIO
Apache Ranger
Azure Marketplace
Cloudera Data Platform
DX Unified Infrastructure Management
Hue
IronCore Labs
Microsoft Azure
Integrations
ActiveBatch Workload Automation
Apache Knox
Apache PredictionIO
Apache Ranger
Azure Marketplace
Cloudera Data Platform
DX Unified Infrastructure Management
Hue
IronCore Labs
Microsoft Azure
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$3.1390 per month
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
Uniited States
Website
hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/products/batch