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 13.2% mindshare in H, compared to Apache’s 17.8% 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

1,118 Views
942 Comparison Views

85% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,580 Views
1,196 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 March 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.2%, down from 17.7% compared to the previous year. The mindshare of Apache Spark is 17.8%, down from 21.2% 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 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 initial setup is straightforward."

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

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

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

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

"The project management is very streamlined."

More Amazon EMR pros

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

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

"The processing time is very much improved over the data warehouse solution that we were using."

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

"ETL and streaming capabilities."

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

"I feel the streaming is its best feature."

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

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 problem for us is it starts very slow."

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

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

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

"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

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

"Apache Spark lacks geospatial data."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"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."

"Amazon EMR's price is reasonable."

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

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

"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

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

"Apache Spark is an open-source tool."

"The product is expensive, considering the setup."

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

"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."

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

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

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for 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

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 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

Azure Data Factory vs Amazon EMR

Compared 5% of the time

AWS Lake Formation 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 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

Jakarta EE vs Apache Spark

Compared 2% 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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