AWS EMR Architecture 101—Core Features and Components (2024)

AWS Elastic MapReduce (EMR) is Amazon’s managed platform for scalable big data processing, designed to simplify the deployment of popular big data frameworks like Apache Spark, Apache Hadoop, Hive, Presto, Flink and more. EMR offers four distinct deployment options to meet varying operational requirements: EMR on AWS EC2 (AWS Elastic Compute Cloud) for traditional cluster management with full control over instance configurations; EMR Serverless for fully managed operation without cluster management, optimized for Spark and Hive jobs; EMR on AWS EKS (Elastic Kubernetes Service) for containerized workloads in Kubernetes environments; and EMR on Amazon Outposts for on-premises deployments. At its very core, EMR integrates with the Amazon EMR File System (EMRFS), enabling direct read/write access to AWS S3, supporting strong consistency, server-side encryption, and role-based access, making EMR a flexible and cost-efficient solution for variable-demand workloads.

In this article, we will cover everything you need to know about AWS EMR and peel back the layers and components of AWS EMR architecture—exploring its origins, features, benefits, and the reasons it has become a widely adopted tool worldwide.

What is AWS EMR? 

AWS EMR (AWS Elastic MapReduce) is the industry-leading cloud-based managed big data processing platform offered by Amazon Web Services (AWS). EMR is designed to simplify and streamline big data processing in the cloud, handing you the power to crunch massive volumes of amounts of data quickly and easily.

AWS EMR was first launched in 2009 as a managed service to simplify big data processing, initially focusing on Hadoop-based workloads. Over the years, EMR expanded to support a wide range of data processing frameworks and use cases beyond Hadoop.

It now supports rapid, scalable analysis and processing of vast datasets using popular open source data processing frameworks like Apache Hadoop, Apache Spark, Apache Hive, Presto and more.

A key advantage of AWS EMR is its ability to decouple storage and compute resources. EMR provides flexible storage options:

• HDFS: Distributed storage across cluster nodes
• AWS S3: Persistent object storage (through EMRFS - EMR File System)
• Instance Store: Local storage for temporary data

EMR File System (EMRFS) is an implementation of HDFS that allows EMR clusters to efficiently read and write data directly to S3.

AWS EMR also provides four primary deployment modes, each offering specific advantages for different use cases:

• EMR on EC2 — The traditional, customizable setup that allows users to configure instance types and cluster sizes for fully managed clusters.
• EMR on EKS — Integrates EMR with Elastic Kubernetes Service (EKS) for containerized workloads, allowing data engineers to leverage Kubernetes for cluster management and scaling.
• EMR Serverless — A serverless option where AWS fully manages the infrastructure, automatically scaling resources based on job needs, eliminating the need to manage clusters directly.
• EMR on Outposts — For on-premises deployment, this version extends EMR capabilities to local AWS Outposts hardware, enabling low-latency and data residency requirements in hybrid environments.

The main goal of AWS EMR is to give organizations a simple way to run big data applications without the hassle of managing the underlying infrastructure.

Features of AWS EMR

AWS EMR (AWS Elastic MapReduce) offers a comprehensive suite of features designed to facilitate efficient big data processing and analytics. Here are some key features of AWS EMR: 

1) Elastic Scalability

AWS EMR provides elastic scalability, allowing users to dynamically adjust their processing capacity based on workload requirements. This is achieved through integration with AWS EC2 (AWS Elastic Compute Cloud) for launching virtual servers and AWS EKS (Elastic Kubernetes Service) for containerized applications. Users can scale their clusters up or down seamlessly, accommodating fluctuating data processing needs without manual intervention.

2) Single-click High Availability

AWS EMR ensures high availability by deploying clusters across multiple Availability Zones within an AWS region. This architecture automatically replicates data and workloads, allowing the service to recover from hardware failures or node issues without losing data. Users can configure their clusters for automatic retries of failed tasks, enhancing fault tolerance and reliability.

3) Data Access Control

AWS EMR integrates with AWS Identity and Access Management (IAM) to provide robust data access control. This feature allows administrators to define fine-grained permissions for users and services accessing EMR resources. Policies can specify who can create, modify, or delete clusters and access processed data, ensuring compliance with security standards.

4) Support for Multiple Data Processing Frameworks

AWS EMR supports a range of open source data processing frameworks, allowing users to select the most suitable one for specific workloads, including batch processing, real-time analytics, and machine learning.

