AWS EMR is one of the most popular clouds and big data-based platforms that provides a supervised architecture for easily, cost-effectively, and securely running data processing frameworks.
It is used for processing large volumes of data with open source technologies including Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto.
In this AWS EMR blog, we’ll look into what exactly Amazon Elastic MapReduce is and how it works along with many other things. Here are the topics we are going to discuss today.
For a better understanding of the concepts, watch this video on AWS EMR.
Introduction to Amazon Elastic MapReduce
Let’s start this blog by answering a simple question – What is Amazon EMR?
The full form of AWS EMR is Amazon Web Services Elastic MapReduce. EMR is a massive data processing and analysis service from AWS.
Elastic MapReduce provides a simple and comprehensible solution to handle the processing of big data sets. Users may set up clusters with such completely integrated analytics and data pipelining stacks within minutes of using AWS EMR.
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EMR has a remarkable pricing list that appeals to businesses and the wider public. You may utilize it only over an hour base and the number of units in your clusters because it has an on-demand charging option.
You will pay a per-second cost for each second we utilize, with a minimum charge of one minute. AWS EMR Pricing starts at $.015 per hour and $131.40 per year with a one-minute minimum usage.
Wondering why we use AWS EMR? Read further.
Purpose of Elastic MapReduce
We frequently run into a basic challenge wherein we can’t assign all of the cluster’s resources to any applications; AWS EMR addresses this dilemma. It allocates the required resources depending on the amount of the data and the individual user requirement. We may also alter it because it is highly elastic.
Architecture of AWS EMR
Now, let’s have a look at the EMR architecture. The AWS EMR service architecture is made up of multiple layers, each offers clusters with specific features and functions. This section gives an outline of the layers and the elements that make them up.
The following are the four core layers of AWS EMR architecture.
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The storage layer contains the various system files which a cluster uses. There are a variety of storage choices available, as shown below.
- Hadoop Distributed File System (HDFS): It is a Hadoop file system that is distributed and scalable. HDFS shares the data it holds among cluster nodes to guarantee that information is not lost if one of them dies. When you stop a cluster, the temporary storage is recovered.
- EMR File System (EMRFS): Amazon EMR enhances Hadoop by allowing users to access data stored in Amazon S3, as though it were a file system similar to HDFS. The EMR File System (EMRFS) can also be used to store data using either HDFS or Amazon’s S3.
- Local file system: A locally attached disc is referred to as a local file system. Every node in a Hadoop cluster is built using an Ec2 Instances of Amazon that has a preset chunk of pre-attached disc storage. Data on instance store volumes is only retained for the duration of the Amazon EC2 instance’s lifespan.
Cluster Resource Management
Then comes the next layer, Cluster Resource Management. This layer is in charge of cluster resource management and data processing scheduling tasks.
- YARN: It is a feature developed in Apache Hadoop 2.0 to remotely handle cluster resources of various data-processing frameworks, and is used by default in AWS EMR. On the other hand, other frameworks and apps available in AWS EMR, do not employ YARN as a resource manager.
- Agent: Every node in the EMR cluster has an agent that manages YARN elements, monitors cluster health, and interacts with EMR.
Data Processing Frameworks
The third layer of the AWS architecture is data processing frameworks. It is an engine that processes and analyses data.
- Hadoop MapReduce: It is a fully accessible high-performance computing programming methodology.
- Apache Spark: It is a programming paradigm and clustering framework for addressing large data applications.
Applications and Programs
The fourth layer contains the applications and programs which aid in the processing and management of big data sets, such as HIVE, PIG, streaming libraries, and machine learning algorithms.
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Features of AMR EMR
Moving on, it’s time to see some features of AWS EMR:
AWS EMR makes it easier to create and manage large data platforms and apps. Easy provision, controlled scaling, and cluster reconfiguration are among the EMR characteristics, as is EMR Studio for cohesive development.
AWS EMR allows you to supply as much capacity as you require fast and efficiently, and to add multiple capacities manually or automatically. This is especially beneficial if your processing requirements are changeable or unexpected.
AWS EMR is highly flexible. You may use several data stores with AWS EMR, including Amazon S3, Hadoop Distributed File System (HDFS), and Amazon DynamoDB.
4. Tools for Big Data
Apache Spark, Apache Hive, Presto, and Apache HBase are among the Hadoop technologies supported by AWS EMR. Data scientists use EMR to execute deep learning and its technologies like TensorFlow and Apache MXNet, as well as scenario tools and frameworks, utilizing bootstrap operations.
5. Data Access
When calling other Amazon Web Services, AWS EMR application processes utilize the EC2 instance account by default. EMR provides three ways for managing user access to Amazon S3 data in multi-tenant clusters.
Before going to the working process of AWS EMR, let us walk you through a few components present in AWS EMR.
