AWS EMR (Elastic MapReduce)

AWS EMR is a cloud-based big data platform that allows processing large datasets using popular frameworks like Spark, Hadoop, HBase, and more

By - Manish Kumar Barnwal
Updated on
October 12, 2023


What is AWS EMR?

AWS EMR leverages a distributed processing model to handle large-scale data processing tasks. It automatically provisions and configures the required compute and storage resources, creating a cluster that can process data in parallel.

Users can choose from a variety of big data frameworks and applications to perform specific tasks. EMR clusters can be customized based on the workload, allowing users to add or remove instances as needed to optimize performance and cost.

When to use AWS EMR?

AWS EMR is suitable for a wide range of use cases and scenarios where processing and analyzing large-scale data sets are required. Some common scenarios where AWS EMR can be utilized effectively include:

  1. Data Warehousing: EMR can be used to process and transform raw data before loading it into data warehouses, making it easier to analyze and gain insights from large datasets.
  2. Log Analysis: Analyzing log files generated by applications and systems can be done efficiently with EMR, providing valuable insights for troubleshooting and monitoring.
  3. ETL (Extract, Transform, Load): EMR can be used for ETL processes, enabling users to extract data from various sources, transform it, and load it into the desired target for analysis.
  4. Data Science and Machine Learning: EMR supports popular machine learning frameworks such as Apache Spark and Apache Hadoop, making it a suitable platform for running data science and ML workloads.
  5. Real-time Analytics: EMR can process streaming data from various sources, allowing organizations to perform real-time analytics and take immediate actions based on the insights.
  6. Genomics and Bioinformatics: EMR can be used for processing and analyzing genomics and bioinformatics data, enabling researchers to gain insights into complex biological processes.

How does AWS EMR work?

Amazon Elastic MapReduce (EMR) is a fully managed big data processing service offered by Amazon Web Services (AWS). It simplifies the processing and analysis of vast amounts of data by providing a scalable, cost-effective, and secure solution.

EMR allows users to run popular big data frameworks such as Apache Hadoop, Apache Spark, Apache Hive, Apache HBase, and more, without the complexities of setting up and managing the underlying infrastructure. With EMR, users can process, transform, and analyze data in real time, making it ideal for various data-intensive use cases.

Features & Advantages

Benefits of AWS EMR

  1. Scalability: AWS EMR allows you to easily scale your big data processing clusters up or down based on the workload requirements. With auto-scaling capabilities, you can automatically add or remove instances to ensure optimal performance and cost efficiency.
  2. Managed Hadoop Ecosystem: EMR is a fully managed service that provides a complete Hadoop ecosystem, including Apache Spark, Hive, HBase, Pig, and other big data processing frameworks. This eliminates the need for manual cluster setup and configuration, making it easy to launch and manage big data applications.
  3. Integration with Amazon S3: EMR seamlessly integrates with Amazon S3, allowing you to store and process vast amounts of data at a lower cost. You can easily read and write data from S3, making it a central data store for your EMR clusters.
  4. Security and Access Control: AWS EMR offers various security features, including encryption of data at rest and in transit, integration with AWS Identity and Access Management (IAM) for access control, and support for Virtual Private Cloud (VPC) for network isolation.
  5. Support for Multiple Frameworks: EMR supports a wide range of big data processing frameworks, such as Apache Spark, Apache Hadoop, Apache Hive, Apache HBase, and Apache Pig. This allows you to choose the right tool for your specific data processing needs.
  6. Data Lake Analytics: EMR can be used as part of a data lake architecture, where data is ingested, stored, and processed in its raw form. By combining EMR with other AWS services like AWS Glue, Athena, and Redshift, you can build a powerful and cost-effective data lake solution.

Advantages of AWS EMR

  1. Cost-Effective: AWS EMR enables you to pay only for the resources you use, making it a cost-effective solution for big data processing. With features like auto-scaling and Spot Instances, you can optimize costs based on workload demands.
  2. Easy to Use: EMR provides a simple and intuitive interface for launching and managing big data clusters. It abstracts the complexities of setting up and configuring Hadoop and other frameworks, allowing you to focus on data processing and analysis.
  3. Flexibility: EMR supports various big data processing frameworks, giving you the flexibility to choose the right tool for the job. You can use Spark for real-time data processing, Hive for SQL-like queries, HBase for NoSQL data storage, and more.
  4. Performance: AWS EMR is designed to deliver high performance for big data processing. With the ability to scale clusters and use powerful instance types, you can process large volumes of data efficiently and quickly.
  5. Integration with AWS Services: EMR seamlessly integrates with other AWS services, such as Amazon S3, Amazon RDS, Amazon DynamoDB, and more. This integration enables you to build comprehensive data pipelines and leverage the full potential of the AWS ecosystem.
  6. Managed Service: EMR is a fully managed service, which means AWS takes care of cluster provisioning, monitoring, patching, and security. This allows you to focus on data analysis and business insights rather than managing infrastructure.