1. Apache Spark: For in-memory processing and stream processing
2. Apache Hadoop: For distributed storage (HDFS) and processing (MapReduce)
3. Apache Hive: For SQL-like queries on large datasets
4. Apache HBase: For NoSQL workloads
5. Apache Flink: For stream processing
6. Presto: For interactive querying

5) Integration with Flexible Data Stores

AWS EMR integrates seamlessly with a wide range of data storage solutions, allowing for flexible data processing and storage management.

• Amazon S3: A scalable and durable storage service that works with EMRFS (AWS Elastic MapReduce File System) for accessing data stored on Amazon S3. EMR can process large datasets directly from S3, making it a key storage option for big data workloads.
• Amazon DynamoDB: A fully managed NoSQL database service that integrates directly with EMR, enabling efficient data transfer between DynamoDB and other storage solutions like S3​.
• Hadoop Distributed File System (HDFS): EMR supports HDFS as part of its default configuration, allowing it to store data locally within the cluster nodes for fast access during processing.
• Other AWS Data Stores: EMR also supports integration with services such as Amazon Redshift (data warehouse), Amazon Glacier (archival storage), and Amazon RDS (relational databases), allowing for diverse storage options across different data types and use cases.

Users can easily choose between these solutions to optimize their storage strategy based on cost, performance, and data access patterns.

6) Integration with AWS Services

AWS EMR is built to work with other AWS services, which gives it more capabilities. Some notable integrations include:

• Amazon S3
• Amazon DynamoDB
• Amazon CloudWatch
• AWS Glue

and more!

7) Real-Time Data Processing Capabilities

AWS EMR supports real-time data processing. This lets organizations analyze streaming data the moment it arrives. It's especially important for applications that need immediate insights, like fraud detection in finance or real-time user analytics in e-commerce. EMR can be paired with tools like Apache Kafka and Apache Flink to efficiently handle streams of data.

8) Cluster Resource Management

AWS EMR utilizes YARN (Yet Another Resource Negotiator) for effective cluster resource management. YARN dynamically allocates resources among different applications running on the cluster, optimizing performance by ensuring that each application receives the necessary resources based on its workload demands.

9) Data Security Features

Security is a priority in AWS EMR, which includes several features such as:

• Integration with IAM for access control.
• Support for server-side encryption of data at rest and in transit.
• Compliance with various regulatory standards (e.g., HIPAA, PCI DSS).
• Deployment within an Amazon Virtual Private Cloud (VPC) for network isolation.

10) Interactive Developer Environments

AWS EMR provides interactive developer environments through tools like:

• EMR Notebooks: Jupyter-based notebooks that allow users to run code interactively on EMR clusters.
• EMR Studio: An integrated development environment that simplifies the process of building and managing big data applications.

Using these interactive environments, users can increase productivity, explore data, visualize results, and collaborate more effectively.

What Is AWS EMR Used For?

AWS EMR (AWS Elastic MapReduce) is versatile, catering to a range of use cases:

1) Batch ETL Processes

AWS EMR is powerful enough to perform batch ETL (Extract Transform Load) workloads by ingesting and transforming large datasets. You can use it to:

• Process data from sources like relational databases or log files.
• Transform data using frameworks like Apache Spark or Hadoop.
• Store processed data in Amazon S3 or load it into data warehouses like AWS Redshift.

EMR’s scalability and pay-as-you-go pricing make it efficient for intermittent, high-volume ETL workloads.

2) Machine Learning Workflows

You can use AWS EMR to preprocess large datasets for machine learning (ML) models. Key capabilities include:

• Performing exploratory data analysis and feature engineering.
• Training models using tools like Apache Spark MLlib and TensorFlow.
• Scaling resources to train complex models without infrastructure limitations.

3) Real-Time Analytics and Stream Processing

For real-time processing, EMR integrates with frameworks like Apache Flink and Apache Kafka, enabling continuous data processing from streaming sources. This feature is especially relevant for:

• Streaming analytics for IoT applications.
• Event detection and real-time decision-making in finance.
• Continuous processing of data from streaming sources.

4) Interactive Data Analysis

EMR facilitates interactive data analysis, allowing users to run queries on large datasets quickly via EMR Notebooks (Jupyter-based), making it easier for data analysts to visualize and manipulate data. This setup is particularly advantageous for ad hoc data analysis where quick, iterative exploration of data is necessary, especially in fields like business intelligence.

