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 13.6% mindshare in H, compared to Apache’s 17.7% 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

1,215 Views
1,041 Comparison Views

85% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,627 Views
1,270 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: January 2025).

Buyer's Guide

Amazon EMR vs. Apache Spark

January 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 (3rd), Java Frameworks (2nd)

Mindshare comparison

As of February 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.6%, down from 18.0% compared to the previous year. The mindshare of Apache Spark is 17.7%, down from 20.8% 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 solution helps us manage huge volumes of data."

"The project management is very streamlined."

"The initial setup is 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."

"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."

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

"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 product's initial setup phase was easy."

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

"Features include machine learning, real time streaming, and data processing."

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

"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"The deployment of the product 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."

More Apache Spark pros

Cons

"The problem for us is it starts very slow."

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

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

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

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

More Amazon EMR cons

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

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

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

"The solution needs to optimize shuffling between workers."

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

"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."

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

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

More Apache Spark cons

Pricing and Cost Advice

"The price of the solution is expensive."

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

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

"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's price is reasonable."

"Amazon EMR is not very expensive."

"The cost of Amazon EMR is very high."

"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 expensive solution."

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

"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."

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

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

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

"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."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

13%

Manufacturing Company

Educational Organization

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 30% 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 4% 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 2% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EMR

February 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

February 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

January 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: January 2025.

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