Amazon EMR vs HPE Data Fabric comparison

Why PeerSpot?

Amazon Web Services (AWS) and Hewlett Packard Enterprise are both solutions in the Hadoop category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while Hewlett Packard Enterprise is ranked #5. Amazon Web Services (AWS) holds a 9.8% mindshare in H, compared to Hewlett Packard Enterprise’s 9.2% mindshare. Additionally, 83% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Hewlett Packard Enterprise users who would recommend it.

Amazon EMR

Read 25 Amazon EMR reviews

4,458 Views
1,962 Comparison Views

83% willing to recommend

HPE Data Fabric

Read 12 HPE Data Fabric reviews

2,863 Views
2,376 Comparison Views

100% willing to recommend

Amazon EMR

HPE Data Fabric

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon EMR and HPE Data Fabric 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. HPE Data Fabric Report (Updated: November 2026).

Buyer's Guide

Amazon EMR vs. HPE Data Fabric

November 2026

Download the complete report

Helped 920,769 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:

Categories and Ranking

Amazon EMR

Ranking in Hadoop

3rd

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (13th)

HPE Data Fabric

Ranking in Hadoop

5th

Average Rating

8.0

Reviews Sentiment

6.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Hadoop category, the mindshare of Amazon EMR is 9.8%, down from 13.2% compared to the previous year. The mindshare of HPE Data Fabric is 9.2%, down from 14.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Amazon EMR	9.8%
HPE Data Fabric	9.2%
Other	81.0%

Hadoop

Featured Reviews

reviewer2043696

Senior Technical Engineer at a transportation company with 5,001-10,000 employees

Data pipelines have simplified complex transformations and provide faster insights for users

The features at Amazon EMR that I have found most valuable are fully customizable functions. I am using Amazon EMR for data transformation, where we load the data. The entire ETL process is encompassed within it. The first thing is the egress part, where we pull the data from various sources, which include our main core databases, the systems that include Oracle, MySQL, SQL Server, and Postgres. We bring data from all these sources and use Amazon EMR specifically to combine all this data, clean the data, and combine it at a particular level. We bring everything to a particular level to make it more production-ready, and we simplify the data because every system has its own problems. We are using it to clean the data and transform the data in such a way that the end-user can get the insights faster.

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Arnab Chatterjee

Regional Head of Data and Application Platform at a financial services firm with 10,001+ employees

It's flexible and easily accessible across multiple locations, but the upgrade process is complicated

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. They're transforming their host stack to increase cloud readiness and edge compute capability. HPE is transitioning from a standard data-driven approach to one powered by AI analytics. That's something they have released very recently. I haven't tried that, but it will probably make things easier. The ability to adapt Ezmeral to the public cloud is probably missing. I've heard that they're getting leaner. However, it doesn't have a clear managed services offering for you if you want to deploy this stack on the cloud. That's a problem. This probably won't meet your needs if you require consistency across on-prem and the cloud. It's not Ezmeral's fault. None of the products would fit the bill. Cloud offerings are biased towards their own implementation. It's a general issue on most big data platforms. They're already working towards that, but it hasn't been released.

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

"Amazon makes it about as easy as it can be to dip a toe in those waters."

"The stability of the product has been great overall."

"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 solution helps us manage huge volumes of data."

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

"Amazon EMR provides out-of-the-box functionality because we can deploy and get Spark functionality over Hadoop."

"This tool is simple to use and it's really accessible to many dev teams."

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

More Amazon EMR pros

"My first choice is MapR, as it is more adaptable to different contexts, and it could be customized in some way to fit the different needs, and this is my first choice and my first advice to people who ask me about this particular platform."

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

"Our customer purchased a paid support service and so far MapR has addressed our issues well."

"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."

"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."

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

"The fact that the heavy computation is required on Big Data can be distributed across many nodes in a cluster, makes this solution a winner."

"I highly recommend MapR."

More HPE Data Fabric pros

Cons

"Better monitoring, debugging, and stability are all needed."

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

"Whenever we have scaling policies for load balancing and our load increases the input rate, we increase the resources. However, at that time Amazon AWS is not providing the scale of the required resources in a short time."

"One of the lacking features is good web support."

"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."

"Modules and strategies should be better handled and notified early in advance."

"The web interface for managing all your cloud services is a bit patchy and needs improvement."

"The cost of Amazon EMR is very high."

More Amazon EMR cons

"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."

"The biggest drawback is that it has vendor locking."

"One weakness for MapR is the Kerberos support."

"Installations and setups are still a bit cryptic and can be improved."

"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."

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

"It'd like to see file system auditing, data encryption, and certification of other vendors' tools."

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

More HPE Data Fabric cons

Pricing and Cost Advice

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

"Amazon EMR's price is reasonable."

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

"Amazon EMR is not very 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 a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."

"The cost of Amazon EMR is very high."

More Amazon EMR pricing and cost advice

"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."

