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, 85% 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 22 Amazon EMR reviews

1,035 Views
872 Comparison Views

85% 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: March 2025).

Buyer's Guide

Amazon EMR vs. Apache Spark

March 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 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 solution helps us manage huge volumes of data."

"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."

"The solution is scalable."

"The initial setup is pretty straightforward."

"The initial setup is straightforward."

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

More Amazon EMR pros

"Provides a lot of good documentation compared to other solutions."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"There's a lot of functionality."

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

"The product's deployment phase is easy."

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

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

"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."

More Apache Spark pros

Cons

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

"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."

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

"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."

"The initial setup was time-consuming."

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

More Amazon EMR cons

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

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

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

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

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

"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."

"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."

"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."

More Apache Spark cons

Pricing and Cost Advice

"The price of the solution is 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."

"Amazon EMR's price is reasonable."

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

More Amazon EMR pricing and cost advice

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

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

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

"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 tool."

"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."

"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."

"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

26%

Computer Software Company

13%

Manufacturing Company

Educational Organization

Financial Services Firm

28%

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 39% 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

More Amazon EMR Competitors

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 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

Jakarta EE vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EMR

March 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

March 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

March 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: March 2025.

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