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 13.3% mindshare in H, compared to Apache’s 17.5% 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 65 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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Helped 848,716 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 (4th), Java Frameworks (2nd)

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.3%, down from 17.1% compared to the previous year. The mindshare of Apache Spark is 17.5%, 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.

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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 straightforward."

"The project management is very streamlined."

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

"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."

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

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

"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."

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

More Amazon EMR pros

"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."

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

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"The deployment of the product is easy."

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

"The product's deployment phase is easy."

"The data processing framework is good."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

More Apache Spark pros

Cons

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

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

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

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

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

"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."

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

More Amazon EMR cons

"Apache Spark should add some resource management improvements to the algorithms."

"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."

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

"The migration of data between different versions could be improved."

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."

"The logging for the observability platform could be better."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

More Apache Spark cons

Pricing and Cost Advice

"The price of the solution is expensive."

"Amazon EMR is not very expensive."

"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 cost of Amazon EMR is very high."

"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."

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

"Amazon EMR's price is reasonable."

More Amazon EMR pricing and cost advice

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

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

"It is an open-source solution, it is free of charge."

"Apache Spark is an open-source tool."

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

"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."

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

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

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?

The cost of Amazon EMR is a little bit expensive, especially considering the support package, which includes a gold package.

See all answers

What needs improvement with Amazon EMR?

Spark jobs take longer on Amazon EMR compared to previous experiences. This aspect could be improved to make them more efficient.

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 28% of the time

Amazon Redshift vs Amazon EMR

Compared 8% of the time

AWS Lake Formation vs Amazon EMR

Compared 5% of the time

Azure Data Factory vs Amazon EMR

Compared 5% of the time

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Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 11% 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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