Try our new research platform with insights from 80,000+ expert users

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
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the Hadoop category, the mindshare of HPE Data Fabric is 14.4%, up from 13.4% compared to the previous year. The mindshare of Spark SQL is 9.4%, down from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
HPE Ezmeral Data Fabric14.4%
Spark SQL9.4%
Other76.2%
Hadoop
 

Featured Reviews

Arnab Chatterjee - PeerSpot reviewer
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.
SurjitChoudhury - PeerSpot reviewer
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.

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."
"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."
"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."
"Overall the solution is excellent."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"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."
"It is a stable solution."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"Data validation and ease of use are the most valuable features."
"The speed of getting data."
"This solution is useful to leverage within a distributed ecosystem."
 

Cons

"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."
"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 product is not user-friendly."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
"Anything to improve the GUI would be helpful."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"There should be better integration with other solutions."
"I've experienced some incompatibilities when using the Delta Lake format."
"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."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
 

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."
"We use the open-source version, so we do not have direct support from Apache."
"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."
"The solution is open-sourced and free."
"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."
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
872,655 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
13%
Comms Service Provider
12%
Government
6%
Financial Services Firm
16%
University
14%
Retailer
11%
Manufacturing Company
10%
 

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 Enterprise5
Large Enterprise4
 

Questions from the Community

Ask a question
Earn 20 points
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.
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 ...
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 ...
 

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: September 2025.
872,655 professionals have used our research since 2012.