5) Clickstream Analysis

AWS EMR is commonly used for analyzing clickstream data, helping businesses:

• Track user interactions on digital platforms.
• Optimize website performance.
• Personalize user experiences.

Its scalability enables handling clickstream data from high-traffic platforms.

6) Data Warehousing and Log Analysis

AWS EMR processes raw log files and prepares them for storage or analysis. Examples include:

• Structuring log data for storage in data warehouses like Redshift.
• Gaining insights from historical logs to support decision-making.

7) Financial Analysis

AWS EMR is used in the finance sector for difficult financial analysis jobs that demand efficient processing of huge datasets. EMR can be used by financial organizations to analyze transaction data, evaluate risk models, and generate regulatory reports. These businesses can effectively handle variable demands due to their ability to dynamically scale resources.

How AWS EMR Works—A Look at AWS EMR Architecture

 AWS EMR is structured into layers—each with specific responsibilities in handling data storage, resource management, processing, and application interactions. These layers are organized to support the complex, distributed computing needs of big data workloads on EMR clusters.

Below is an in-depth elaboration on each layer of the AWS EMR architecture.

➥ Cluster Composition in AWS EMR

Clusters in AWS EMR are groups of Amazon EC2 (Elastic Compute Cloud) instances called nodes, organized into roles to facilitate distributed data processing:

Primary Node – Manages cluster operations, including job distribution and monitoring. The cluster always has one primary node responsible for overseeing task scheduling and data distribution.

Core Nodes – Execute tasks and handle storage within the Hadoop Distributed File System (HDFS) or other file systems. They are essential for multi-node clusters.

Task Nodes – Task-only nodes that handle processing without storing data. These nodes are often used in transient Spot Instances to optimize costs.

➥ Storage Layer

Storage layer in AWS EMR is crucial for managing data input, output, and intermediate results during processing. It supports multiple storage options tailored to different processing needs:

Hadoop Distributed File System (HDFS) – HDFS is designed for large-scale data storage and parallel processing. It distributes data over several instances and replicates it for fault tolerance. This ephemeral storage option—available on core nodes—is perfect for caching intermediate processing data. However, data in HDFS does not persist beyond the cluster's lifecycle.

EMR File System (EMRFS) – EMRFS extends Hadoop's flexibility by allowing seamless integration with Amazon S3, which acts as a persistent storage solution. Unlike HDFS, data in Amazon S3 persists even after the cluster ends, providing long-term storage. EMRFS is especially valuable for input, output, and data archiving, allowing EMR to scale with Amazon S3’s vast storage capabilities.

Local File System (LFS)

Each EC2 (AWS Elastic Compute Cloud) instance in an EMR cluster has a dedicated instance store, which is locally attached storage. This is typically used for temporary data storage during computations, particularly when persistent storage isn’t necessary. Local file system data does not persist beyond the lifecycle of the EC2 instance.

➥ Cluster Resource Management Layer

Cluster resource management layer governs the allocation and management of resources across the EMR cluster, including CPU, memory, and network bandwidth, ensuring efficient task distribution and fault tolerance.

YARN (Yet Another Resource Negotiator) – YARN (Yet Another Resource Negotiator) is the primary resource management system in EMR, enabling dynamic resource allocation and management for multiple data-processing frameworks. YARN separates job management from resource allocation, ensuring better scalability and multi-tenancy. YARN’s configuration on EMR also includes default settings for node labels (such as “CORE” for core nodes), which allow certain processes to only run on core nodes, enhancing job stability.

Spot Instance Integration – AWS EMR frequently leverages Spot Instances for cost efficiency, often assigning them to non-persistent, task-specific roles. Task nodes on Spot Instances may be terminated based on availability, so AWS EMR ensures that critical application master processes run only on core nodes. This reduces job failures, as task nodes can be interrupted without impacting the primary job control.

Cluster Monitoring and Health Management

AWS EMR includes agents on each node that monitor YARN processes and the cluster’s health, with metrics integrated into Amazon CloudWatch. This monitoring setup provides automated recovery in case of node failure, ensuring cluster stability.

➥ Data Processing Frameworks Layer

Data Processing Frameworks layer enables data processing and analysis by supporting various frameworks that cater to diverse workload types—batch, interactive, streaming, and in-memory processing.

