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Cloudera Distribution for Hadoop vs HPE Data Fabric comparison

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Comparison Buyer's Guide

Executive Summary

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

Cloudera Distribution for H...
Ranking in Hadoop
2nd
Average Rating
8.0
Reviews Sentiment
6.3
Number of Reviews
51
Ranking in other categories
NoSQL Databases (10th)
HPE Data Fabric
Ranking in Hadoop
4th
Average Rating
8.0
Reviews Sentiment
6.1
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2026, in the Hadoop category, the mindshare of Cloudera Distribution for Hadoop is 14.7%, down from 21.8% compared to the previous year. The mindshare of HPE Data Fabric is 9.3%, down from 14.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Cloudera Distribution for Hadoop14.7%
HPE Data Fabric9.3%
Other76.0%
Hadoop
 

Featured Reviews

Rok Dolinsek - PeerSpot reviewer
Manager, Bussines Development & Co Owner at Troia d.o.o.
Enables on-premise implementation with powerful data processing capabilities
This is the only solution that is possible to install on-premise. Cloudera provides a hybrid solution that combines compute on cloud or on-premises. It includes all machine learning algorithms in the Spark machine learning library. All functionalities needed for a big data platform and ETL are on the platform, eliminating the need for other tools. It is scalable, ready for vertical scaling, and very powerful, offering numerous functionalities and configurations for generative AI.
Arnab Chatterjee - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The solution is stable."
"Cloudera Distribution for Hadoop provides numerous features and capabilities combined into one platform, offers power processing, supports different file systems and query engines, and provides parallel processing for handling many requests."
"In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues."
"The data science aspect of the solution is valuable."
"This is the only solution that is possible to install on-premise."
"The main advantage is the storage is less expensive."
"We switched because Cloudera just works."
"With a single script, we are able to run the jobs within minutes, which is an advantage."
"Our customer purchased a paid support service and so far MapR has addressed our issues well."
"This product enabled us opening up endless possibilities in data analytics, IOE/IOT, and predictive analysis."
"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."
"Outside of human error, MapR is probably the most stable of the major releases."
"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."
"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."
"I highly recommend MapR."
"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."
 

Cons

"There are multiple bugs when we update."
"The performance of some analytics engines provided by Cloudera is not that good."
"The competitors provide better functionalities."
"Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions."
"The security of this solution could be improved."
"It is quite complicated to configure and install."
"The dashboard could be improved."
"Cloudera CDH5.5.x does not support SparkR."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."
"The biggest drawback is that it has vendor locking."
"Installations and setups are still a bit cryptic and can be improved."
"The deployment could be faster. I want more support for the data lake in the next release."
"It'd like to see file system auditing, data encryption, and certification of other vendors' 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."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
 

Pricing and Cost Advice

"The price could be better for the product."
"Cloudera requires a license to use."
"The tool is not expensive."
"The pricing must be improved."
"I haven't bought a license for this solution. I'm only using the Apache license version."
"The tool is expensive...For the SMB market or customers whose environments are not that complex and do not have multiple systems running, Cloudera might not be a good option."
"When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive."
"The solution is expensive."
"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."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Outsourcing Company
11%
Construction Company
9%
Manufacturing Company
8%
Construction Company
17%
Financial Services Firm
16%
Healthcare Company
10%
Comms Service Provider
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business16
Midsize Enterprise9
Large Enterprise32
By reviewers
Company SizeCount
Small Business4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Cloudera Distribution for Hadoop?
The price for Cloudera is average, yet it is very good compared to other solutions. It can be deployed on-premises, unlike competitors' cloud-only solutions.
What needs improvement with Cloudera Distribution for Hadoop?
If they could support modifying the data more easily than the current implementation, it would be beneficial.
What is your primary use case for Cloudera Distribution for Hadoop?
We use Cloudera Distribution for Hadoop for many use cases including analytics, storing huge data sets, and various data processing tasks.
Ask a question
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Also Known As

No data available
MapR, MapR Data Platform
 

Overview

 

Sample Customers

37signals, Adconion,adgooroo, Aggregate Knowledge, AMD, Apollo Group, Blackberry, Box, BT, CSC
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 Cloudera Distribution for Hadoop vs. HPE Data Fabric and other solutions. Updated: September 2026.
917,403 professionals have used our research since 2012.