Components of AWS EMR
The AWS EMR service consists of a few components as follows:
Clusters: Clusters are groups of EC2 instances. You can build two sorts of clusters which are temporary clusters and long-running clusters.
- A temporary cluster that ends when the steps are completed
- A permanent cluster is a long-running cluster that keeps operating unless you explicitly stop it.
Node: Every EC2 instance in a cluster is referred to as a node. The node type refers to the role that each node plays inside the cluster. The different sorts of nodes are the Master node, Core node, and Task node.
- Every cluster has a master node that oversees data and job distribution among all the other nodes. The master node keeps track of project status and oversees the cluster’s stability. Automated fallback is not supported. Just the master node is supported in a single-node cluster.
- The Core Node is in charge of performing the job and storing the data in the cluster’s HDFS. All processing is handled by the core Node, and the data is then written to the chosen HDFS location.
- As the Task Node is optional, it simply has the job of completing the task. The data is not stored in HDFS in this case.
How does AWS EMR work? That’s what we are going to discuss next.
Working of AWS EMR
In Amazon EMR, you can define the work that needs to be completed in a variety of ways when you run a cluster.
To submit your work to a cluster, you can use ways such as to terminate a cluster when a task is completed or to submit steps to a long-running cluster via the EMR interface or CLI.
We can also use a method of connecting the master node to other nodes through a secure connection and use the interfaces and tools provided for the software that runs straight on your cluster. Using this method, you can submit work and connect with the software deployed in your AWS EMR cluster instantly.
The cluster distribution in EMR is depicted in the diagram below. Let’s take a closer look at that:
When you use AWS EMR to process data, the data is saved as files underneath your file system of choices, such as Amazon S3 or HDFS. In the process, this data moves from one stage to the next. (EMR clusters can accept one or more ordered steps.)
The resulting data is written in a specified place, such as an Amazon S3 bucket, in the last step.
To run the data, the steps are performed in the following order:
1. To begin the procedural processes, a request is filed.
2. All steps’ states are set to PENDING.
3. The state of the sequence changes to RUNNING when the first step begins. The other stages are still shown as PENDING.
4. When the first step is finished, the status of the step switches to COMPLETED.
5. The next step in the series begins, and the status of the sequence is changed to RUNNING. Its status switches to COMPLETED after it’s finished.
6. This procedure is repeated for each stage until they are all finished and the processing is finished.
Benefits of AWS EMR
Now, let’s take a look at the advantages of AWS EMR.
The following are the benefits of using AWS EMR.
- Reasonable Pricing: The cost of AWS EMR is determined by the instance type and number of Ec2 Resources you use, as well as the region in which your cluster is launched. The pricing is reasonable. By using Reserved Instances and Spot Instances we can help you save even more money.
- Monitoring and Deployment: We have adequate monitoring tools for all systems operating on EMR clusters, keeping the analysis process visible and simple. It also has an auto-deployment capability, which automatically configures and deploys the applications.
- Scalable: As your computing demands vary, EMR allows you to scale your cluster down and up. When peak workloads decrease, it allows you to expand your cluster and add instances for peak workloads and remove ones to reduce expenses.
- Secure and Reliable: To manage inbound and outgoing traffic, AWS EMR has a fantastic Security group.
It uses other AWS services, such as IAM and Amazon VPC, and features such as Amazon EC2 key pairs which makes it more secure since it creates multiple permissions to access the data and that keeps data safe.
AWS EMR is reliable too. In the event that a node in your cluster fails, EMR immediately stops and substitutes the instance. So, we only lose a minimum amount of data.
- Interaction with EMR: We can interact with EMR through various ways such as Console, AWS Command Line Interface (AWS CLI), Software Development Kit (SDK), Web Service API.
- Integration with Amazon Web Services: EMR interacts with other AWS services easily to offer networking, storage, security, and other features and functionality for clusters.
Difference Between AWS EMR And EC2
What is the difference between AWS EMR and EC2? This is a common query for most of us. So, let’s answer this today.
Both AWS Elastic MapReduce and Elastic Compute Cloud are the services offered by AWS. Elastic Compute Cloud is a service designed based on cloud that provides clients with a variety of computer instances, often known as virtual machines.
Whereas, AWS EMR is a service designed based on big data. Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto computing clusters are the services provided by EMR.
Hence, AWS EC2 is a low-level service compared to EMR because EC2 is just servers executing applications and operating systems, but AWS EMR now has the software pre-installed and configured. This speeds up the setup process and eliminates the need for all of the maintenance and patching that comes with a manual installation.
Hence, we covered all the topics related to AWS EMR. We have looked at Amazon EMR, which aids in the processing of large amounts of data. We talked about AWS EMR’s architecture, components, and features.
Along the way, we also learned about Amazon Elastic Mapreduce’s many features and benefits. If you still have concerns, feel free to discuss them with us.
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