"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

15%

Manufacturing Company

10%

Construction Company

Healthcare Company

Construction Company

17%

Financial Services Firm

15%

Comms Service Provider

10%

Healthcare Company

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	5
Large Enterprise	12

By reviewers
Company Size	Count
Small Business	4
Large Enterprise	7

Questions from the Community

What is your experience regarding pricing and costs for Amazon EMR?

I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.

See all answers

What needs improvement with Amazon EMR?

I feel some lack of functionality in Amazon EMR. I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.

See all answers

What advice do you have for others considering Amazon EMR?

I find it easy to integrate Amazon EMR with other AWS services like S3 or EC2 for data processing needs. I would rate this review as eight out of ten.

See all answers

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Compared 7% of the time

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Compared 6% of the time

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Compared 6% of the time

IBM Analytics Engine vs Amazon EMR

Compared 5% of the time

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Compared 17% of the time

IBM Spectrum Computing vs HPE Data Fabric

Compared 11% of the time

IBM Analytics Engine vs HPE Data Fabric

Compared 11% of the time

Zettaset Data Platform vs HPE Data Fabric

Compared 9% of the time

Cloudera Data Platform vs HPE Data Fabric

Compared 9% of the time

More HPE Data Fabric Competitors

Product Reports

Buyer's Guide

Amazon EMR

October 2026

Download Amazon EMR product report

Buyer's Guide

HPE Data Fabric

October 2026

Download HPE Data Fabric product report

Also Known As

Amazon Elastic MapReduce

MapR, MapR Data Platform

Overview

Amazon EMR simplifies big data processing by offering integration with popular tools. It's scalable and cost-efficient, enabling fast processing while managing infrastructure effortlessly. It's designed for users aiming to streamline data workflows and leverage its batch processing capabilities effectively.

Amazon EMR is a managed service that provides robust features for big data processing. It integrates seamlessly with S3, EC2, Hive, and Spark to facilitate sophisticated data transformation tasks and infrastructure management. It allows organizations to run data lakes, Spark, and Hadoop clusters effortlessly, offering flexibility with on-demand execution and extensive scalability. The platform is valued for its strong processing speed and comprehensive security features, making it ideal for complex data engineering projects. It supports both batch processing and real-time workflows, designed to eliminate hardware management while maintaining cost efficiency and stability.

What are the key features of Amazon EMR?

Cluster Management: Offers intuitive control and configuration of clusters
Integration: Seamlessly integrates with S3, EC2, Spark, and more
Scalability: Provides flexible scaling to meet data demands
Batch Processing: Allows efficient handling of large data sets
Cost Efficiency: Minimizes costs with managed service offerings

What benefits and ROI should be considered?

Processing Speed: Fast performance for data processing tasks
Security: Built-in features ensure data protection
Infrastructure Simplification: Eliminates hardware management needs
Flexibility: Adapts to changing data loads with ease
Affordability: Offers economic processing power

Amazon EMR is implemented by industries such as healthcare and tech processing for complex data tasks like building data lakes or financial data processing. It supports AI-driven analytics and data engineering projects, integrating with SageMaker for predictions and maintaining workflows in public health applications, allowing professionals in different fields to manage data pipelines, resource utilization, and job execution efficiently.

Amazon Web Services (AWS)

HPE Data Fabric delivers robust data management with features like multi-tenancy, security, and ease of configuration. It supports high performance and unified analytics, making it a reliable choice for organizations looking to manage extensive data efficiently.

HPE Data Fabric provides a comprehensive data management platform with clustered node distribution and no single point of failure, ensuring high availability. Its compatibility with MapR-DB and NFS functionality allows integration with existing systems. Although there are challenges with third-party tool compatibility and upgrades, it supports big data initiatives by acting as both a database and messaging layer. Users benefit from bundled ecosystem support and simplified administration, enhancing usability across multiple teams and locations.

What features make HPE Data Fabric valuable?

Multi-tenancy: Supports multiple users or tenants securely.
Clustered Node Distribution: Distributes data across nodes for reliability.
POSIX Compliance: Compatibility with POSIX standards for ease of integration.
NFS Functionality: Facilitates network-based file sharing.
Unified Analytics: Provides integrated data analysis capabilities.

Why consider HPE Data Fabric in reviews?

Reliability: No single point of failure, ensuring consistent performance.
Cost Efficiency: Offers cost-effective data management solutions.
High Availability: Provides resilient storage and service availability.
Ease of Configuration: Streamlines administrative tasks with UI-driven tools.

Organizations in sectors such as finance, healthcare, and logistics use HPE Data Fabric to manage large volumes of data efficiently. Its role in supporting distributed processing and acting as a NoSQL storage solution enables these industries to leverage big data for enhanced operational insights and decision-making capabilities. The inclusion of AI tools further expands its utility, facilitating advanced data environments that are cost-effective and scalable for growing organizational demands.

Hewlett Packard Enterprise

Sample Customers

Yelp

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

Buyer's Guide

Amazon EMR vs. HPE Data Fabric

November 2026

Free Report: Amazon EMR vs. HPE Data Fabric

Find out what your peers are saying about Amazon EMR vs. HPE Data Fabric and other solutions. Updated: November 2026.

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