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 8.3, while Apache is ranked #1 with an average rating of 8.6. Amazon Web Services (AWS) holds a 13.8% mindshare in H, compared to Apache’s 17.7% 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

816 Views
672 Comparison Views

86% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,153 Views
860 Comparison Views

90% willing to recommend

HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews

1,060 Views
558 Comparison Views

100% willing to recommend

Amazon EMR

Apache Spark

HPE Ezmeral Data Fabric

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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: June 2025).

Buyer's Guide

Hadoop

June 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:

Mindshare comparison

As of June 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.8%, down from 16.6% compared to the previous year. The mindshare of Apache Spark is 17.7%, down from 21.1% compared to the previous year. The mindshare of HPE Ezmeral Data Fabric is 15.4%, up from 11.8% 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.

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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

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

"The solution is scalable."

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

"The solution helps us manage huge volumes of data."

"I rate Amazon EMR as ten out of ten."

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

"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."

More Amazon EMR pros

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

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

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

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

"The good performance. The nice graphical management console. The long list of ML algorithms."

"We use Spark to process data from different data sources."

"The scalability has been the most valuable aspect of the solution."

More Apache Spark pros

"I like the administration part."

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

"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."

"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."

Cons

"There is room for improvement in pricing."

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

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

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

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

More Amazon EMR cons

"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

"The migration of data between different versions could be improved."

"It should support more programming languages."

"The main concern is the overhead of Java when distributed processing is not necessary."

"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."

"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 solution’s integration with other platforms should be improved."

More Apache Spark cons

"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."

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

"The product is not user-friendly."

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

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

Pricing and Cost Advice

"Amazon EMR is not very expensive."

"Amazon EMR's price is reasonable."

"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."

"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."

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

"The price of the solution is expensive."

More Amazon EMR pricing and cost advice

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

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

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

"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."

"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."

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

"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."

More Apache Spark pricing and cost advice

"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."

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

12%

Educational Organization

10%

Manufacturing Company

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

19%

Computer Software Company

14%

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

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Compared 10% of the time

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Compared 7% of the time

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Compared 3% of the time

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Compared 55% of the time

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Compared 10% of the time

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Compared 8% of the time

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Compared 7% of the time

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Product Reports

Buyer's Guide

Amazon EMR

June 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

June 2025

Download Apache Spark product report

Buyer's Guide

HPE Ezmeral Data Fabric

June 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: June 2025.

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