Hadoop MapReduce – Hadoop MapReduce is an open source programming model for distributed computing. It simplifies the process of writing parallel distributed applications by handling all of the logic, while you provide the Map and Reduce functions. The Map function generates intermediate key-value pairs, while the Reduce function consolidates these to produce the final result. MapReduce applications like Apache Hive simplify job creation by abstracting MapReduce code through high-level SQL queries.

Apache Spark – Spark is a cluster framework and programming model for processing big data workloads. Like Hadoop MapReduce, Spark is an open-source, distributed processing system but uses directed acyclic graphs for execution plans and in-memory caching for datasets. When you run Spark on AWS EMR, you can use EMRFS to directly access your data in Amazon S3. Spark supports multiple interactive query modules such as SparkSQL.

You've also got other frameworks to choose from, like Apache HBase for storing NoSQL data, and Presto for when you need to run low-latency queries.

➥ Applications and Programs Layer

The Applications and Programs layer supports multiple big data tools and applications that can be executed on the EMR cluster, allowing users to build, test, and deploy data processing workloads.

Applications Supported by EMR – AWS EMR includes support for Apache Hive, Pig, Spark Streaming, and others, offering high-level programming interfaces for various data processing needs. For instance, Spark Streaming handles real-time data processing, while Hive and Pig are designed for batch processing with SQL-like interfaces.

Programming Languages and Interfaces – Developers can interact with EMR applications using a range of languages and APIs. For example, Spark supports Java, Scala, Python, and R, while MapReduce primarily relies on Java. EMR applications can be managed through the EMR console, AWS CLI, or SDKs, providing a flexible interface for running and monitoring jobs.

How AWS EMR Works?

AWS EMR (AWS Elastic MapReduce) uses EC2 (AWS Elastic Compute Cloud) instances to handle compute tasks, running open source frameworks like Apache Spark, Hadoop, Flink, HBase, and Presto. These frameworks enable distributed data processing across large datasets, splitting work into tasks executed in parallel for efficiency.

For storage, AWS EMR integrates with Amazon S3 via EMRFS (EMR File System). This lets you store data in S3 while performing computations without affecting the data storage. The separation of compute and storage reduces costs and offers high scalability. Data stored in S3 remains accessible even after clusters are terminated, which helps manage costs by only charging for active compute resources.

Resource management within EMR is handled by YARN (Yet Another Resource Negotiator). YARN allocates and schedules resources across EC2 (AWS Elastic Compute Cloud) instances, optimizing performance and workload distribution. The cluster typically includes a master node to manage tasks, core nodes to store and process data, and task nodes to handle specific tasks without storing data. This setup allows flexible scaling—more nodes can be added during high demand and removed when the load decreases.

EMR also supports autoscaling, automatically adjusting cluster size based on the workload. This capability works in conjunction with Spot Instances for cost savings, particularly in non-critical jobs. Multiple clusters can run in parallel, accessing the same dataset in S3, enabling you to distribute workloads across teams or applications without conflicts.

Monitoring is handled through Amazon CloudWatch, which tracks metrics, logs, and alarms. CloudWatch helps manage cluster health, and if a node fails, EMR will replace it automatically. CloudTrail logs API calls for auditing and compliance, ensuring you can track all activity in the cluster.

You have flexibility in how to deploy EMR. The traditional EC2 (AWS Elastic Compute Cloud) setup gives you control over instance types and cluster configuration, while EMR on EKS (Elastic Kubernetes Service) allows you to run workloads in Kubernetes environments, suitable for containerized applications. EMR Serverless abstracts away infrastructure management, scaling resources as needed for short-term or unpredictable workloads.

For developers, EMR Studio provides an integrated environment for building, running, and debugging Spark and Hadoop jobs. It supports Python, Scala, and R, and integrates with version control systems like Git, enabling streamlined data exploration and collaboration.

Finally, after processing jobs, you can terminate clusters to stop compute charges, with data persisting in S3. This model makes AWS EMR an effective, cost-efficient solution for big data processing.

Best Tips for Working with AWS EMR

Working with AWS EMR (AWS Elastic MapReduce) can significantly enhance your data processing capabilities. Here are some best tips to help you optimize your experience and manage costs effectively.