How much does AWS EMR cost?

AWS EMR is a flexible and cost-effective big data processing service, designed to handle large-scale data workloads. While EMR is not entirely free, it offers a pay-as-you-go pricing model, allowing you to pay only for the resources and services you use. Let's explore the pricing factors, whether there are any free tiers, and the pricing tiers available for AWS EMR.

AWS EMR Pricing Factors:

The pricing of AWS EMR is determined by several factors, and understanding these factors is essential for cost optimization. The key pricing factors for AWS EMR are:

  1. Instance Type: The choice of instance type significantly impacts the cost. AWS offers a wide range of EC2 instance types optimized for different workloads. Each instance type comes with varying compute capabilities and costs, allowing you to select the one that best aligns with your big data processing requirements.
  2. Cluster Duration: AWS EMR charges are based on the time your cluster is running. You are billed per second of usage, and partial hours are rounded to the nearest second. To optimize costs, it's crucial to terminate the cluster when it's not actively processing data.
  3. Data Processing: AWS EMR charges for data processing, which includes data transfer between EMR and other AWS services, such as Amazon S3 or Amazon DynamoDB, as well as data processing operations.
  4. Additional Services: If you use additional AWS services in conjunction with EMR, such as Amazon RDS or Amazon Redshift, you will incur separate charges for those services.

Is AWS EMR Free or Paid?

AWS EMR is not entirely free, and its usage incurs costs based on the factors mentioned above. However, AWS offers a free tier for new customers, allowing them to explore and experiment with EMR for a limited time at no cost.

The AWS Free Tier includes 750 hours of EC2 compute usage per month for the first 12 months, which can be used for running EMR clusters. Additionally, the free tier includes 5 GB of Amazon S3 storage and 20,000 read and 2,000 write requests per month for the first 12 months.

AWS EMR Pricing Factors

AWS EMR does not have predefined pricing tiers. Instead, the pricing is based on the factors discussed earlier, such as instance type, cluster duration, data processing, and additional services used. AWS follows a pay-as-you-go model, where you are billed for the specific resources and services you consume during your EMR cluster's runtime.

Let's break down the pricing and tiers for Amazon EMR on Amazon EC2. The pricing is shown in USD per hour.


On-Demand Instances (Per Hour):

General Purpose - Current Generation:

  1. m7g.xlarge: $0.1632 (EC2) + $0.0408 (EMR) = $0.2040 per hour
  2. m7g.2xlarge: $0.3264 (EC2) + $0.0816 (EMR) = $0.4080 per hour
  3. m7g.4xlarge: $0.6528 (EC2) + $0.1632 (EMR) = $0.8160 per hour

Compute Optimized - Current Generation:

  1. c7g.xlarge: $0.1445 (EC2) + $0.03625 (EMR) = $0.18075 per hour
  2. c7g.2xlarge: $0.2890 (EC2) + $0.07250 (EMR) = $0.36150 per hour
  3. c7g.4xlarge: $0.5781 (EC2) + $0.1450 (EMR) = $0.72310 per hour

Memory Optimized - Current Generation:

  1. x2gd.xlarge: $0.3340 (EC2) + $0.0835 (EMR) = $0.41750 per hour
  2. x2gd.2xlarge: $0.6680 (EC2) + $0.1670 (EMR) = $0.83500 per hour
  3. x2gd.4xlarge: $1.3360 (EC2) + $0.3340 (EMR) = $1.67000 per hour

Accelerated Computing - Current Generation:

  1. p3.2xlarge: $3.06 (EC2) + $0.27 (EMR) = $3.33 per hour
  2. p3.8xlarge: $12.24 (EC2) + $0.27 (EMR) = $12.51 per hour
  3. p3.16xlarge: $24.48 (EC2) + $0.27 (EMR) = $24.75 per hour

General Purpose - Previous Generation:

  1. m4.large: $0.10 (EC2) + $0.03 (EMR) = $0.13 per hour
  2. m4.xlarge: $0.20 (EC2) + $0.06 (EMR) = $0.26 per hour
  3. m4.2xlarge: $0.40 (EC2) + $0.12 (EMR) = $0.52 per hour

GPU Optimized - Previous Generation:

  1. g3.4xlarge: $1.14 (EC2) + $0.27 (EMR) = $1.41 per hour
  2. g3.8xlarge: $2.28 (EC2) + $0.27 (EMR) = $2.55 per hour
  3. g3.16xlarge: $4.56 (EC2) + $0.27 (EMR) = $4.83 per hour

Reserved Instances: One-year and three-year Reserved Instances offer discounted pricing compared to On-Demand. The exact discounts and prices would depend on the specific type of Reserved Instances you purchase.

