HPE Data Fabric vs Spark SQL comparison

Hewlett Packard Enterprise and Apache are both solutions in the Hadoop category. Hewlett Packard Enterprise is ranked #4, while Apache is ranked #5 with an average rating of 8.0. Hewlett Packard Enterprise holds a 14.3% mindshare in H, compared to Apache’s 6.1% mindshare. Additionally, 100% of Hewlett Packard Enterprise users are willing to recommend the solution, compared to 86% of Apache users who would recommend it.

HPE Data Fabric

Read 12 HPE Data Fabric reviews

1,233 Views
986 Comparison Views

100% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

674 Views
600 Comparison Views

86% willing to recommend

HPE Data Fabric

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between HPE Data Fabric and Spark SQL 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 HPE Data Fabric vs. Spark SQL Report (Updated: February 2026).

Buyer's Guide

HPE Data Fabric vs. Spark SQL

February 2026

Download the complete report

Helped 881,515 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

HPE Data Fabric

Ranking in Hadoop

4th

Average Rating

8.0

Reviews Sentiment

6.1

Number of Reviews

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

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Hadoop category, the mindshare of HPE Data Fabric is 14.3%, down from 14.6% compared to the previous year. The mindshare of Spark SQL is 6.1%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
HPE Data Fabric	14.3%
Spark SQL	6.1%
Other	79.6%

Hadoop

Featured Reviews

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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Kemal Duman

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.

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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

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

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

"I like the administration part."

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

"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."

"This solution is useful to leverage within a distributed ecosystem."

"The performance is one of the most important features. It has an API to process the data in a functional manner."

"The stability was fine. It behaved as expected."

"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

"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."

"I find the Thrift connection valuable."

More Spark SQL pros

Cons

"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."

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

"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."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

"It would be useful if Spark SQL integrated with some data visualization tools."

"There should be better integration with other solutions."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"Anything to improve the GUI would be helpful."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

More Spark SQL cons

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."

"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."

"The solution is bundled with Palantir Foundry at no extra charge."

"There is no license or subscription for this solution."

"We use the open-source version, so we do not have direct support from Apache."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Comms Service Provider

Performing Arts

Computer Software Company

Financial Services Firm

15%

University

15%

Retailer

12%

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	6
Large Enterprise	4

Comparisons

Cloudera Distribution for Hadoop vs HPE Data Fabric

Compared 21% of the time

Cloudera Data Platform vs HPE Data Fabric

Compared 16% of the time

Apache Spark vs HPE Data Fabric

Compared 11% of the time

Amazon EMR vs HPE Data Fabric

Compared 9% of the time

Zettaset Data Platform vs HPE Data Fabric

Compared 5% of the time

More HPE Data Fabric Competitors

Apache Spark vs Spark SQL

Compared 30% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 15% of the time

SAP HANA vs Spark SQL

Compared 14% of the time

Amazon EMR vs Spark SQL

Compared 13% of the time

IBM Netezza Performance Server vs Spark SQL

Compared 12% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

HPE Data Fabric

January 2026

Download HPE Data Fabric product report

Buyer's Guide

Spark SQL

January 2026

Download Spark SQL product report

Also Known As

MapR, MapR Data Platform

No data available

Overview

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

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

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

Buyer's Guide

HPE Data Fabric vs. Spark SQL

February 2026

Free Report: HPE Data Fabric vs. Spark SQL

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