1) Use Amazon S3 for Storage

Use Amazon S3 as your primary data store instead of HDFS. S3 is typically cheaper (~ $0.023 per GB per month for standard storage) compared to EBS volumes used with HDFS, which can cost ~$0.08–$0.10 per GB per month. S3 automatically scales with your data, eliminating the need to provision additional nodes for storage, unlike HDFS where you need to add nodes as your data grows.

2) Use EMRFS for accessing data in S3

Use EMR File System (EMRFS) for accessing data in S3. It integrates seamlessly with EMR and provides a reliable way to manage data stored in S3.

3) Compress Your Data

Compress your data to reduce storage needs and network traffic. Use formats like Apache Parquet or ORC that support compression natively. This can lead to significant storage savings.

4) Avoid Small Files

Aim for larger files (ideally over 128 MB). Small files increase the number of LIST requests to S3, which can degrade performance.

5) Columnar Formats

Use columnar formats like Parquet or ORC for better read performance, especially if you often query a subset of columns.

6) Partition Your Data

Organize your data in S3 using partitions based on commonly queried fields. This reduces the amount of data scanned during queries, lowering costs and improving performance.

7) Bucket Your Data

Consider bucketing your data to further optimize query performance. Bucketing organizes data by a range of values, which can improve efficiency during joins and aggregations.

8) Choose the Right Instance Types

Choose instance types based on your workload. For general purposes, consider m5 or m6g instances. For compute-heavy tasks, use c5 instances, and for memory-intensive applications, use r5 instances.

9) Use Graviton2 Instances

Graviton2 instances can reduce costs by up to 30% while improving performance by about 15%. They are suitable for various workloads running on EMR.

10) Use Spot Instances

Use Spot Instances for non-critical workloads. They can offer discounts of up to 90% compared to On-Demand pricing. Be prepared for potential interruptions but remember that many big data workloads can handle this gracefully.

11) Mix Instance Types

Combine On-Demand and Spot Instances in your clusters. This approach allows you to maintain necessary compute capacity while reducing costs.

12) Managed Scaling

Enable EMR Managed Scaling to automatically adjust the number of instances based on workload demands. This helps minimize costs by ensuring you only pay for what you need.

13) Monitor Utilization

Use tools like AWS CloudWatch or Ganglia to monitor cluster utilization metrics such as CPU, memory, and disk usage. Regular monitoring helps identify underutilized resources that can be scaled down to save costs.

14) Right Size Resources

Adjust YARN and Spark memory settings according to your instance specifications. Properly sizing these resources maximizes utilization and improves performance.

15) Evaluate Job Performance

Regularly analyze job execution times and resource usage patterns. This helps you identify bottlenecks and optimize configurations accordingly.

The tips mentioned are just the basic tips. To really get the best out of AWS EMR, you have to try it out and see what works for you. Even so, following these tips can make a big difference—you'll have a better experience with AWS EMR, cut costs, and make your big data processing tasks more efficient.

AWS EMR Pricing Structure

AWS EMR (AWS Elastic MapReduce) pricing varies significantly depending on the underlying service chosen, instance types, and workload requirements. Here are the main pricing models for different EMR configurations:

➥ AWS EMR on AWS EC2 (AWS Elastic Compute Cloud)

When deploying AWS EMR (AWS Elastic MapReduce) on EC2 (AWS Elastic Compute Cloud), users incur charges for both the EMR service and the underlying EC2 instances. Pricing for AWS EMR clusters on AWS EC2 encompasses both AWS EMR and AWS EC2 costs, with additional charges for Amazon Elastic Block Store (Amazon EBS) if volumes are attached. Billing occurs per second, with a one-minute minimum.

• AWS EMR Costs: Based on the resources utilized by EMR clusters.
• AWS EC2 Options: Includes On-Demand, Reserved Instances (one-year or three-year commitments), Savings Plans, and Spot Instances (up to 90% off On-Demand prices). Spot Instances can offer significant savings by utilizing spare EC2 capacity.
• Storage Charges: EBS volume costs add to the total, with details accessible on the EBS pricing page.

Note: The cost varies by instance type (like Accelerated Computing, Compute optimized, GPU instance, General purpose, Memory optimized, Storage optimized) and region.

For example an AWS EMR deployment with one master and two core nodes (c4.2xlarge instances) in the US-East-1 region, On-Demand pricing would apply as follows:

• Master Node:
  • EMR Charges: 1 x $0.105/hour x 730 hours in a month = $76.65
  • EC2 Charges: 1 x $0.398/hour x 730 hours in a month = $290.54
• Core Nodes (2 instances):
  • EMR Charges: 2 x $0.105/hour x 730 hours in a month = $153.30
  • EC2 Charges: 2 x $0.398/hour x 730 hours in a month = $581.08

Total: $1101.57 for a month at full utilization.

