AWS EMR

• What is Amazon EMR?
• The Architecture of AWS EMR
• Amazon EMR Deployment Options
• Features of AWS EMR
• Components of AWS EMR
• How does Amazon EMR work?
• Benefits and Limitations
• Conclusion

What is Amazon EMR?

Amazon EMR is a massive data processing and analysis service provided by Amazon web service (AWS). EMR provides the supervised architecture for securely running data processing frameworks.

The idea of Amazon EMR was developed by Google in 2004 for indexing web pages and replacing original indexing algorithms.

EMR pricing is entirely dependent on the developer's usage. You may utilize the services on an hourly basis or yearly basis. AWS EMR costs $.015 per hour and $131.40 per year.

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Purpose of Elastic Map Reduce

The main purpose of elastic map reduction is that your company can process and analyze huge data sets in computed environments that are set in distributed structures. This can be done with the help of data analytics software Hadoop.
Most big tech giants use Amazon EMR services because it is reliable, scalable, easy to set up data platforms, and highly secure.

The Architecture of AWS EMR

Amazon EMR architecture consists of several layers; each has its functionalities. Here I'm going to talk about the layers and components of EMR:

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1. Storage layer:

The storage layer offers different file systems that can be used for your clusters. The three types of storage locations include:

Hadoop Distributed File Systems:

HDFS stores data at different instances to ensure no data is lost even if one instance fails to work. It helps in catching intermediate results during map reduction processing.

EMR File Systems:

Using EMRFS, Amazon EMR extends Hadoop to access data stored in Amazon S3 directly. Amazon S3 stores input and output data, whereas HDFS stores the intermediate results.

Local File Systems:

When you create a Hadoop cluster, each node in the cluster comes from an EC2 instance with built-in disk storage called instance store. The instance store is used to store temporary data like buffers, scratch data and other temporary data.

2. Cluster resource management layer:

This layer is responsible for processing multiple data frameworks using YARN (yet another resource negotiator). The yarn component is first used as a resource manager in Apache Hadoop 2.0 that keeps clusters healthy.

3. Data processing framework layer:

This layer is used for processing and analyzing the data. There are various types of frameworks available for different processing needs. You have to choose the framework depending on your use case. The two major data processing frameworks in Amazon EMR are Hadoop Map Reduce and Spark.

Hadoop Map Reduce:

Hadoop map reduction saves developer time by automatically generating a map and reducing programs for distributed computing.

Apache Spark:

Apache Spark is used for processing large data workloads using EMRFS. When you run spark applications on Amazon EMR, you can directly access the data stored in Amazon S3.

4. Applications and Programs:

Amazon EMR is used in many applications such as Hive, Pig, and Spark streaming, and you can interact with these applications using various libraries and languages available in Amazon EMR.

Amazon EMR Deployment Options

Three models of Amazon EMR Deployment Options include:

• Deployment on Amazon EC2 allows customers to choose instances that offer optimal price and performance ratios for specific workloads.
• Deployment on AWS Outposts allows customers to manage and scale Amazon EMR in on-premises environments, just as they would in the cloud.
• Deployment of containers on top of Amazon Elastic Kubernetes Service (EKS).

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Features of AWS EMR

Now it's time to take a glance at some of the features of AWS EMR that, including:

Adaptability:

AWS EMR helps in creating and managing large data platforms and apps. Easy provision, controlled scaling, and cluster reconfiguration are some of the characteristics of Amazon EMR.

Flexibility:

• Another feature is its flexibility in using several data stores like Amazon S3, Hadoop distributed file system (HDFS), and Amazon DynamoDB.

Tools for big data:

• Amazon EMR supports Hadoop tools like Apache Spark, Presto, and Apache Hive for data processing and pipeline development.

Elasticity:

• AWS EMR is highly elastic, as you can add or remove the capacity depending on your processing requirements. For example, if bulk processing occurs during the day and less at night, you need 500 instances during the day and 100 instances at night.

Data access control:

• Amazon EMR uses EC2 instances to enforce data access control when users call other AWS services.

Components of AWS EMR

Amazon EMR enables fine-grained access control with lake formation using the following three components that include:

Proxy agent:

• Proxy agents receive SAML requests, translate the claims to temporary credentials, and store the credentials in the secret agent based on Apache Knox.

Secret agent:

• The secret agent stores and distributes other EMR components securely. The secrets include encryption keys, Kerberos tickets, and user credentials.

Record server:

• The recording server receives requests to access the data. It obtains data from Amazon S3 and returns the column-level data to the user.

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How does Amazon EMR work?

In Amazon EMR, you can submit your work to a cluster by terminating a cluster or assigning steps to a cluster through the EMR interface.
When you need to process your data in Amazon EMR, the data is first saved as files such as Amazon S3 or HDFS. The data is moved from one stage to the next, and the end data is written in the Amazon S3 bucket.

The following procedure is followed when you run the data:

• A request is filed before running the data.
• All the step states are set to PENDING.
• The state of the sequence changes to RUNNING when the first step begins, and the remaining stages are shown as PENDING.
• When the first step is completed, the sequence state switches to the COMPLETED stage.
• The next step in the series begins, and the status changes to RUNNING and enters a COMPLETED state when the first step is finished.
• The process is repeated until all the data processing is finished.

When to use AWS EMR?

You can use AWS EMR for the following reasons:

• Large data processing frameworks.
• When there is no need for a cluster 24×7.
• When elasticity is important
• when you want to save money.
• When you want to perform storage and computing separately.

Understanding your AWS Costs

Amazon EMR cost is incomprehensible as it depends entirely on the number of clusters you are running. Amazon charges on an hourly basis. For example, a 5-node cluster running for 5 hours is the same as a 25-node cluster running for 1 hour. The hourly rate varies on the instance type used. The hourly price ranges from $0.011/hour to $0.27 per hour.

Benefits and Limitations

Benefits:

• Amazon EMR is cost-effective as it eliminates the need to purchase physical servers, and the pricing depends on the number of EC2 instances you deploy.
• High scalable and flexible, you can add or subside the instances depending on your peak workloads.
• Reliable in nature as it automatically replaces the instance when a failure occurs.
• EMR uses other AWS services like Amazon VPC and Amazon EC2 key pairs for securing users' data.

Limitations:

• Beginners find it hard to understand the AWS EMR interface at once. They need to take support from certified personnel to understand Amazon EMR services.
• Users cannot use Amazon EMR services to analyze data stored in other cloud storage platforms.

EMR pricing

EMR cost is incomprehensible as it depends entirely on the number of clusters you are running. The hourly rate varies on the instance type used. The hourly price ranges from $0.011/hour to $0.27 per hour.

Conclusion

We hope our article on "What is AWS EMR" has given the answer you are searching for. This article clearly explained in detail about architecture and services of Amazon EMR. So, here is the end of our writing.

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