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 8.7. Amazon Web Services (AWS) holds a 14.2% mindshare in H, compared to Apache’s 18.4% 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

1,295 Views
1,112 Comparison Views

85% willing to recommend

Apache Spark

Read 64 Apache Spark reviews

1,728 Views
1,331 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 (11th)

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 January 2025, in the Hadoop category, the mindshare of Amazon EMR is 14.2%, down from 18.2% compared to the previous year. The mindshare of Apache Spark is 18.4%, down from 21.5% 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

"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."

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

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

"The project management is very streamlined."

"The initial setup is pretty straightforward."

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

"The solution is scalable."

More Amazon EMR pros

"The data processing framework is good."

"Spark can handle small to huge data and is suitable for any size of company."

"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."

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

"The solution is scalable."

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

"Apache Spark can do large volume interactive data analysis."

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

More Apache Spark pros

Cons

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

"The product's features for storing data in static clusters could be better."

"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."

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

"The initial setup was time-consuming."

"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."

More Amazon EMR cons

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

"There were some problems related to the product's compatibility with a few Python libraries."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"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."

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

"They could improve the issues related to programming language for the platform."

"The initial setup was not easy."

More Apache Spark cons

Pricing and Cost Advice

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

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

"Amazon EMR's price is reasonable."

"The cost of Amazon EMR is very high."

"Amazon EMR is not very expensive."

"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."

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

More Amazon EMR pricing and cost advice

"Apache Spark is an expensive solution."

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

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

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

"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 an open-source tool."

"It is an open-source platform. We do not pay for its subscription."

"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

24%

Computer Software Company

14%

Manufacturing Company

Educational Organization

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

University

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 main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

See all answers

Comparisons

Snowflake vs Amazon EMR

Compared 38% of the time

Cloudera Distribution for Hadoop vs Amazon EMR

Compared 30% of the time

Amazon Redshift vs Amazon EMR

Compared 7% of the time

Azure Data Factory vs Amazon EMR

Compared 6% of the time

HPE Ezmeral Data Fabric vs Amazon EMR

Compared 4% of the time

More Amazon EMR Competitors

Spring Boot vs Apache Spark

Compared 28% 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 9% of the time

Amazon EC2 vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EMR

January 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

January 2025

Download Apache Spark product report

Also Known As

Amazon Elastic MapReduce

No data available

Learn More

Amazon Web Services (AWS)

Apache

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.

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

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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