Amazon EMR vs Apache Spark comparison

Amazon Web Services (AWS) and Apache are both solutions in the Hadoop category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.3, while Apache is ranked #1 with an average rating of 8.6. Amazon Web Services (AWS) holds a 13.9% mindshare in H, compared to Apache’s 17.8% mindshare. Additionally, 86% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon EMR

Read 23 Amazon EMR reviews

888 Views
751 Comparison Views

86% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,245 Views
966 Comparison Views

90% willing to recommend

Amazon EMR

Apache Spark

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Executive Summary

We performed a comparison between Amazon EMR and Apache Spark based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon EMR vs. Apache Spark Report (Updated: April 2025).

Buyer's Guide

Amazon EMR vs. Apache Spark

April 2025

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Helped 851,604 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Amazon EMR

Ranking in Hadoop

3rd

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (12th)

Apache Spark

Ranking in Hadoop

1st

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Compute Service (5th), Java Frameworks (2nd)

Mindshare comparison

As of May 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.9%, down from 17.2% compared to the previous year. The mindshare of Apache Spark is 17.8%, down from 21.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Prashant Singh

Vice President -Product Management at Paytm

Seamless data integration enhances reporting efficiency and an easy setup

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.

Read full review

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"It has a variety of options and support systems."

"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."

"Amazon EMR is a good solution that can be used to manage big data."

"The project management is very streamlined."

"It allows users to access the data through a web interface."

"Amazon EMR's most valuable features are processing speed and data storage capacity."

"The solution helps us manage huge volumes of data."

"I rate Amazon EMR as ten out of ten."

More Amazon EMR pros

"The scalability has been the most valuable aspect of the solution."

"The most valuable feature of this solution is its capacity for processing large amounts of data."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"The product's initial setup phase was easy."

"It provides a scalable machine learning library."

"This solution provides a clear and convenient syntax for our analytical tasks."

More Apache Spark pros

Cons

"The product must add some of the latest technologies to provide more flexibility to the users."

"The initial setup was time-consuming."

"The most complicated thing is configuring to the cluster and ensure it's running correctly."

"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."

"The product's features for storing data in static clusters could be better."

"The dashboard management could be better. Right now, it's lacking a bit."

"Spark jobs take longer on Amazon EMR compared to previous experiences."

"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."

More Amazon EMR cons

"The initial setup was not easy."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

More Apache Spark cons

Pricing and Cost Advice

"Amazon EMR is not very expensive."

"The product is not cheap, but it is not expensive."

"Amazon EMR's price is reasonable."

"You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances."

"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."

"There is no need to pay extra for third-party software."

"The cost of Amazon EMR is very high."

"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."

More Amazon EMR pricing and cost advice

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"The solution is affordable and there are no additional licensing costs."

"We are using the free version of the solution."

"Spark is an open-source solution, so there are no licensing costs."

"The product is expensive, considering the setup."

"They provide an open-source license for the on-premise version."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

14%

Educational Organization

Manufacturing Company

Financial Services Firm

26%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Amazon EMR?

Amazon EMR is a good solution that can be used to manage big data.

See all answers

What is your experience regarding pricing and costs for Amazon EMR?

Compared to others, Amazon seems efficient and is considered good for Big Data workloads. Costs are involved based on cluster resources, data volumes, EC2 ( /products/amazon-ec2-reviews ) instances...

See all answers

What needs improvement with Amazon EMR?

There is room for improvement with respect to retries, handling the volume of data on S3 ( /products/amazon-s3-reviews ) buckets, cluster provisioning, scaling, termination, security, and integrati...

See all answers

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

See all answers

Comparisons

Snowflake vs Amazon EMR

Compared 36% of the time

Cloudera Distribution for Hadoop vs Amazon EMR

Compared 27% of the time

Amazon Redshift vs Amazon EMR

Compared 8% of the time

AWS Lake Formation vs Amazon EMR

Compared 6% of the time

Azure Data Factory vs Amazon EMR

Compared 5% of the time

More Amazon EMR Competitors

Spring Boot vs Apache Spark

Compared 27% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 11% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Azure Stream Analytics vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EMR

May 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Also Known As

Amazon Elastic MapReduce

No data available

Overview

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.

Amazon Web Services (AWS)

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Sample Customers

Yelp

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

Buyer's Guide

Amazon EMR vs. Apache Spark

April 2025

Free Report: Amazon EMR vs. Apache Spark

Find out what your peers are saying about Amazon EMR vs. Apache Spark and other solutions. Updated: April 2025.

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