Category: Analytics
Service: Amazon EMR
Answer:
Amazon Elastic MapReduce (EMR) is a managed big data processing service that simplifies the process of running large-scale data processing frameworks such as Apache Hadoop, Apache Spark, and Presto. An EMR cluster is a collection of Amazon Elastic Compute Cloud (EC2) instances that work together to process large datasets. The components of an EMR cluster and how they work together are described below:
Master Node: The master node is the central control node of the EMR cluster. It is responsible for coordinating all the activities of the cluster, such as scheduling tasks, managing resources, and monitoring the overall health of the cluster. The master node runs the EMR web console, which can be used to monitor and manage the cluster.
Core Nodes: The core nodes are responsible for processing the data. They are the workhorses of the cluster, and they execute the data processing tasks. The core nodes typically run Hadoop Distributed File System (HDFS) and YARN (Yet Another Resource Negotiator) daemons, which enable the distributed processing of data.
Task Nodes: The task nodes are used to process short-lived and bursty workloads. They are used to perform additional processing capacity when needed. Task nodes are not required for an EMR cluster, but they can be added to increase the processing power of the cluster.
Hadoop Distributed File System (HDFS): HDFS is a distributed file system that is used to store and manage large datasets. HDFS is responsible for replicating data across the EMR cluster, ensuring that the data is always available even if some nodes fail.
Yet Another Resource Negotiator (YARN): YARN is a resource manager that manages the allocation of resources to applications running on the cluster. It ensures that the applications have access to the resources they need to execute their tasks.
Spark: Spark is a distributed data processing engine that can be used with EMR. It provides a fast and flexible processing engine for large-scale data processing. Spark can be used to perform tasks such as data filtering, sorting, aggregation, and machine learning.
In an EMR cluster, these components work together to process large-scale data sets. The master node coordinates the activities of the cluster, while the core nodes process the data using HDFS and YARN. Task nodes can be added to increase processing power, and Spark can be used to perform data processing tasks. The result is a scalable and fault-tolerant system that can handle large volumes of data
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