HPE Ezmeral Data Fabric vs Spark SQL comparison

Hewlett Packard Enterprise and Apache are both solutions in the Hadoop category. Hewlett Packard Enterprise is ranked #4 with an average rating of 8.0, while Apache is ranked #5 with an average rating of 7.0. Hewlett Packard Enterprise holds a 14.2% mindshare in H, compared to Apache’s 9.8% mindshare. Additionally, 100% of Hewlett Packard Enterprise users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews

1,153 Views
969 Comparison Views

100% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

639 Views
562 Comparison Views

85% willing to recommend

HPE Ezmeral Data Fabric

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between HPE Ezmeral 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 Ezmeral Data Fabric vs. Spark SQL Report (Updated: September 2025).

Buyer's Guide

HPE Ezmeral Data Fabric vs. Spark SQL

September 2025

Download the complete report

Helped 867,370 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 Ezmeral 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 September 2025, in the Hadoop category, the mindshare of HPE Ezmeral Data Fabric is 14.2%, up from 13.5% compared to the previous year. The mindshare of Spark SQL is 9.8%, down from 10.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
HPE Ezmeral Data Fabric	14.2%
Spark SQL	9.8%
Other	76.0%

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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SurjitChoudhury

Data engineer at Cocos pt

Offers the flexibility to handle large-scale data processing

My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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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

"I like the administration part."

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

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

"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."

"It is a stable solution."

"I find the Thrift connection valuable."

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

"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."

"Overall the solution is excellent."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

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

"Data validation and ease of use are the most valuable features."

More Spark SQL pros

Cons

"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."

"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."

"The product is not user-friendly."

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

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

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

"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."

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

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

"In the next release, maybe the visualization of some command-line features could be added."

"SparkUI could have more advanced versions of the performance and the queries and all."

"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 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 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 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."

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

Computer Software Company

13%

Comms Service Provider

11%

Government

Financial Services Firm

18%

University

11%

Retailer

10%

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	5
Large Enterprise	4

Questions from the Community

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 Fabric is not compatible with third-party tools. For example, HPE Ezmeral Data Fabri...

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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 with the help of HPE Ezmeral Data Fabric. It is possible to use Spark engine with ...

See all answers

What do you like most about Spark SQL?

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

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

What is your primary use case for Spark SQL?

I employ Spark SQL for various tasks. Initially, I gathered data from databases, SAP systems, and external sources via SFTP, storing it in blob storage. Using Spark SQL within Jupyter notebooks, I ...

See all answers

Comparisons

Cloudera Distribution for Hadoop vs HPE Ezmeral Data Fabric

Compared 52% of the time

Cloudera Data Platform vs HPE Ezmeral Data Fabric

Compared 16% of the time

Apache Spark vs HPE Ezmeral Data Fabric

Compared 13% of the time

Amazon EMR vs HPE Ezmeral Data Fabric

Compared 10% of the time

IBM Analytics Engine vs HPE Ezmeral Data Fabric

Compared 10% of the time

More HPE Ezmeral Data Fabric Competitors

Apache Spark vs Spark SQL

Compared 54% of the time

SAP HANA vs Spark SQL

Compared 20% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 14% of the time

Amazon EMR vs Spark SQL

Compared 13% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

HPE Ezmeral Data Fabric

September 2025

Download HPE Ezmeral Data Fabric product report

Buyer's Guide

Spark SQL

August 2025

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 Ezmeral Data Fabric vs. Spark SQL

September 2025

Free Report: HPE Ezmeral Data Fabric vs. Spark SQL

Find out what your peers are saying about HPE Ezmeral Data Fabric vs. Spark SQL and other solutions. Updated: September 2025.

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