The component of Hadoop that prepares the RAM and CPU for Hadoop to run data in batch, stream, interactive, and graph processing is YARN (Yet Another Resource Negotiator). Here's a step-by-step explanation of how it works:

1. **Resource Management**: YARN is responsible for managing and allocating resources in a Hadoop cluster. It keeps track of the resources (RAM, CPU, etc.) available on each node in the cluster and allocates these resources to various applications running on Hadoop.

2. **Job Scheduling**: YARN also handles job scheduling. When an application submits a job to run on Hadoop, YARN determines how to break the job down into tasks, where to run these tasks, and in what order.

3. **Application Management**: YARN manages the lifecycle of applications. It starts and stops applications, monitors their progress, and retries failed tasks.

4. **Support for Multiple Processing Models**: YARN supports multiple data processing models, including batch processing (via MapReduce), stream processing, interactive processing, and graph processing. This is achieved through its pluggable architecture, which allows different processing frameworks to run on top of YARN and take advantage of its resource management capabilities.

5. **Node Manager and Resource Manager**: The two main components of YARN are the Node Manager (which runs on each machine in the cluster and keeps track of the resources on that machine) and the Resource Manager (which coordinates the allocation of resources across the cluster).

By managing resources and scheduling jobs, YARN enables Hadoop to run a variety of data processing tasks in an efficient and scalable manner.

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The component of Hadoop that prepares the RAM and CPU for Hadoop to run data in batch, stream, interactive, and graph processing is YARN (Yet Another Resource Negotiator). Here's a step-by-step explanation of how it works:

1. **Resource Management**: YARN is responsible for managing and allocating resources in a Hadoop cluster. It keeps track of the resources (RAM, CPU, etc.) available on each node in the cluster and allocates these resources to various applications running on Hadoop.

2. **Job Scheduling**: YARN also handles job scheduling. When an application submits a job to run on Hadoop, YARN determines how to break the job down into tasks, where to run these tasks, and in what order.

3. **Application Management**: YARN manages the lifecycle of applications. It starts and stops applications, monitors their progress, and retries failed tasks.

4. **Support for Multiple Processing Models**: YARN supports multiple data processing models, including batch processing (via MapReduce), stream processing, interactive processing, and graph processing. This is achieved through its pluggable architecture, which allows different processing frameworks to run on top of YARN and take advantage of its resource management capabilities.

5. **Node Manager and Resource Manager**: The two main components of YARN are the Node Manager (which runs on each machine in the cluster and keeps track of the resources on that machine) and the Resource Manager (which coordinates the allocation of resources across the cluster).

By managing resources and scheduling jobs, YARN enables Hadoop to run a variety of data processing tasks in an efficient and scalable manner.

Knowee AI · Accepted Answer

The component of Hadoop that prepares the RAM and CPU for Hadoop to run data in batch, stream, interactive, and graph processing is YARN (Yet Another Resource Negotiator). Here's a step-by-step explanation of how it works:

1. **Resource Management**: YARN is responsible for managing and allocating resources in a Hadoop cluster. It keeps track of the resources (RAM, CPU, etc.) available on each node in the cluster and allocates these resources to various applications running on Hadoop.

2. **Job Scheduling**: YARN also handles job scheduling. When an application submits a job to run on Hadoop, YARN determines how to break the job down into tasks, where to run these tasks, and in what order.

3. **Application Management**: YARN manages the lifecycle of applications. It starts and stops applications, monitors their progress, and retries failed tasks.

4. **Support for Multiple Processing Models**: YARN supports multiple data processing models, including batch processing (via MapReduce), stream processing, interactive processing, and graph processing. This is achieved through its pluggable architecture, which allows different processing frameworks to run on top of YARN and take advantage of its resource management capabilities.

5. **Node Manager and Resource Manager**: The two main components of YARN are the Node Manager (which runs on each machine in the cluster and keeps track of the resources on that machine) and the Resource Manager (which coordinates the allocation of resources across the cluster).

By managing resources and scheduling jobs, YARN enables Hadoop to run a variety of data processing tasks in an efficient and scalable manner.

Which of the following Hadoop core components prepares the RAM and CPU for Hadoop to run data in batch, stream, interactive, and graph processing?

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