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 8.0, 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

1,035 Views
872 Comparison Views

86% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,404 Views
1,090 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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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 (4th), 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

"The initial setup is pretty straightforward."

"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."

"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."

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

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

"The solution is pretty simple to set up."

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

"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."

More Amazon EMR pros

"The data processing framework is good."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

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

"I found the solution stable. We haven't had any problems with it."

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

"We use Spark to process data from different data sources."

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"The fault tolerant feature is provided."

More Apache Spark pros

Cons

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

"The legacy versions of the solution are not supported in the new versions."

"The solution can become expensive if you are not careful."

"Modules and strategies should be better handled and notified early in advance."

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

"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."

"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."

"There is room for improvement in pricing."

More Amazon EMR cons

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"The solution’s integration with other platforms should be improved."

"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

"Apache Spark's GUI and scalability could be improved."

"The product could improve the user interface and make it easier for new users."

"At the initial stage, the product provides no container logs to check the activity."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"One limitation is that not all machine learning libraries and models support it."

More Apache Spark cons

Pricing and Cost Advice

"Amazon EMR's price is reasonable."

"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."

"Amazon EMR is not very expensive."

"The cost of Amazon EMR is very high."

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

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

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

"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."

More Amazon EMR pricing and cost advice

"The product is expensive, considering the setup."

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

"Apache Spark is an expensive solution."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

14%

Educational Organization

Manufacturing Company

Financial Services Firm

27%

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?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

See all answers

Comparisons

Snowflake vs Amazon EMR

Compared 37% 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 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Jakarta EE vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EMR

April 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

April 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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