Amazon EMR Reviews

Name: Amazon EMR
Brand: Amazon Web Services (AWS)
Rating: 3.9 (22 reviews)

Vendor: Amazon Web Services (AWS)

3.9 out of 5

22 reviews
85% willing to recommend

1,123 followers

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What is Amazon EMR?

Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.

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Amazon EMR is the #3 ranked solution in top Hadoop solutions and #11 ranked solution in top Cloud Data Warehouse solutions. PeerSpot users give Amazon EMR an average rating of 7.8 out of 10. Based on the analysis of the 22 most recent Amazon EMR reviews, the overall sentiment is positive, with a sentiment score of 7.2. (Among the highest in the category). Amazon EMR is most commonly compared to Apache Spark: Amazon EMR vs Apache Spark. Amazon EMR is popular among the large enterprise segment, accounting for 73% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 25% of all views.

Helped 824,053 peers since 2012

Featured reviews

Prashant Singh

Vice President -Product Management at Paytm

The cost is increasing. We are looking into how we can optimize the cost part of EMR. We're doing a comparison between Cloudera running on AWS and running AWS EMR. We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part.

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Prashant Singh

Vice President -Product Management at Paytm

Amazon EMR has multiple connectors that can connect to various data sources. The service charges are based on processing only, depending on the resources used, which can help save money. It is easy to integrate with other services for storage, allowing data to be shifted to cheaper storage based on usage.

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Quan Vu

Data Governance Manager at VPBFC

We need to have a data pipeline tool to ensure consistent data processing for the initial setup. We create a framework, read the code, and execute it in a data catalog. The size of the maintenance team depends on the project and the use cases. Usually, one backup team of four or five DevOps executives takes care of the backend and database. We need to separate our environments into production and development. We use GitHub for source control, Jenkins for the deployment pipeline, and a standard CI/CD tool to deploy code changes into production. We need to develop a deployment framework so developers only need to provide the code for their projects. The underlying engine then deploys the code, reads it, addresses the EMR filter, executes it, and completes the data processing.

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Amazon EMR mindshare

Product category:

As of December 2024, the mindshare of Amazon EMR in the Hadoop category stands at 14.7%, down from 18.7% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Hadoop

PeerAnalyst reports

Type	Title	Date
Category	Hadoop	Dec 21, 2024	Download
Product	Reviews, tips, and advice from real users	Dec 21, 2024	Download
Comparison	Amazon EMR vs Apache Spark	Dec 21, 2024	Download
Comparison	Amazon EMR vs Cloudera Distribution for Hadoop	Dec 21, 2024	Download
Comparison	Amazon EMR vs Spark SQL	Dec 21, 2024	Download

Title	Rating	Mindshare	Recommending
Apache Spark	4.2	18.0%	90%	64 interviews Add to research
Cloudera Distribution for Hadoop	4.0	28.2%	92%	49 interviews Add to research

Valuable Features

Amazon EMR offers scalability and cost-effectiveness, using auto-scaling and managed services for ease of use. It integrates seamlessly with Hadoop, HDFS, and multiple open-source platforms. Users appreciate its stability, reliability, and high processing efficiency with tools like Hive, Spark, and Flink. EMR provides robust data management with flexible cloud storage options. Its secure workflow management is valued, and the pricing is resource-based, supporting extensive data processing without hardware management overhead.

"Amazon EMR has multiple connectors that can connect to various data sources."
"The security of the managed workflow and the managed services are the best features for us. Since we inherited their security model and it's all managed services, those are the key benefits for our clients."
"The solution helps us manage huge volumes of data."

Room for Improvement

Amazon EMR requires improved user interface, better integration with tools like Hive and Prometheus Grafana, and enhanced stability. Configuration complexity poses a challenge for users. Cost control and scalability need optimization. Initial startup is slow, and legacy version compatibility is problematic. Automation for cluster resizing and enhanced support are crucial. Improved monitoring, debugging, and web support are necessary. Users suggest more flexible features and improvements in CI/CD, MLOps, and data storage management.

"Spark jobs take longer on Amazon EMR compared to previous experiences."
"The solution can become expensive if you are not careful."
"The product must add some of the latest technologies to provide more flexibility to the users."

ROI

ROI from Amazon EMR varies by company and usage. Many companies see significant cost savings and a high return on investment, especially those transitioning from on-premise systems. Some businesses report up to 20% cost reduction, and returns of at least two to one are common. However, some companies have not quantified their ROI, and others cannot specify exact figures.

Pricing

Amazon EMR pricing varies by vendor and usage, often considered high compared to competitors like Microsoft. Costs are primarily driven by underlying infrastructure, with no additional licensing fees, as it’s a fully managed service. Users pay only for EC2 resources, dictated by clusters and usage time. Feedback indicates prices range significantly, from $40,000 monthly costs to licensing fees reaching over a million annually for large clients. Pricing is seen as expensive, with unexpected charges possible.

"I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."
"The product is not cheap, but it is not expensive."
"Amazon EMR is not very expensive."

Popular Use Cases

Organizations primarily utilize Amazon EMR to process large-scale data, execute Spark scripts, and manage data pipelines. They deploy EMR on cloud infrastructure, often integrating it with other AWS services like SageMaker for data analysis and personalized recommendations. Use cases include building data lakes, handling B2B project management, migrating workflows to serverless architectures, and supporting AI-driven analytics projects across various sectors, including finance, healthcare, and public management systems. Data is commonly stored and processed using Amazon S3.

Service and Support

Amazon EMR's customer service and support show inconsistency, with experiences ranging from exceptional responsiveness to dissatisfaction with response times. Many users express satisfaction with technical support's knowledge and responsiveness, while some highlight challenges in integration with open-source solutions. Premier Partners appreciate direct contact with product teams. Several users commend their timely assistance through chat portals, contributing to high ratings, while others desire faster response times. There is admirable satisfaction among numerous organizations.

Deployment

Amazon EMR's initial setup is described as straightforward and easy by many users. It often requires minimal time for deployment, ranging from 30 minutes to a day, depending on configurations and familiarity with the tool. While some complexities are noted, especially for new users, many find using the AWS Console or Terraform beneficial. Challenges such as configuring multi-factor authentication can arise. Teams generally consist of a few members, with tasks divided among DevOps and cloud specialists.

Scalability

Amazon EMR is widely perceived as highly scalable, accommodating various user needs related to memory, storage, and GPU. It performs well for enterprises and large organizations with up to thousands of users, allowing flexible scaling. Some report issues with resource allocation timing and computational performance between cloud and on-premises environments. Users mention a scalable platform that can be customized for specific needs, demonstrating reliability in handling extensive data processing tasks.

Stability

Amazon EMR is described as a stable and reliable platform by multiple users, with no significant issues noted in terms of stability. Some mention necessary configuration adjustments for optimal performance. Occasional issues arise but are not frequent. The tool is rated high in terms of stability, with some suggesting minor improvements. High availability and data integrity are highlighted, contributing to its reliability. Users consistently find Amazon EMR to be stable.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Amazon EMR Buyer's Guide for additional reliable information.