Introduction to AWS EMR
Amazon EMR is a big data platform currently leading in cloud-native platforms for big data with its features like processing vast amounts of data quickly and at an cost-effective scale and all these by using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi and Presto, with auto-scaling capability of Amazon EC2 and storage scalability of Amazon S3, EMR gives the flexibility to run short-lived clusters that can automatically scale to meet demand task, or for long-running highly available clusters.
AWS EMR provides many functionalities that makes thing easier for us, some of the technologies are:
- Amazon EC2
- Amazon RDS
- Amazon S3
- Amazon CloudFront
- Amazon Auto Scaling
- Amazon Lambda
- Amazon Redshift
- Amazon Elastic MapReduce (EMR)
One of the major services provided by AWS EMR and we are going to deal with is Amazon EMR.
EMR commonly called Elastic Map Reduce comes over with an easy and approachable way to deal with the processing of larger chunks of data. Imagine a big data scenario where we have a huge amount of data and we are performing a set of operations over them, say a Map-Reduce job is running, one of the major issue the Bigdata application faces is the tuning of the program, we often find it difficult to fine-tune our program in such a way all the resource allocated is consumed properly. Due to this above tuning factor, the time taken for processing increases gradually. Elastic Map Reduce the service by Amazon, is a web service that provides a framework that manages all these necessary features needed for Big data processing in a cost-effective, fast, and secure manner. From cluster creation to data distribution over various instances all these things are easily managed under Amazon EMR. The services here are on-demand means we can control the numbers based on the data we have that makes if cost-efficient and scalable.
Reasons for Using AWS EMR
So Why Using AMR what makes it better from others. We often encounter a very basic problem where we are unable to allocate all the resources available over the cluster to any application, AMAZON EMR taking care of these problems and based on the size of data and the demand of application it allocates the necessary resource. Also, being Elastic in nature we can change it accordingly. EMR has huge application support be it Hadoop, Spark, HBase that makes it easier for Data processing. It supports various ETL operations quickly and cost-effectively. It Can also be used over for MLIB in Spark. We can perform various machine learning algorithms inside it. Be it Batch data or Real-Time Streaming of Data EMR is capable to organize and process both types of Data.
Working of AWS EMR
Now let’s see this diagram of the Amazon EMR cluster and will try to understand how actually it Works:
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The following diagram depicts the cluster distribution of inside EMR. Let’s check that over detail:
1. The Clusters are the central component in the Amazon EMR architecture. They are a collection of EC2 Instances called Nodes. Each node has their specific roles within the cluster termed as Node type and based on their roles we can classify them in 3 types:
- Master Node
- Core Node
- Task Node
2. The Master Node as the name suggests is the master that is responsible for managing the cluster, running the components and distribution of data over the nodes for processing. It just keeps tracks whether everything is properly managed and running fine and works on in the case of failure.
3. The Core Node has the responsibility of running the task and store the data in HDFS in the cluster. All the processing parts are handled by the core Node and the data after that processing is put to the desired HDFS location.
4. The Task Node being optional only has the job to run the task this doesn’t store the data in HDFS.
5. Whenever after submitting a job, we have several methods to choose how the works need to be completed. Being it from termination of the cluster after job completion to a long-running cluster using EMR console and CLI to submit steps we have all the privilege to do so.
6. We can directly Run the Job on the EMR by connecting it with the master node through the interfaces and tools available that run jobs directly on the cluster.
7. We can also run our data in various steps with the help of EMR, all we have to do is submit one or more ordered steps in the EMR cluster. The Data is stored as a file and is processed in a sequential manner. Starting it from “Pending state to Completed state” we can trace the processing steps and find the errors also being it from ‘Failed to be Canceled’ all these steps can be easily traced back to this.
8. Once all the instance is terminated the completed state for the cluster is achieved.
Architecture for AWS EMR
The architecture of EMR introduces itself starting from the storage part to the Application part.
- The very first layer comes with the storage layer which includes different file systems used with our cluster. Be It from HDFS to EMRFS to local file system these all are used for data storage over the entire application. Caching of the intermediate results during MapReduce processing can be achieved with the help of these technologies that come with EMR.
- The Second layer comes with the Resource Management for the cluster, this layer is responsible for resource management for the clusters and nodes over the application. This basically helps as the management tools that helps to evenly distribute the data over cluster and proper managing. The Default resource Management tool that EMR uses is YARN that was introduced in Apache Hadoop 2.0. It centrally manages the resources for multiple data processing frameworks. It takes care of all the information that is needed for the cluster well-running being it from node health to resource distribution with memory management.
- The Third layer comes with the Data processing Framework, this layer is responsible for the analysis and the processing of data. there are many frameworks supported by EMR that plays an important role in parallel and efficient data processing. Some of the framework it supports and we are aware of is APACHE HADOOP, SPARK, SPARK STREAMING, etc.
- The Fourth layer coms with the Application and programs such as HIVE, PIG, streaming library, ML Algorithms that are helpful for processing and managing large data sets.
Advantages of AWS EMR
Let us now check some of the benefits of using EMR:
- High Speed: Since all the resources are utilized properly the Processing time for the query is comparatively faster than the other data processing tools have a much clear picture.
- Bulk Data Processing: Be larger the data size EMR has the capability for processing of huge amount of data in ample time.
- Minimal Data Loss: Since data are distributed over the cluster and processed parallelly over the network, there is a minimum chance for data loss and well, the accuracy rate for the processed data is better.
- Cost-Effective: Being cost-effective it is cheaper than any other alternative available that makes it strong over the industry usage. Since the pricing is less we can accommodate over large amounts of data and can process them within budget.
- AWS Integrated: It is integrated with all the services of AWS that makes easy availability under a roof so the security, storage, networking everything is integrated in one place.
- Security: It comes with an amazing Security group to control the inbound and outbound traffic also the use of IAM Roles makes it more secure as it comes up with various permissions that make data secure.
- Monitoring and deployment: we have proper monitoring tools for all the application that is running over EMR clusters that makes it transparent and easy for analysis portion also it comes with an auto-deployment feature where the application is configured and deployed automatically.
There are a lot more advantages to having EMR as a better choice other cluster computation method.
AWS EMR Pricing
EMR comes with an amazing price listing that attracts developers or the market towards it. Since it comes with an on-demand pricing feature we can use it just over an hourly basis and number of nodes in our cluster. We can pay for a per-second rate for every second we use with one minute as a minimum. We also can choose our instances to be used as Reserved Instances or Spot Instances, the spot being much cost saving.
We can calculate the total bill over a simple monthly calculator from the below link:-
https://calculator.s3.amazonaws.com/index.html#s=EMR
For more details over the exact pricing details you can refer the doc below by Amazon:-
https://aws.amazon.com/emr/pricing/
Conclusion
From the above article, we saw how EMR can be used for the fair processing of big data with all the resources being utilized conventionally.
Having EMR solves our basic problem of data processing and reduces much the processing time by a good number, being cost-effective it is easy and convenient to use.
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