The full form of AWS EMR is Amazon Web Service Elastic Map Reduce, a cluster-managed platform used for processing and analyzing large amounts of data using open-source tools like Apache Spark, presto Apache Hive, and Apache Hadoop. AWS EMR allows multiple clusters to access the same datasets at once. With the application of EMR, we can reduce the cost of physical infrastructure as EMR removes the need to purchase, maintain, and house physical server infrastructure. It also saves time for data configuration and provisioning onsite servers for big data computational tasks. Amazon EMR is used in various applications like log analysis, financial analysis, bioinformatics, data warehousing and web indexing. Some big tech giants using Amazon EMR services are Netflix, Kellogg, Airbnb, Pfizer, Twitch, Twitter, Nordstrom, and Epic Games. Here in this blog, I'm going to discuss the purpose of EMR, the architecture of Amazon EMR, its components, its benefits and limitations. So, without a wait, let's dive into the topic together.
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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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.
Amazon EMR architecture consists of several layers; each has its functionalities. Here I'm going to talk about the layers and components of EMR:
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.
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.
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.
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.
Three models of Amazon EMR Deployment Options include:
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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:
Tools for big data:
Elasticity:
Data access control:
Amazon EMR enables fine-grained access control with lake formation using the following three components that include:
Proxy agent:
Secret agent:
Record server:
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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:
You can use AWS EMR for the following reasons:
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:
Limitations:
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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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.
You can deploy your workload to Amazon EMR using Amazon EC2, on-premises AWS Outposts, and Amazon Elastic Kubernetes Service (EKS). You can manage your workload within the EMR and coordinate them using Amazon-managed workflows for Apache Airflow.
You must opt for Amazon EMR as it processes a large amount of data quickly, alerts the user when any changes occur in their infrastructure and is extremely cost-effective.