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HPE Data Fabric vs Spark SQL comparison

 

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

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
Spark SQL
Ranking in Hadoop
5th
Average Rating
7.8
Reviews Sentiment
7.6
Number of Reviews
15
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Hadoop category, the mindshare of HPE Data Fabric is 14.9%, up from 14.2% compared to the previous year. The mindshare of Spark SQL is 6.6%, down from 10.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
HPE Data Fabric14.9%
Spark SQL6.6%
Other78.5%
Hadoop
 

Featured Reviews

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.
Kemal Duman - PeerSpot reviewer
Team Lead, Data Engineering at Nesine.com
Data pipelines have run faster and support flexible batch and streaming transformations
We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

Quotes from Members

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

Pros

"The model creation was very interesting, especially with the libraries provided by the platform."
"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."
"It is a stable solution...It is a scalable solution."
"I like the administration part."
"The stability was fine. It behaved as expected."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"It is a stable solution."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"Data validation and ease of use are the most valuable features."
"I find the Thrift connection valuable."
 

Cons

"The product is not user-friendly."
"The deployment could be faster. I want more support for the data lake in the next release."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"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."
"SparkUI could have more advanced versions of the performance and the queries and all."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"I've experienced some incompatibilities when using the Delta Lake format."
"There are many inconsistencies in syntax for the different querying tasks."
"It would be useful if Spark SQL integrated with some data visualization tools."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
 

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."
"The solution is bundled with Palantir Foundry at no extra charge."
"We use the open-source version, so we do not have direct support from Apache."
"There is no license or subscription for this solution."
"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."
"The solution is open-sourced and free."
"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Comms Service Provider
8%
Performing Arts
7%
Computer Software Company
7%
Financial Services Firm
16%
University
16%
Retailer
13%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Large Enterprise7
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise6
Large Enterprise4
 

Also Known As

MapR, MapR Data Platform
No data available
 

Overview

 

Sample Customers

Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Find out what your peers are saying about HPE Data Fabric vs. Spark SQL and other solutions. Updated: December 2025.
880,745 professionals have used our research since 2012.