➥ AWS EMR on AWS EKS (Elastic Kubernetes Service)

Pricing for AWS EMR (AWS Elastic MapReduce) on EKS (Elastic Kubernetes Service) includes AWS EMR charges in addition to AWS EKS costs. Compute options on EKS can be met through either EC2 instances or AWS Fargate.

• AWS EC2 with EKS (Elastic Kubernetes Service): Costs cover EC2 instances or EBS volumes used for Kubernetes worker nodes. Pricing is per usage, with detailed rates on the EC2 pricing page.
• AWS Fargate with EKS (Elastic Kubernetes Service): Charges are based on the vCPU and memory allocated from container image download start to EKS pod termination, rounded to the nearest second with a one-minute minimum.

For AWS EMR on EKS, costs are calculated based on requested vCPU and memory resources for the task or pod from image download to pod termination. Rates are specific to the AWS region in use.

Example Region Rates (US East - Ohio):

• vCPU per hour: $0.01012
• Memory per GB per hour: $0.00111125

For example, running an AWS EMR-Spark application on an EKS cluster with 100 vCPUs and 300 GB of memory for 30 minutes incurs the following costs:

• vCPU Charges: 100 vCPU x $0.01012/hour x 0.5 = $0.506
• Memory Charges: 300 GB x $0.00111125/hour x 0.5 = $0.1667

Total: $0.6727

Additional EKS cluster fees may apply, and compute resources on EKS are separately charged if using AWS Fargate.

➥ AWS EMR on AWS Outposts

Pricing for AWS EMR (AWS Elastic MapReduce) on AWS Outposts aligns with standard EMR pricing. AWS Outposts extends AWS infrastructure to on-premises data centers, delivering consistent EMR functionality.

For more in-depth AWS Outposts-specific charges, see AWS Outposts pricing page.

➥ AWS EMR Serverless

AWS EMR (AWS Elastic MapReduce) Serverless is a fully managed option, where you only pay for the vCPU, memory, and storage resources used by your applications. EMR Serverless automatically manages scaling, so costs are based on actual usage from application start to finish, billed per second with a one-minute minimum.

• Worker Resource Configuration: Flexible configurations allow you to define the number of vCPUs, memory (up to 120 GB), and storage per worker (up to 2 TB).
• Compute and Memory Rates: Rates depend on aggregate resource usage across all workers in an application.
• Storage Options: Standard ephemeral storage or shuffle-optimized storage for heavy data movement needs.

Example Rates (US East - Ohio):

• vCPU per hour: $0.052624
• Memory per GB per hour: $0.0057785
• Standard storage per GB per hour: $0.000111

Additional AWS services such as Amazon S3 or Amazon CloudWatch may add to the cost, depending on the workload’s requirements.

For a job running on EMR Serverless with 25-75 workers (each 4 vCPU, 30 GB memory), costs are calculated as follows:

• vCPU Usage: (100 vCPU x $0.052624/hour x 0.5) + (200 vCPU x $0.052624/hour x 0.25) = $5.2624
• Memory Usage: (750 GB x $0.0057785/hour x 0.5) + (1500 GB x $0.0057785/hour x 0.25) = $4.333875

Total = $9.5963

AWS EMR WAL

For applications requiring Apache HBase, AWS EMR provides a Write Ahead Log (WAL) service, which ensures data durability and rapid recovery in case of cluster or availability issues. Charges apply for storage (WALHours), read (ReadRequestGiB), and write (WriteRequestGiB) operations.

• WAL Storage (WALHours): Charges per hour per HBase region, with retention for 30 days if data isn't flushed to Amazon S3 or removed by the user.
• Read and Write Operations: Each write or read request through Apache HBase is billed based on data size.

Example Rates (US East - Ohio):

• WALHours: $0.0018 per hour
• ReadRequestGiB and WriteRequestGiB: $0.0883 per GiB

Using EMR WAL with Apache HBase to log 3.55 million write and 1 million read requests over a month in the US-East-1 region:

• Write Requests: 3.55 GiB x $0.0883 per GiB = $0.30
• Read Requests: 1 GiB x $0.0883 per GiB = $0.08
• WAL Storage: 10 tables x 2 regions x 30 days x 24 hours x $0.0018/hour = $25.92

Total = $26.52 for the month.