  1. Spot Instances: Spot Instances provide spare EC2 capacity at up to a 90% discount compared to On-Demand prices. The prices are dynamic and can vary based on supply and demand for EC2 instances.


General Purpose: Let's say you run a medium-sized Hadoop cluster for data processing using m7g.xlarge instances for 10 hours a day:

  1. On-Demand Price: $0.2040 per hour
  2. Total Cost: $0.2040 * 10 = $2.04 per day

Accelerated Computing: If you need to run GPU-intensive machine learning tasks on p3.8xlarge instances 24/7:

  1. On-Demand Price: $12.51 per hour
  2. Total Cost: $12.51 * 24 = $300.24 per day

Reserved Instances: Suppose you need to run a large EMR cluster consistently for a year. You can purchase Reserved Instances for a reduced hourly rate:

  1. Reserved Price: $0.15 per hour (example discounted rate)
  2. Total Cost: $0.15 * 24 * 365 = $1,314 per year

Remember that these are just simplified examples, and your actual usage patterns and requirements may vary. Always check the AWS website or use the AWS Pricing Calculator for precise pricing details based on your specific needs.

Cost Optimization

How to reduce AWS EMR Costs?

To optimize costs while using AWS EMR, consider implementing the following strategies:

  1. Right-Sizing EMR Clusters: One of the key aspects of cost optimization for AWS EMR is to right-size the clusters based on the actual workload requirements. Analyze the historical usage patterns and resource utilization to determine the optimal number and types of instances for the cluster. AWS offers various instance families, each with different performance and pricing characteristics. By selecting the most suitable instance types, you can strike the right balance between performance and cost.
  2. Spot Instances for Cost Savings: Leveraging Amazon EC2 Spot Instances for EMR clusters can significantly reduce costs. Spot Instances are spare EC2 capacity offered at highly discounted prices compared to on-demand instances. However, they can be interrupted by AWS when the demand for regular instances increases. EMR can automatically handle Spot Instance interruptions by using automatic instance replacement, which seamlessly switches to on-demand instances to ensure the continuity of your data processing tasks.
  3. Leveraging Auto-Scaling: Implementing auto-scaling policies for your EMR clusters allows them to dynamically adjust the number of instances based on actual workload demand. During peak periods, the cluster can scale up to handle higher loads, and during low-demand periods, it can scale down to save costs. Auto-scaling ensures that you pay only for the resources you need, eliminating over-provisioning and underutilization of instances.
  4. Data Compression and Storage Optimization: Optimize data storage costs by compressing data before storing it in Amazon S3. EMR supports various compression formats like gzip, Snappy, and LZO, which can significantly reduce storage costs without compromising data processing speed. Additionally, consider using S3 Intelligent-Tiering to automatically move infrequently accessed data to lower-cost storage classes, such as S3 Glacier, to further save on storage expenses.
  5. Use Spot Blocks for Mission-Critical Jobs: For critical jobs that require a guaranteed execution time, you can use Spot Blocks, a feature that enables you to reserve Spot Instances for a specified duration (one to six hours). Spot Blocks provide a cost-effective solution for time-sensitive workloads, as they offer a significant discount compared to on-demand instances.
  6. Scheduled Clusters: If you have periodic or batch jobs, consider using scheduled clusters. With this approach, you create clusters only during the specific time periods when data processing is required. This way, you avoid running clusters continuously and incur costs only when necessary.

Best Practices for AWS EMR Cost Optimization

  1. Right-size your EMR clusters based on workload requirements to avoid over-provisioning.
  2. Utilize EC2 Spot Instances and auto-scaling to dynamically adjust resources and reduce costs during low-demand periods.
  3. Compress data before storing it in S3 and leverage intelligent tiering for cost-effective storage.
  4. Consider Spot Blocks for mission-critical jobs that require guaranteed execution time.
  5. Use scheduled clusters for periodic or batch jobs to avoid running clusters continuously.
  6. Monitor performance and resource utilization to identify optimization opportunities and make informed decisions.

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