Amazon EMR vs Apache Spark vs HPE Ezmeral Data Fabric 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 8.1, 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 19.2% 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

1,863 Views
671 Comparison Views

86% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

4,468 Views
983 Comparison Views

90% willing to recommend

HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews

1,172 Views
984 Comparison Views

100% willing to recommend

Amazon EMR

Apache Spark

HPE Ezmeral Data Fabric

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon EMR, Apache Spark, and HPE Ezmeral Data Fabric based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.

To learn more, read our detailed Hadoop Report (Updated: July 2025).

Buyer's Guide

Hadoop

July 2025

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Helped 864,574 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:

Mindshare comparison

As of August 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.6%, down from 15.3% compared to the previous year. The mindshare of Apache Spark is 19.2%, down from 20.2% compared to the previous year. The mindshare of HPE Ezmeral Data Fabric is 14.8%, up from 13.2% 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.

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Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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Hamid M. Hamid

Data architect at Banking Sector

A stable and scalable tool that serves as a great database

The initial setup of HPE Ezmeral Data Fabric is easy. I am not sure how long it took to deploy HPE Ezmeral Data Fabric, but I haven't heard about any disadvantages when it comes to the time taken for the deployment. I remember that one of our company's clients who had purchased the product never mentioned the product's setup phase being complex. One of the drawbacks with HPE Ezmeral Data Fabric stems from the fact that the product's upgrade was not straightforward, and it was a complex process since one of the teams in my company who deals with the tool found the upgrade part to be tough. The solution is deployed on an on-premises model. My company has two dedicated staff members to look after the deployment and maintenance phases of HPE Ezmeral Data Fabric.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

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

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

"The solution is pretty simple to set up."

"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."

"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."

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

"This is the best tool for hosts and it's really flexible and scalable."

"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 deployment of the product is easy."

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

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

"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."

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

"It provides a scalable machine learning library."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"The solution has been very stable."

More Apache Spark pros

"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."

"The model creation was very interesting, especially with the libraries provided by the platform."

"I like the administration part."

"My customers find the product cheaper compared to other solutions. The previous solution that we used did not have unified analytics like the runtime or the analog."

"It is a stable solution...It is a scalable solution."

Cons

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

"The initial setup was time-consuming."

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

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

"There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB."

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

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

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

More Amazon EMR cons

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"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."

"The initial setup was not easy."

"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."

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

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"The Spark solution could improve in scheduling tasks and managing dependencies."

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

More Apache Spark cons

"The deployment could be faster. I want more support for the data lake in the next release."

"HPE Ezmeral Data Fabric is not compatible with third-party tools."

"Upgrading Ezmeral to a new version is a pain. They're trying to make the solution more container-friendly, so I think they're going in the right direction. The only problem we've had in the past was the upgrades. The process isn't smooth due to how the Red Hat operating system upgrades currently work."

"The product is not user-friendly."

"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."

Pricing and Cost Advice

"The cost of Amazon EMR is very high."

"Amazon EMR is not very 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."

"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."

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

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

More Amazon EMR pricing and cost advice

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

"The product is expensive, considering the setup."

"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."

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

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

"Apache Spark is an expensive solution."

"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."

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

More Apache Spark pricing and cost advice

"There is a need for my company to pay for the licensing costs of the solution."

"HPE is flexible with you if you are an existing customer. They offer different models that might be beneficial for your organization. It all depends on how you negotiate."

"The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs."

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Top Industries

By visitors reading reviews

Financial Services Firm

24%

Computer Software Company

13%

Educational Organization

10%

Manufacturing Company

Financial Services Firm

26%

Computer Software Company

10%

Comms Service Provider

Manufacturing Company

Financial Services Firm

19%

Computer Software Company

13%

Comms Service Provider

10%

Healthcare Company

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?

Compared to others, Amazon seems efficient and is considered good for Big Data workloads. Costs are involved based on...

See all answers

What needs improvement with Amazon EMR?

There is room for improvement with respect to retries, handling the volume of data on S3 ( /products/amazon-s3-review...

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?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite com...

See all answers

What do you like most about HPE Ezmeral Data Fabric?

It is a stable solution...It is a scalable solution.

See all answers

What needs improvement with HPE Ezmeral Data Fabric?

There are some drawbacks in HPE Ezmeral Data Fabric when it comes to the interoperability part. HPE Ezmeral Data Fabr...

See all answers

What is your primary use case for HPE Ezmeral Data Fabric?

The main purpose of HPE Ezmeral Data Fabric for me is that it acts as a database. In my company, we store our data wi...

See all answers

Comparisons

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Compared 6% of the time

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Compared 4% of the time

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Compared 24% of the time

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Compared 11% of the time

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Compared 9% of the time

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Compared 7% of the time

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Cloudera Distribution for Hadoop vs HPE Ezmeral Data Fabric

Compared 50% of the time

Cloudera Data Platform vs HPE Ezmeral Data Fabric

Compared 11% of the time

IBM Analytics Engine vs HPE Ezmeral Data Fabric

Compared 8% of the time

Informatica Big Data Parser vs HPE Ezmeral Data Fabric

Compared 7% of the time

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Compared 6% of the time

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Product Reports

Buyer's Guide

Amazon EMR

August 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

August 2025

Download Apache Spark product report

Buyer's Guide

HPE Ezmeral Data Fabric

July 2025

Download HPE Ezmeral Data Fabric product report

Also Known As

Amazon Elastic MapReduce

No data available

MapR, MapR Data Platform

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

Forward-leaning companies win market share because they leverage data more effectively than their competitors. Unlock the potential of your data assets with HPE Ezmeral Data Fabric (formerly MapR Data Platform). Empower your data science, analytics, and business teams by simplifying data management on a globally distributed scale. All with enterprise-grade reliability, security, and performance.

Hewlett Packard Enterprise

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

Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: July 2025.

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