Try it out yourself using the AWS Pricing Calculator for more details.

When to Use Redshift vs EMR?

Here are the key differences between AWS EMR and AWS Redshift for big data and analytics use cases:

Conclusion

And that’s a wrap! AWS EMR’s versatility and scalable architecture make it a powerful choice for big data processing. With support for a range of frameworks and deployment options, it's a complete disruptor for enterprise workload management—from ETL (Extract Transform Load) to real-time analytics. It simplifies data processing and helps you get to insights faster.

In this article, we have covered:

• What is AWS EMR?
• What is AWS EMR used for?
• How AWS EMR works: A look at AWS EMR architecture
• Best tips for working with AWS EMR
• AWS EMR pricing structure
• When to use Redshift vs EMR?

… and so much more!

FAQs

What is an AWS EMR?

AWS EMR (AWS Elastic MapReduce) is a cloud-native big data platform that simplifies running Apache Hadoop, Spark, and other distributed frameworks for processing large datasets.

How does AWS EMR work?

AWS EMR operates by creating a scalable cluster of EC2 (AWS Elastic Compute Cloud) instances to run distributed data processing jobs. Users can submit tasks via Spark, Hadoop, and other frameworks, and the system distributes these tasks across nodes. EMR supports storage in Amazon S3, HDFS, and EMRFS and integrates with AWS services like DynamoDB and Glue for additional data handling.

What is the difference between EC2 and EMR in AWS?

EC2 (AWS Elastic Compute Cloud) provides virtual servers (instances) in the AWS cloud, while EMR is a fully managed cluster platform that uses EC2 instances to process big data workloads. Essentially, EC2 is the underlying infrastructure, whereas EMR simplifies the management, scaling, and application of big data processing frameworks on top of EC2 resources.

Is AWS EMR an ETL tool?

Yes, AWS EMR can function as an ETL (Extract Transform Load) tool, particularly for batch processing and data transformation.

What types of workloads can be run on AWS EMR?

EMR supports various workloads, including:

• Batch ETL (Extract Transform Load) jobs
• Real-time analytics and stream processing
• Machine learning tasks
• Data warehousing and log analysis
• Financial and clickstream analysis
• Interactive data exploration

Is AWS EMR serverless?

AWS offers a serverless version of EMR called EMR Serverless, which lets users run big data jobs without managing cluster infrastructure, which automatically scales resources up and down based on workload needs.

What is EMR used for?

AWS EMR is used for large-scale data processing and analytics tasks, including batch processing, data transformations, and machine learning. It's also valuable for complex data workflows that involve heavy computation and vast datasets, integrating well with other AWS services for diverse analytical needs.

Is AWS EMR open-source?

EMR itself is not open-source; however, it supports open-source big data tools and frameworks like Apache Spark, Hadoop, and Hive. EMR provides a managed environment for these open-source tools, reducing the operational burden of configurations​.

Does AWS EMR use Hadoop?

Yes, AWS EMR supports the Hadoop ecosystem, including HDFS and MapReduce. 

What are the different node types in an EMR cluster?

An EMR cluster typically has three types of nodes:

• Master Node: Manages cluster coordination and monitors health.
• Core Nodes: Run tasks and store data in HDFS.
• Task Nodes: Perform compute tasks but do not store data

Does AWS EMR support auto-scaling?

Yes, AWS EMR supports auto-scaling, which allows clusters to dynamically adjust the number of EC2 (AWS Elastic Compute Cloud) instances based on workload demand. 

What is the default file system used by EMR?

The default file system in EMR is the Hadoop Distributed File System (HDFS) for storage within the cluster. EMR also integrates with Amazon S3 using EMRFS, a Hadoop-compatible file system for reading and writing to S3.

Can I use spot instances with AWS EMR?

Yes, EMR supports spot instances, which can help reduce costs significantly. Spot instances are ideal for fault-tolerant jobs where interruptions are manageable.

Can I schedule recurring jobs on AWS EMR?

While EMR itself doesn't provide a built-in scheduler, you can schedule jobs using AWS Data Pipeline, AWS Step Functions, or by setting up cron jobs on EC2 (AWS Elastic Compute Cloud) instances.