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 a 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 the 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 make things 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 will 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 issues 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. Furthermore, the services here are on-demand, which means we can control the numbers based on our data, making it cost-efficient and scalable.
Reasons for Using AWS EMR
So Why Using AMR? What makes it better than others. First, we often encounter a fundamental problem where we cannot allocate all the resources available over the cluster to any application; AMAZON EMR takes care of these problems. Based on the size of data and the demand of the application, it allocates the necessary resource. Also, being Elastic in nature, we can change it accordingly. Second, EMR has huge application support, be it Hadoop, Spark, HBase, making 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 can 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:
The following diagram depicts the cluster distribution 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 its specific roles within the cluster termed as Node type, and based on their roles; we can classify them into 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 distributing of data over the nodes for processing. It just keeps track of whether everything is properly managed and running fine and works on in the case of failure.
3. The Core Node is responsible for running the task and storing the data in HDFS in the cluster. In addition, the core Node handles all the processing parts, 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 submitting a job, we have several methods to choose how to complete the work. Being it from the cluster’s termination 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 sequentially. Starting it from “Pending state to Completed state”, we can trace the processing steps and find the errors 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 first layer comes with the storage layer, including different file systems used with our cluster. From HDFS to EMRFS to local file systems, these all are used for data storage over the entire application. Caching of the intermediate results during MapReduce processing can be achieved with these technologies that come with EMR.
- The Second layer comes with 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 distribute the data over the 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 needed for the cluster well-running, from node health to resource distribution with memory management.
- The Third layer comes with the Data processing Framework; this layer is responsible for analysing and processing 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 comes with the Application and programs such as HIVE, PIG, streaming library, ML Algorithms that help process and manage 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 query processing time is comparatively faster than the other data processing tools that have a much clearer picture.
- Bulk Data Processing: Be larger the data size EMR can process huge amounts 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, making it strong over industry usage. Since the pricing is less, we can accommodate large amounts of data and 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 into one place.
- Security: It comes with an amazing Security group to control the inbound and outbound traffic. Also, IAM Roles’ use 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 applications running over EMR clusters, making it transparent and easy for the analysis portion. It also 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 than other cluster computation methods.
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 the 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:-
For more details over the exact pricing details, you can refer to the doc below by Amazon:-
From the above article, we saw how EMR could be used to fair data processing with all the resources being utilized conventionally.
Having EMR solves our basic data processing problem and reduces the processing time by a good number, being cost-effective. In addition, it is easy and convenient to use.
This has been a guide to AWS EMR. Here we discuss an introduction to AWS EMR and its Working and Architecture and Advantages. You can also go through our other suggested articles to learn more –